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In this paper, the aging characterization of a kind of insulating paper modified by magnetron sputtering MgO particles based on a microstrip resonant sensor was presented. Firstly, the modified insulating paper with 0, 15 and 30 min MgO particle sputtering times was prepared by a magnetron sputtering device. After that, the properties of the modified insulating paper with different sputtering times were analyzed through microscopic characterization, infrared spectrum, polymerization degree, dielectric constant, AC breakdown strength and thermal aging experiments. The results show that the dielectric constant of the modified insulating paper decreased obviously, the AC breakdown strength increased and the thermal aging resistance was better after 15 min of sputtering. The overall performance of the modified insulating paper after 30 min of sputtering is reduced due to excessive sputtering. In addition, microstrip resonant sensors are introduced to characterize the thermal aging degree of the modified insulating paper, and two microstrip resonant sensors are prepared: a complementary split ring resonator (CSRR) and an interdigital-capacitor-shaped defected ground structure resonator (IDCS-DGS). The resonance frequency deviation of the modified insulating paper samples after aging was measured by microstrip resonance sensors to show the influence of aging temperature on aging degree. The experimental results show that the test results of the microstrip resonance sensors are in good agreement with the traditional characterization methods and can characterize the various aging stages of the modified insulating paper to a certain extent, which proves the feasibility of the characterization method. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is under increasing pressure to reduce its energy consumption and carbon footprint. This study examines the feasibility of integrating high-temperature heat pumps (HTHP) into tissue paper production to enhance energy efficiency and decarbonization. Focusing on the energy-intensive drying process, the study uses data from a typical tissue paper mill to simulate and optimize an HTHP system producing four tons per hour of nine-bar saturated steam. It also addresses necessary modifications for HTHP integration applicable across the sector. Various refrigerants were analyzed, achieving a maximum coefficient of performance (COP) of 2.01. Results showed that HTHP can reduce energy consumption and emissions by up to 17% and 40%, respectively, based on the European electricity mix. Although steam production costs increase by 55% compared to fossil fuel-based systems, HTHP is more cost-effective than direct electric resistance heating, which raises costs by 196%. With a CO2 price of EUR 100/t, HTHP offers a 12% cost reduction. However, without public funding, capital expenditures may be unsustainable in many regions, though viable in countries with favorable gas and electricity price differentials. The paper underscores the need for advancements in HTHP technology and cost reductions, emphasizing industry adaptation for seamless HTHP integration. [ABSTRACT FROM AUTHOR]

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

The subject of this study is the optimization of the paper production process in one of Poland's leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (Vs) and winding speed (Vn) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes. [ABSTRACT FROM AUTHOR]

To eliminate the problem of the aging of cellulose insulation in the manufacturing stage, a new drying method is being developed based on the use of methanol vapors. Previous studies have shown that the complete removal of methanol from the cellulose insulation after the drying process is very difficult. Therefore, it is necessary to check how the remaining methanol after drying affects the properties of both the cellulose materials and mineral oil. To conduct such studies, it is necessary to know the methanol content in oil that can be expected depending on its initial content in the cellulose materials and the temperature of the insulation system. Therefore, the main goal of this work is to develop methanol equilibrium curves for oil–paper insulation. To achieve the assumed goal, three-stage studies were conducted. A gas chromatograph equipped with a flame ionization detector was used in all stages of these studies. The gas partition coefficient between oil and air was determined for a temperature of 70 °C. The key experimental finding was the development of methanol equilibrium curves for oil–paper insulation. Thanks to this achievement, it is possible to estimate the methanol content in cellulose materials and mineral oil depending on the insulation temperature. Such data are necessary, among others, to plan appropriate studies aimed at assessing the impact of methanol content on the dielectric and physicochemical properties of these materials, important from the point of view of the operation of power transformers. [ABSTRACT FROM AUTHOR]

In order to reveal the migration and evolution of gas molecules in the actual oil–paper insulation composite system of transformer from the molecular level, the diffusion behavior of seven gas molecules (H2, CO, CO2, CH4, C2H2, C2H4, C2H6) generated during the operation and aging of oil-immersed transformers in the oil–paper composite insulation system is studied by molecular dynamics. Firstly, based on the molecular dynamics software, the model of the oil–paper composite insulation system and the gas molecule model is constructed. In order to compare and analyze the diffusion properties of gas molecules in a single medium, a single model of insulating oil and cellulose is also constructed. Then, the diffusion coefficients of gas molecules in the insulating oil, cellulose, and oil–paper insulating composite system are simulated and calculated. And the differences in the diffusion properties of gas molecules in the three insulating mediums are discussed. Finally, the microscopic mechanism of diffusion of different gas molecules in the three mediums is analyzed. The simulation results show that among the three mediums, the diffusion coefficient of H2 is the largest, while the diffusion coefficients of the other gas molecules are not very different. The diffusion coefficients of the seven gas molecules are the smallest in the oil-immersed paper composite insulation system, followed by cellulose, while the diffusion coefficients are the largest in mineral oil. It indicates that the diffusion of gas molecules is inhibited in oil–paper insulation systems where the insulating paper is completely immersed in oil. This is mainly due to the fact that the insulating oil completely penetrates into the paper, filling the pores and voids between the fibers, resulting in a reduction in the transition vacancies of the intermediate gas molecules, which hinders the diffusion of the gas molecules. [ABSTRACT FROM AUTHOR]

The connection between surface tension and viscosity has been the subject of several pieces of research on nanofluids. Researchers have discovered differing relationships between these two suspension qualities in the literature. Surface tension and viscosity have been found to be correlated in certain research works but not in other. The behavior of these fluids may be influenced by several factors, including temperature, the presence of surfactants, and the functional groups on carbon nanotubes (CNTs). This study investigates the relationship between surface tension and viscosity in CNT-Nanofluids by reviewing earlier research on the impact of CNT addition on water's intermolecular interactions. The findings show that depending on different aspects of the nanofluids, the connection is complicated and uncertain. The study shows that although temperature and the addition of carbon nanotubes affect both surface tension and viscosity, other studies only consider how these factors affect one of these qualities. We conclude that under certain heat transfer circumstances, there is no clear-cut relationship between surface tension and viscosity in CNT–water fluids. [ABSTRACT FROM AUTHOR]

Natural and synthetic esters are increasingly being chosen instead of mineral oil for environmental and fire safety reasons. However, their use in power transformers is limited due to insufficiently well-understood ageing processes affecting their properties and the properties of cellulosic materials impregnated with them. The research results presented in many scientific papers prove that the use of esters slows the ageing process of cellulosic materials. This article presents the results of research aimed at answering the question of whether the effect of slowing the ageing process will also occur in the case of insulation with very low initial moisture. The answer to this question will allow us to better understand the role of water in the ageing process of the transformer's insulation system. The thermal ageing process was carried out at a temperature of 150 °C in closed systems. The degree of cellulose polymerisation was taken as a measure of the degree of paper ageing. Great attention was paid to measuring the water content in both paper and electro-insulating liquids at various stages of their ageing. Furthermore, measurements of the properties of electro-insulating liquids were made, which are considered markers of ageing. The test results obtained indicate that in the case of a dry insulation system, corresponding to the initial moisture of the new units, the type of liquid used to impregnate the cellulosic material does not significantly affect its depolymerisation process. However, in the case of paper impregnated with natural esters, a lower dynamic of moisture increase in insulation was observed. [ABSTRACT FROM AUTHOR]

Hydrogen production through electrolysis using renewable resources is highly promising for reducing greenhouse gas emissions. While significant efforts have focused on developing more efficient and cost-effective catalysts to lower hydrogen production costs, catalysts are not the primary expense in electrolyzer fabrication. In the case of Proton Exchange Membrane Water Electrolyzers (PEMWEs), other components—such as the proton membrane, gas diffusion layer (GDL), and bipolar plates—contribute more to overall costs. To explore this, a study was conducted on the performance of PEMWEs with various carbon paper GDLs, developed in the lab, on the cathodic side. This study examined how properties like electrical conductivity, porosity, and gas permeability affect performance. These findings emphasize the need to optimize components beyond catalysts to improve the cost-effectiveness of hydrogen production through electrolysis. [ABSTRACT FROM AUTHOR]

The momentum of the Fourth Industrial Revolution is driving increased demand for certain specific metals. These include copper, silver, gold, and platinum group metals (PGMs), which have important applications in renewable energies, green hydrogen, and electronic products. However, the continuous extraction of these metals is leading to a rapid decline in their ore grades and, consequently, increasing the environmental impact of extraction. Hence, obtaining metals from secondary sources, such as waste electrical and electronic equipment (WEEE), has become imperative for both environmental sustainability and ensuring their availability. To evaluate the sustainability of the process, this paper proposes using an exergy approach, which enables appropriate allocation among co-products, as well as the assessment of exergy losses and the use of non-renewable resources. As a case study, this paper analyzes the recycling process of waste printed circuit boards (PCBs) by disaggregating the exergy cost into renewable and non-renewable sources, employing different exergy-based cost allocation methods for the mentioned metals. It further considers the complete life cycle of metals using the Circular Thermoeconomics methodology. The results show that, when considering the entire life cycle, between 47% and 53% of the non-renewable exergy is destroyed during recycling. Therefore, delaying recycling as much as possible would be the most desirable option for minimizing the use of non-renewable resources. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

Mass-impregnated (MI) cables have been used for many years as cables in high-voltage direct current (HVDC) systems. In line commutated converter (LCC) HVDC systems, polarity reversal for power flow control can induce significant electrical stress on MI cables. Furthermore, the mass oil and kraft paper comprising the impregnated insulation have significantly different coefficients of thermal expansion. Load fluctuations in the cable lead to expansion and contraction of the mass, creating pressure within the insulation and causing redistribution of the impregnant. During this process, shrinkage cavities can form within the butt gaps. Since the dielectric strength of the cavities is lower than that of the surrounding impregnation, cavitation phenomena in impregnated paper insulation are considered a factor in degrading insulation performance. Consequently, this study analyzes the electrical conductivity of thermally aged materials and investigates the transient electric field characteristics within the cable. Additionally, it closely analyzes the formation and dissolution of cavities in MI cables during polarity reversal based on a numerical model of pressure behavior in porous media. The conductivity of the impregnated paper indicates that it has excellent resistance to thermal degradation. Simulation results for various load conditions highlight that the interval of load-off time and the magnitude of internal pressure significantly influence the cavitation phenomenon. Lastly, the study proposes stable system operation methods to prevent cavitation in MI cables. [ABSTRACT FROM AUTHOR]

This paper focuses on proton exchange membrane fuel cell (PEMFC) operation at current densities in the order of 6 A/cm2. Such high current densities are conceivable when the traditional carbon fiber papers are replaced with perforated metal plates as the gas diffusion layer to enhance waste heat removal, and at the same time the relative humidity inside the fuel cell is kept below 100% by applying appropriate operating conditions as was found in previous one-dimensional modeling work. In the current paper, we applied a three-dimensional, multi-phase computational fluid dynamics model based on Ansys-CFX to obtain additional insight into the underlying physics. The calculated pressure drops are in very good agreement with previous one-dimensional modeling work, and the current densities in all case studies are in the order of 5–6 A/cm2, but different from the previous one-dimensional study, the results suggest that the relative humidity is very close to 100% throughout the entire channel length when the inlet relative humidity is 100%, ensuring best hydration cell conditions and hence best performance. Importantly, the model results suggest that fuel cell performance at a high current density in conjunction with relatively low stoichiometric flow ratios around 1.5–2 is possible. [ABSTRACT FROM AUTHOR]

The increasing global water pollution leads to the need for urgent development of rapid and accurate water quality monitoring methods. Microbial fuel cells (MFCs) have emerged as real-time biosensors for biochemical oxygen demand (BOD), but they grapple with several challenges, including issues related to reproducibility, operational stability, and cost-effectiveness. These challenges are substantially shaped by the selection of an appropriate air-breathing cathode. Previous studies indicated a critical influence of the cathode on both the enduring electrochemical performance of MFCs and the taxonomic diversity at the electroactive anode. However, the effect of different gas diffusion electrodes (GDE) on 3D-printed single-chamber MFCs for BOD biosensing application and its effect on the bioelectroactive anode was not investigated before. Our study focuses on comparing GDE cathode materials to enhance MFC performance for precise and rapid BOD analysis in wastewater. We examined for over 120 days two Pt-coated air-breathing cathodes with distinct carbonaceous gas diffusion layers (GDLs) and catalyst layers (CLs): cost-effective carbon paper (CP) with hand-coated CL and more expensive woven carbon cloth (CC) with CL pre-applied by the supplier. The results show significant differences in electrochemical characteristics and anodic biofilm composition between MFCs with CP and CC GDE cathodes. CP-MFCs exhibited lower sensitivity (16.6 C L mg−1 m−2) and a narrower dynamic range (25 to 600 mg L−1), attributed to biofouling-related degradation of the GDE. In contrast, CC-MFCs demonstrated superior performance with higher sensitivity (37.6 C L mg−1 m−2) and a broader dynamic range (25 to 800 mg L−1). In conclusion, our study underscores the pivotal role of cathode selection in 3D-printed MFC biosensors, influencing anodic biofilm enrichment time and overall BOD assessment performance. We recommend the use of cost-effective CP GDL with hand-coated CL for short-term MFC biosensor applications, while advocating for CC GDL supplied with CL as the preferred choice for long-term sensing implementations with enduring reliability. [ABSTRACT FROM AUTHOR]

Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers. [ABSTRACT FROM AUTHOR]

The defossilisation of the agricultural sector is driven by intense worldwide academic research on non-fossil, renewable and energy-efficient agriculture, and the acknowledgment of the need for sustainable farming practices. For this purpose, not only technical transformations but also socio-technical system changes towards sustainability need to take place in a co-evolutionary manner. This paper investigates structural and qualitative characteristics of the knowledge produced by research on fossil-energy-free agriculture. We provide evidence on the worldwide research directions, as well as investigate whether academic research and publicly funded research projects foster knowledge creation for the desired transformation. Bibliographic maps are constructed using a query-based methodology as social networks to investigate the efficiency of the EU-funded research to achieve the goals set for the 2050 EU Green Deal. The H2020-funded papers are further analysed with dictionary-based text analysis to quantify the relative emphasis of different types of knowledge in the text. This approach is eventually used to relate transformational capacity to project profiles in the European Union, to evaluate past funding schemes and to improve the shape of future research programs on renewable and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]

Heat pumps have the potential for several applications across various industrial sectors, showcasing significant promise, especially in sectors such as pulp and paper, food and beverage, chemical, non-metallic minerals, and machinery. Envisioning the near future, there is confidence that heat pumps can achieve temperatures above 200 °C, offering substantial potential for utilization in these sectors. Nevertheless, a crucial aspect for the advancement of high-temperature heat pumps is the selection of the fluid. Fluid selection involves considerations of both thermodynamic efficiency and environmental impact, requiring fluids with zero ODP, negligible GWP, and no PFAS. Moreover, it is essential to consider the risks to human health associated with a specific fluid. Despite extensive research, particularly in the realm of vapour compression heat pumps, choosing the most suitable working fluid for these applications is a complex undertaking. Therefore, this paper conducts a theoretical analysis to evaluate potential fluids with suitable thermodynamic properties for high-temperature heat pumps (HTHPs). The comparative results gleaned from this study provide valuable insights for the comprehensive analysis of fluids, showing promise within temperature ranges dictated by specific applications. The metrics employed in the comparison emphasise the merits of fluids in terms of the overall performance, dimensions, and operating ranges of applicable compressor, heat exchange capacity, transport properties, and safety. One noteworthy finding from the analysis is that maintaining a constant HTHP lift (at 40 K) results in having the highest COP across all fluids when the condensing temperature ranges between 85% and 90% of their respective critical temperatures. According to the results of the analysis, natural fluids, including water and alcohols like ethanol or methanol, emerge as particularly compelling candidates. [ABSTRACT FROM AUTHOR]

This article is a summary of many years of work by the author, in which the thermal properties of various types of insulating liquids, used in power transformers, were evaluated. Recently, esters have been displacing mineral oil. There is a common view that mineral oil has better thermal properties than esters. This claim is supported by comparative results of tests of both materials as a liquid only filling the remaining volume of the transformer. The effect of the type of liquid on the thermal properties of the paper–oil insulation has not been analyzed so far. On this basis, the conclusions formulated may be incomplete. For this reason, the author has analyzed the influence of the type of liquid on both the thermal properties of the liquid filling the remaining volume of the transformer and the paper–oil insulation. It was proved that the more effective liquid filling the remaining volume of the transformer was indeed mineral oil. On the other hand, a more effective electrical insulating liquid, which is an element of paper–oil insulation, is a natural ester. A comprehensive assessment that takes into account both the paper–oil insulation and the remaining transformer volume showed that the natural ester proved to be a slightly more effective electrical insulating liquid than the other analyzed liquids. [ABSTRACT FROM AUTHOR]

As a core component of clean energy technology, proton exchange membrane fuel cells (PEMFC) play a crucial role in promoting the evolution of energy structures and realizing sustainable development, representing an environmentally friendly energy conversion strategy. This paper identifies the key core technology themes in the field of the proton exchange membrane fuel cells by constructing patent and paper datasets in the field, applying the BERTopic model for theme identification, and calculating the key core technology scores of each theme using the importance, innovativeness, and high competitiveness barriers to identify the key core technology themes in the field, so as to provide guidance and references for the relevant research and practice. The results of the study show that patent documents and academic papers show obvious differentiation in technical themes: the key core technologies identified in patent texts include 'battery separator materials', 'rubber sealing materials', and 'porous carbon fibre materials'. The key core technologies identified in the academic paper of the thesis include 'palladium-based electrocatalys', 'graphene oxide composite film', and 'platinum-graphene oxide catalyst'. [ABSTRACT FROM AUTHOR]

Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

The influence of carrier mobility on the space charge transport behavior inside the oil-impregnated pressboard insulation of converter transformers cannot be neglected. However, at present, current knowledge is usually derived from empirical or theoretical values, lacks experimental studies, and often ignores the effects of temperature and field strength under actual operating conditions. In this paper, based on the variable-temperature surface potential decay (SPD) method, a carrier mobility measurement platform for oil-impregnated pressboard is established, and the carrier mobility values for different combinations of oil and oil-impregnated pressboard are obtained experimentally to analyze and reveal the influence mechanisms of temperature and field strength on the carrier mobility. The results indicate the following: (1) The positive and negative carrier mobilities of oil-impregnated pressboard are in the range of 10−12–10−13 m2·V−1·s−1, and the negative carrier mobility is always higher than the positive carrier mobility. (2) The carrier mobility is positively correlated with the changes of temperature and field strength, and when the temperature increases from 20 °C to 80 °C, the positive and negative carrier mobilities increase by 4.01 times and 4.72 times, respectively; when the field strength increases from 1 kV/mm to 7 kV/mm, the positive and negative carrier mobility increases by 2.53 and 2.72 times, respectively. (3) The carrier mobility of the pressboard with a higher oil absorption rate changes more significantly with temperature; when the field strength is 7 kV/mm and the temperature increases from 20 °C to 80 °C, the positive polarity carrier mobility increases from 3.96 × 10−13 m2·V−1·s−1 to 2.64 × 10−11 m2·V−1·s−1, an increase of 66.67 times, while the increase in the carrier mobility of the pressboard with a lower oil absorption rate is only 1.59 times. (4) The carrier mobility of the naphthenic transformer oil-impregnated pressboard is higher than that of the paraffin-based transformer oil-impregnated pressboard, and the carrier mobility of two kinds of naphthenic transformer oil-impregnated pressboard is 3.16 times and 2.47 times higher than that of the paraffin-based transformer oil-impregnated pressboard, respectively, under the conditions of 60 °C and 7 kV/mm. (5) Utilizing the Darcy model and microscopic scanning results of the pressboard morphology, it was revealed that permeability and fiber structure are key factors influencing the variation in carrier mobility. The research results of this paper can provide theoretical basis for the calibration and optimization of the oil-pressboard insulation structure of converter transformers. [ABSTRACT FROM AUTHOR]

Confronted with the significant challenges of global climate change and environmental deterioration, the pursuit of carbon emission peaks and the realization of carbon neutrality have become a collective goal for countries worldwide. As an exemplary combustion technology noted for its efficiency and environmental friendliness, the circulating fluidized bed (CFB) is instrumental in curbing the release of carbon dioxide alongside other deleterious gases. The technology is pivotal in promoting the clean and efficient use of coal, simultaneously expediting the global shift towards a sustainable, green, and low-carbon future. This study employs a bibliometric analysis, a social network analysis, and information visualization techniques to delve into the evolution of CFB technology, leveraging the Web of Science database (SCI-EXPANDED and CPCI-S) and the Derwent Innovations Index (DII). Through a meticulous examination of academic papers and patent literature related to CFB technology, this research unveils the developmental trajectory and trends of CFB technology, providing a scientific foundation and reference for strategic technology planning and focused research in key areas. The findings indicate that, while there is a downward trend in the global publication of academic papers on CFB technology, the number of patent applications continues to grow steadily. CFB technology has achieved significant advancements in enhancing combustion efficiency, environmental protection, energy utilization, and waste management, and is progressing towards a direction of diversification and greater efficiency. Moving forward, the development of CFB technology should concentrate on pivotal areas such as material science, fluid dynamics simulation, environmental impact assessment, system integration, and intelligentization, to foster ongoing innovation and a broad application of the technology. [ABSTRACT FROM AUTHOR]

This document is a compilation of research papers that explore the topics of Smart Cities and Positive Energy Districts (PEDs). The papers cover a range of subjects including energy efficiency, renewable energy communities, smart urban management, and financial schemes for sustainable neighborhoods. The findings emphasize the importance of energy renovation, potential energy and financial savings, and the need for collaborative approaches and policy direction to achieve sustainable urban development. The papers also provide insights into innovative solutions and technological advancements that can contribute to the realization of carbon-neutral and sustainable cities. [Extracted from the article]

The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model's complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments. [ABSTRACT FROM AUTHOR]

Functional graded materials (FGMs) have been successfully applied for improving the insulation performance of insulators, yet there is currently no relevant literature on their application in dry-type transformers. Therefore, it is meaningful to study the feasibility of using FGMs to improve the insulation performance of dry-type transformers through research on electric field distribution. This paper sets FGMs in the dry-type transformer spacer only or both the spacer and winding end to change the relative permittivity values in the corresponding areas. By comparing the electric field distribution characteristics on the inner and outer surfaces of the high-voltage coil and the lower surface of the spacer of the dry-type transformer under each control method with the case without setting FGMs, the study found that when FGMs were only set on the spacer, the control methods set in this paper could not improve the electric field distribution of the transformer. When FGMs are employed at the spacers and end windings, two modulation strategies can mitigate the electric field intensity on the outer surface of the high-voltage windings. Specifically, a continuous increase in relative permittivity can reduce the electric field intensity by 34.02%, while an initial decrease followed by an increase can result in a reduction of 13.58%. This paper provides theoretical guidance for insulation design of transformers with high voltage levels of 66 kV and above, especially as transformers with high voltage levels face greater challenges in regulating surface electric fields. [ABSTRACT FROM AUTHOR]

This document discusses the degradation and failure mechanisms of dielectric insulation in medium- and high-voltage power equipment. It highlights the importance of controlling insulation material degradation to prevent equipment failures and reduce the risk of environmental pollution. The document includes several research papers that address various topics related to the measurement, monitoring, and improvement of power equipment components. These papers cover issues such as winding breakdown faults in transformers, overvoltages caused by vacuum circuit breaker interruptions, insulation resistance degradation in cables, temperature rise in composite insulators, partial discharges and their effects on insulation, and acoustic inspection for detecting partial discharges. The document also presents studies on the technical condition of on-load tap changers, the behavior of silicone elastomers under electrical and mechanical stress, and the percolation phenomenon in square matrices. [Extracted from the article]

This paper presents an experimental study of the effect of the mass of dust retained on a fibrous filter bed operating singly and in a "cyclone-filter-bed" system on changes in filtration efficiency and accuracy, as well as the increase in flow resistance. The research was carried out using a novel and unprecedented method, determining the dust absorption coefficient km of the filter baffle under laboratory conditions. A filtration system built of a single cyclone and a cylindrical filter cartridge with an appropriately sized surface set behind it was studied. Conditions corresponding to the actual operating conditions of the air filter were maintained: dust concentration, filtration speed and dust extraction from the cyclone settling tank. The purpose of the research was to evaluate filter materials with different structures in terms of filtration efficiency and accuracy, as well as flow resistance. The study showed that the parameters of the structure of filter materials—permeability, grammage and thickness—affect the process of retaining dust particles. It was shown that the increase in the flow resistance of the filter bed has a higher intensity when dust grains of small sizes are directed at it, which is the case when the bed is operated behind a cyclone, which separates larger dust grains from the air. There is a reduction in the operating time of the filtration system due to the limitation of the permissible resistance ∆pfdop, and the corresponding dust absorption km has a lower value. For a fixed value of the flow resistance, the dust absorption coefficient km2 of three different filtration baffles AC, B2, and B, working with a cyclone, take values 50–100% smaller than when working in a single-stage system. It has been shown that the "cyclone-filter baffle" unit, due to its greater dust separation capability, allows the filter cartridge to operate for a longer time until a certain flow resistance is reached. This allows the unit to operate longer at lower flow resistance without changing the filter cartridge, thus saving energy. The km values obtained during the tests, using the proposed original method, allow the selection of the filter bed for specific vehicle operating conditions by modelling its course. [ABSTRACT FROM AUTHOR]

Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles. [ABSTRACT FROM AUTHOR]

The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable access capacity of DPV in the distribution network, this paper proposes an economic access capacity evaluation method for DPV in the distribution network considering proper PV power curtailment. Firstly, a method for generating typical joint light intensity and load power operation scenarios based on an improved K-means clustering algorithm is proposed, which provides comprehensive scenario support for the evaluation. Secondly, based on active and reactive power regulation, this paper proposes a DPV access capacity enhancement method to improve the DPV access capacity. Thirdly, considering proper PV power curtailment, an evaluation model of DPV economic access capacity in the distribution network is established to solve the maximum DPV economic access capacity in the distribution network. And aiming at the nonlinear problem in the model, the second-order cone relaxation method is employed to transform the model into the second-order cone programming model, so as to solve the objective function conveniently and efficiently. Finally, based on the improved IEEE 33-node distribution network analysis, the results show that the proposed method can be more comprehensive and effective in evaluating the DPV economic access capacity in the distribution network, and proper PV power curtailment can significantly increase the DPV economic access capacity in the distribution network. [ABSTRACT FROM AUTHOR]

There is a general consensus that bio-waste is a suitable material for valorization by means of the fermentation process with the production of biogas. The success of a bio-waste closed-loop economy will ultimately be determined by the demand for the products made from it. Poor-quality composts and fermentation products will not be allowed on the market in the long term. This means that not only final products but also bio-waste from separate collections must also meet the quality requirements. The aim of this 12-month study was a monthly analysis determining the level of contaminants in bio-waste collected from rural communities, single-family neighborhoods in urban areas, and multi-family neighborhoods in urban areas. The share of contaminants in bio-waste from rural areas and single-family urban housing averaged 8.2% and 7.2%, respectively, while multi-family urban housing had a significantly higher average of 16.6%. The primary contaminants identified were treated wood, plastics, mineral wastes, paper, and glass in rural areas and plastics, paper, treated wood, glass, and textiles in urban areas. The close positive correlation found between the total content of pollutants and, in particular, with plastics and kitchen waste in bio-waste collected in rural communities and from multi-family housing in cities indicates that they are likely the main source of the origin of these pollutants. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

In general, the fuel cell electric vehicle (FCEV) is regarded as more environmentally friendly than other vehicles. However, the commercialization of FCEV technology is hardly fulfilled due to high-cost fuel cells and an inadequate refueling infrastructure. Different technological trajectories of fuel cells are fiercely competitive, and related technologies are iterating quickly. It is an open issue in terms of what are the technological advances achieved or the opportunities for innovators. The paper proposes a novel approach to identify the key components of an FCEV by constructing the directed co-occurrence network of the International Patent Classification (IPC) and then adopts the Natural Language Processing (NLP) to construct the matrix of technology characteristics and functions. It is suitable to analyze the sentence structure of Subject–Action–Object (SAO) in patent documents by utilizing the NLP technology, which can help computers understand the text and communicate with us. The paper finds that the advances achieved in the fuel cell field are fuel cell composition, manufacturing fuel cells, and providing energy using fuel cells, and the advance in electric motors is supplying power for fuel cell vehicles, while the advances in hydrogen storage are to manage and store hydrogen. By contrast, the opportunities for innovators are to develop the control, diagnosis, and performance of the control system and hydrogen filling. This paper will be a contribution towards a better understanding of the advances and opportunities for developing FCEV technology. [ABSTRACT FROM AUTHOR]

As an indispensable part of the power system, transformers need to be continuously monitored to detect anomalies or faults in a timely manner to avoid serious damage to the power grid and society. This article proposes a combined model for transformer state prediction, which integrates gas concentration prediction and fault diagnosis models. First, based on the historical monitoring data, each characteristic gas sequence is subjected to one optimal variational mode decomposition (OVMD) and one complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The decomposed sub-sequences are input into a bi-directional long short-term memory network (Bi-LSTM) optimized by the sparrow search algorithm (SSA) for prediction, and the predicted value of each sub-sequence was then superimposed to be the predicted value of the characteristic gas. We input the predicted values of each gas into the improved sparrow search algorithm-optimized support vector machine (ISSA-SVM) model, which can output the final fault type. After the construction of the combined model of state prediction is completed, this paper uses three actual cases to test the model, and at the same time, it uses the traditional fault diagnosis methods to judge the cases and compare these methods with the model in this paper. The results show that the combined model of transformer state prediction constructed in this paper is able to predict the type of transformer faults in the future effectively, and it is of great significance for the practical application of transformer fault type diagnosis. [ABSTRACT FROM AUTHOR]

In a rail transit system, there is a constant leakage of current from the subway rails to the earth, and these stray currents have complex propagation paths and a wide range of influence. Since no stray current collection devices are installed at subway depots, some of the stray current leaking from the mainline will converge at the depot, seriously corroding the structural reinforcement and buried metal of the station, thereby jeopardizing the normal operation of subway trains and passenger safety. In this paper, a field-circuit coupling method is proposed to analyze the current leakage and distribution law of the subway mainline and depot. It is found that the failure of the gauge block at the mainline will trigger the maximum leakage of rail current. Additionally, it is observed that the stray current distribution at the depot is mainly influenced by the operating status of the one-way conduction device (OWCD) and the change of rail potential. These results validate the applicability and effectiveness of the field-circuit coupling method proposed in this paper and provide new technical support for the study of stray current leakage distribution in subways. [ABSTRACT FROM AUTHOR]

This paper aims to help data center administrators choose thermal simulation tools, which manage thermal conduction from power for energy savings. When evaluating and suggesting data center thermal simulators for users, questions such as "What are the simulator's differences? Are they easy to use? Which is the best choice?" are frequently asked. To answer these questions, this paper reviews the thermal simulation works for data centers in the last ten years. After that, it proposes the versatility and dexterity metrics for these simulators and discovers that it is difficult to choose them despite their similar design purpose and functions. Empowered by the survey, we claim that the widespread practice simulators still need more enhancement in data center scenarios. We back up our claim by comparing typical simulators and propose improvements to thermal simulators for future studies. [ABSTRACT FROM AUTHOR]

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an "Electric-Hydrogen-Ammonia" energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy. [ABSTRACT FROM AUTHOR]

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste. [ABSTRACT FROM AUTHOR]

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data's collection and pre-processing. [ABSTRACT FROM AUTHOR]

In this review paper, information about bicycle lamp technical parameters is provided. The review is based on the searched Web of Science (WoS) and Scopus bibliographic databases and Transportation Research Information Services, which include references to books, technical reports, conference proceedings, and journal articles. This review showed that these papers about bicycle lighting legal rules in given countries are very easy to reach, but deep searching must be performed to find this research paper having the information about types of optical systems and light sources used in bicycle lamps. This research was conducted using PRISMA 2020 assumptions in the area of bicycle lighting technologies and innovations. The information about bicycle lamp technical parameters was obtained on the basis of the examination of technical documents and papers from high-impact journals. The author's review shows that contemporary bicycle lamps are becoming increasingly smarter and more energy efficient, while also enhancing safety and comfort for cyclists on their routes in and beyond urban environments. [ABSTRACT FROM AUTHOR]

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China's "Dual-Carbon Goals" and "Clean Heating" policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of "envelope structure + heating system" is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems. [ABSTRACT FROM AUTHOR]

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period. [ABSTRACT FROM AUTHOR]

The diagnosis of vibration faults in hydropower units is essential for ensuring the safe and stable operation of these systems. This paper proposes a fault diagnosis method for hydropower units that combines Dynamic Mode Decomposition (DMD) with an optimized Extreme Learning Machine (ELM) utilizing the Hiking Optimization Algorithm (HOA). To address the issue of noise interference in the vibration signals of hydropower units, this study employs DMD technology alongside a thresholding technique for noise reduction, demonstrating its effectiveness through comparative trials. Furthermore, to facilitate a thorough analysis of the operational status of hydropower units, this paper extracts multidimensional features from denoised signals. To improve the efficiency of model training, Principal Component Analysis (PCA) is applied to streamline the data. Given that the weights and biases of the ELM are generated randomly, which may impact the model's stability and generalization capabilities, the HOA is introduced for optimization. The HOA-ELM model achieved a classification accuracy of 95.83%. A comparative analysis with alternative models substantiates the superior performance of the HOA-ELM model in the fault diagnosis of hydropower units. [ABSTRACT FROM AUTHOR]

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users' electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users' electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account. [ABSTRACT FROM AUTHOR]

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5 % . [ABSTRACT FROM AUTHOR]

In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of scientific papers and market reports analyzing the economic competitiveness of fuel cell electric buses (FCEBs) with respect to their conventional alternatives via the total cost of ownership (TCO) methodology. We discussed the variables and data taken into account and compared the resulting outcomes by year and geographical areas. It emerged that FCBs are not currently cost competitive. The decreasing trend in acquisition and fuel costs, however, indicates potential for future competitiveness. We find that the current TCO literature on FCEBs presents several areas of uncertainty and weakness. Potential improvements can be achieved by: (i) extending the geographic coverage to Asian and African developing countries; (ii) making use of real-world data instead of simulated data, in particular, concerning acquisition costs, hydrogen costs under different pathways, fuel efficiency, and maintenance costs; (iii) clarifying the role of infrastructural costs; (iv) exploring the existence of economies of scale at fleet level; (v) distinguishing among different bus sizes. [ABSTRACT FROM AUTHOR]

High-quality development (HQD) has been listed as the first and foremost task in building a modern socialist country in all respects and also an overarching issue of China's economic and social development in the new era. To achieve economic HQD, a key approach lies in integrating energy development with poverty alleviation and fully leveraging the foundational role of energy infrastructure and supply services in reducing poverty. Using the provincial panel data from 2007 to 2017, this paper analyzes the impact of energy poverty alleviation on economic HQD from multiple dimensions in an empirical way and draws the following conclusions: first, energy poverty alleviation drives the economic growth of China's eastern region and western region, but it cannot effectively promote the synchronous economic growth of the central region, thereby resulting in a greater imbalance in regional development; second, energy poverty alleviation has an effect on reducing the urban–rural income inequality, and such an effect is more significant in the western region; and finally, energy poverty alleviation has a significant effect on promoting economic HQD, and the effect is more significant in the central region and the western region. Furthermore, the transmission mechanism of energy poverty alleviation driving HQD is tested. It is found that energy poverty alleviation can drive HQD by promoting urbanization and technological progress. [ABSTRACT FROM AUTHOR]