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The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system. [ABSTRACT FROM AUTHOR]

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid's security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections. [ABSTRACT FROM AUTHOR]

An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach. [ABSTRACT FROM AUTHOR]

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%. [ABSTRACT FROM AUTHOR]

This paper is aimed at performing a comprehensive review of the approaches employed in nanosatellite platforms, specifically 1 U and 3 U, for electrical power systems (EPS). Moreover, it seeks to develop a robust maximum power point tracking (MPPT) controller using the sliding mode control (SMC) method. The proposed control strategy is intended to monitor the solar panel's maximum power point (MPP) and adapt to changes in temperatures and solar irradiance in low Earth orbit (LEO). The EPS model, designed with the proposed sizing method, is built in MATLAB-Simulink and integrates a solar panel, battery storage, and power converters controlled by diverse MPPT methods. Then, simulation results demonstrate the effectiveness of the proposed SMC approach compared to other conventional control methods for the designed EPS under varying LEO conditions. To achieve a robust validation of the most appropriate MPPT control method under steady-state conditions, this paper presents an experimental investigation into the proposed EPS hardware design. The proposed SMC method achieved an increase in power generation from 10% to 12% for buck and boost power converters, respectively, compared to traditional control methods. [ABSTRACT FROM AUTHOR]

This document provides an overview of innovations in thermoelectric technology, with a focus on materials and applications. It discusses thermoelectric generators (TEGs), which convert thermal energy into electrical energy, and their versatility in various settings such as space, medical applications, and vehicles. The document also highlights advancements in thermoelectric materials, such as Cu2−xSe and PbTe0.7S0.3, which improve the efficiency of TEGs. The included research papers cover topics such as TEG design, durability under different conditions, and the development of nanostructured materials. The findings emphasize the need for further research and collaboration to enhance the efficiency and practicality of thermoelectric systems. [Extracted from the article]

The growing use of gas-fired power generators and electricity-driven gas compressors and storage has increased the interdependence between electric power infrastructure and natural gas infrastructure. However, the increasing interdependence may spread the failures from one system to the other, causing subsequent failures in an integrated power and gas system (IPGS). This paper investigates the structural vulnerability of a realistic IPGS based on complex network theory. Different from the existing works with a focus on the static vulnerability analysis for an IPGS, this paper considers both static and dynamic vulnerability analysis. The former focuses on vulnerability analysis under random and selective failures without flow redistribution, while the latter concentrates on vulnerability analysis under cascading failures caused by flow redistribution. Also, different from the existing works with a focus on the IPGS as a whole, we not only analyze the vulnerability of the IPGS but also analyze the vulnerability of the power subsystem (PS) and gas subsystem (GS), in order to understand how the vulnerability of the IPGS is affected by its PS and GS. The analysis results show that (1) if the PS and GS are more susceptible to cascading failures than selective and random failures, the IPGS as a whole is also more vulnerable to cascading failures. (2) There are different dominant factors affecting the IPGS vulnerability under cascading failures and selective failures. Under cascading failures, the GS has a more significant impact on the IPGS vulnerability; under selective failures, the PS has a more important impact on the IPGS vulnerability. (3) The IPGS is more vulnerable to failures on the critical nodes, which are identified from the IPGS as a whole rather than from the individual PS or GS. The results provide insights into the design and planning of IPGSs to improve their overall reliability. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid algorithm using a hybrid firefly and particle swarm optimization (HFPSO) algorithm to determine the optimal placement of STATCOM devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal STATCOM location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms, the HFPSO algorithm is used to determine the optimal placement of STATCOM FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in an electrical power transmission systems. [ABSTRACT FROM AUTHOR]

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system's normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset's optimization to demonstrate the expanded dataset's dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid algorithm using a Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm to determine the optimal placement of SSSC devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal SSSC location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms. The HFPSO algorithm is used to determine the optimal placement of SSSC series FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and IEEE 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in electrical power transmission systems. [ABSTRACT FROM AUTHOR]

The state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change. [ABSTRACT FROM AUTHOR]

Hybrid power supply systems (HPSSs) are considered as a good option for electric power supply of remotely located from the grid consumers due to significant fuel savings compared to diesel sets. Quick development and improvement of HPSSs may be achieved using specialized methodologies and programs. In the paper a schematic diagram is proposed and operation principles of a 400 V / 50 Hz HPSS were developed. The system’s main component is the master generating unit of the hydropower plant using a 250 kW induction generator (IG). The voltage of the system is controlled by the controller of the AC/DC power converter. The electrical frequency of the system is controlled by the speed controller of the hydropower turbine. A wind turbine, an energy storage system and a regulated dump load are connected to the IG through the AC/DC converter. Goal. The paper aims to develop a methodology for steady state performance analysis of the hydraulic turbine driven isolated IG operating in parallel through an AC/DC power converter with additional sources and consumers of active power. Methodology. The methodology for evaluation of performance characteristics of the IG operating in the proposed system has been developed. The methodology is based on the equivalent circuit of the system, equations of active and reactive power balance in the system and the superposition method. Results. The equations of frequency, voltage and power regulators of the system are given. The performance characteristics of the IG operating in the system supplying resistive and RL load in «constant voltage – constant frequency» mode are obtained. Novelty. The developed methodology is innovative in taking into account the control algorithms of the system. The comparative analysis of the IG’s performance operating in the stand-alone generating unit and in the generating unit connected to the proposed system is performed. Practical value. The developed methodology can be used for development and performance improvement of hybrid AC power systems. References 19, table 1, figures 4. [ABSTRACT FROM AUTHOR]

This paper is dedicated to optimizing the functionality of Microgrid-Integrated Charging Stations (MICCS) through the implementation of a new control strategy, specifically the fractional-order proportional-integral (FPI) controller, aided by a hybrid optimization algorithm. The primary goal is to elevate the efficiency and stability of the MICCS-integrated inverter, ensuring its seamless integration into modern energy ecosystems. The MICCS system considered here comprises a PV array as the primary electrical power source, complemented by a proton exchange membrane fuel cell as a supporting power resource. Additionally, it includes a battery system and an electric vehicle charging station. The optimization model is formulated with the objective of minimizing the integral of square errors in both the DC-link voltage and grid current while also reducing total harmonic distortion. To enhance the precision of control parameter estimation, a hybrid of the one-to-one optimizer and sine cosine algorithm (HOOBSCA) is introduced. This hybrid approach improves the exploitation and exploration characteristics of individual algorithms. Different meta-heuristic algorithms are tested against HOOBSCA in different case studies to see how well it tunes FPI settings. Findings demonstrate that the suggested method improves the integrated inverters' transient and steady-state performance, confirming its improved performance in generating high-quality solutions. The best fitness value achieved by the proposed optimizer was 3.9109, outperforming the other algorithms investigated in this paper. The HOOBSCA-based FPI successfully improved the response of the DC-link voltage, with a maximum overshooting not exceeding 8.5% compared to the other algorithms employed in this study. [ABSTRACT FROM AUTHOR]

This paper is an attempt to provide a dual-source inverter, an intelligent inverter topology that links two isolated DC sources to a single three-phase output through single-stage conversion. The converter is designed to be utilized in hybrid photovoltaic fuel cell systems, among other renewable energy applications. The proposed dual-source inverter employs a single DC-AC converter, as opposed to conventional dual-source hybrid inverters which make use of several input DC-DC modules to obtain the voltage formed across the inverter's input DC-link. In the proposed topology, the semiconductor count is low, which leads to improved efficiency, cost, complexity, and reliability. The proposed topology makes use of two impedance networks connected by transformers, diodes, and capacitors. The regulation of the electrical power generated by primary sources and the independence of the converter on key factors like voltage and frequency are essential parameters in multi-input converters. This feature becomes highly prominent when the control algorithm is implemented by conventional processors. Viewed from this perspective, the control method described in this paper is worthy of consideration. The research work describes a 220-W/50 Hz prototype that employs Simple Boost-SPWM. Experimental results verify the analyses and corroborate the satisfactory performance of the suggested converter. [ABSTRACT FROM AUTHOR]

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable. [ABSTRACT FROM AUTHOR]

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This paper presents the results of investigations into modeling the DC and dynamic characteristics of an alkaline electrolyzer. A model of the device under consideration is proposed in the form of analytical relationships in which the coefficients depend on the concentration of the potassium hydroxide solution contained in the electrolyzer under consideration. The correctness of the proposed model is verified by comparing the calculated and measured current–voltage characteristics and the dependence of the module of the impedance of the electrolyzer on the frequency obtained at different values of the electrolyte concentration. The dependence of the time needed to produce a given portion of hydrogen on the supply current and the electrolyte concentration is also presented. Good compliance with the calculation and measurement results is obtained over a wide range of voltage and current, frequency, and concentration of the electrolyte. [ABSTRACT FROM AUTHOR]

As electric vehicle (EV) adoption increases, transportation services will shift dependence from liquid fuel infrastructure to electric power infrastructure. Since transportation plays a major role in disaster response and recovery, this shift in dependence has important implications for coupling electrical grids and transport resilience. The implications for the electrical grid for southwestern British Columbia, Canada are examined, motivated by this region's high EV adoption rate and the potential for a catastrophic magnitude 9.5 earthquake. A comparison of the resilience of the electrical infrastructure compared to the fuel infrastructure is provided and approaches for increasing resilience in the region for EV power supply are discussed. This paper compiles lessons learned from past large earthquakes in Chile, Japan, and New Zealand with consideration given to successes and failures. While this paper was written with a focus on southwestern British Columbia, many of the suggestions for increasing power system resilience could be applied in other seismically active locations during the transition to EVs. This paper also considers integrated disaster resilience planning for the changing transport landscape from fuel vehicles to EVs. [ABSTRACT FROM AUTHOR]

The active power losses are dependent on the flowing electric power value through overhead and cable lines. The current flow through the conductor causes negative phenomena to occur, such as released heat. The source of the current harmonics is the non-linear loads. Hence, the skin effect occurs, and the current carrying capacity of cables is reduced. This results in the increase in and uneven distribution of the temperature inside the conductor. This paper presents a comparison of the temperature distribution inside a power cable for an ideal 50 Hz sine wave and highly distorted current (T H D I = 41 %) . The calculated active power losses for the IEC 60287-1-1:2006+A1:2014 standard and the method described in the literature were used as a basis for further calculations. The obtained results revealed the problem of the uneven distribution of the conductor temperature. Considering the skin effect, increasing the temperature in the outer layers leads to severe damage and faster insulation aging. The abovementioned phenomenon is a decrease in the permissible load capacity of the conductor. The table given in the IEC 60364-5-52 standard summarizes the percentage contribution of the third harmonic to the current waveform. For percentages between 15% and 33%, the current carrying capacity is reduced by up to 86% of the full-load current rating. In addition, consideration of thermal conditions forces the use of cables with larger cross-sections. This leads to their non-optimal use and makes the investment more expensive from an economic point of view. [ABSTRACT FROM AUTHOR]

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller's effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources. [ABSTRACT FROM AUTHOR]

In this paper, a new linear-based closed-loop control method for a Solid-State Transformer (SST) has been proposed. In this new control method, individual current and voltage loops for each of the power conversion stages (AC-DC, DC-DC, DC-AC) are implemented. The feedback between the input and output control signals for each loop is achieved through the voltage on the DC link capacitors and the current transferred between the converters. This enables the SST to be controlled easily in a linear-based closed-loop manner without the need for complex computations. In order to evaluate the performance analysis of the proposed control system, a simulation of an SST with approximately 10 kVA apparent power was performed. Based on the obtained simulation results, the response time of the proposed control method for dynamic load variations was proved to be in the range of 40 milliseconds, and it has been observed that this method allows electrical power to be transferred from the load to the grid. The power factor value of SST under inductive load is measured to be approximately 99%, and the overall system efficiency is 96% and above, indicating that this proposed new control method has very high performance. [ABSTRACT FROM AUTHOR]

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an α β -frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a d q frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model's accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup. [ABSTRACT FROM AUTHOR]

A lot of previous studies have shown a great interest in connecting photovoltaic (PV) arrays to electric power grids. A Maximum Power Point Tracker (MPPT) controlled 100 kW PV array to boost its power that feeds the grid with the help of a three-level inverter. This paper investigated the effects of open-circuit faults of three-level inverter IGBT switches on the performance of the PV array system. This paper proposes a new diagnostic method called the Double Threshold-Trigonometric Coordinates (DT-TC) that relies on the double threshold technique and the trigonometric coordinates, respectively, for early detection and the precise location of open-circuit faults. To the best knowledge of the authors, two simultaneous open-circuit faults were not considered in the previous studies. The obtained simulation results prove the great success of the proposed diagnosis method even under noticeable changes in irradiation, temperature, and the number of PV array connected in series and in parallel. In this study, the reconfigurable fault-tolerant inverter was included to ensure the perfect continuity of the PV array system and its recovery in an extremely very short time up to 7 ms. [ABSTRACT FROM AUTHOR]

This paper proposes a new DC–DC bipolar resonance converter that combines a dual‐active‐bridge and a multi‐port resonance Buck‐Boost converter. This structure uses a resonance network between the input and output ports, which has the following advantages: it creates a constant current supply in the open‐loop mode; it keeps the current phase of the output ports constant under different loads; and it allows reversing the power from any output port by adjusting the phase shift of its switches, if the converter is connected to a DC microgrid. In this way, power can be exchanged among each of the output ports and input ports. The advantages of this converter are bidirectional power exchange, power exchange between output ports, soft switching, high efficiency, integration of transformer parasitic elements, and the modular capability to connect multiple resources at the input. Furthermore, the proposed structure of the bipolar converter has been implemented for verification purposes, and its results are presented in three modes: open loop, voltage control, and current control. Additionally, the results of different modes of power sharing and their bidirectionally are shown. [ABSTRACT FROM AUTHOR]

An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied. [ABSTRACT FROM AUTHOR]

This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of the physical system. Experimental tests confirm its feasibility, showing that the digital twin's real-time outputs closely match those of the physical system. Additionally, the digital twin can be used for control-hardware-in-the-loop and power-hardware-in-the-loop tests, allowing the real-time integration of simulated subsystems with hardware. This capability facilitates testing of new subsystems and optimization during the project's development phases. Additionally, to demonstrate the advanced capabilities of this model, the digital twin is used to simulate the CubeSat electrical power system behavior in real time throughout a complete orbital cycle in low Earth orbit conditions. This simulation provides valuable insights into the CubeSat operation by capturing the transient and steady-state responses of the EPS components under real orbital conditions. The results obtained indicate that the digital twin significantly enhances the testing and optimization process of new subsystems during the development phases of the project. Moreover, the capabilities of the digital twin can be further augmented by incorporating real-time telemetry data from the CubeSat, resulting in a highly accurate replication of the satellite's in-orbit behavior. This approach is crucial for identifying and diagnosing failures or malfunctions in the electrical power system, ensuring the robust and reliable operation of the CubeSat. [ABSTRACT FROM AUTHOR]

This document provides summaries of several research papers related to wave energy converters (WECs). The papers cover various topics, including modeling methods, control strategies, wave directionality, and robust optimization. The research emphasizes the importance of accurate modeling, real-time dynamics, and hardware integration in validating WEC concepts. It also highlights the need to consider wave directionality and mooring dynamics in control synthesis and performance evaluation. The papers compare different control techniques and demonstrate the potential of reinforcement learning in enhancing WEC efficiency. Additionally, the research addresses the challenge of ensuring robustness in WEC design and suggests incorporating stochastic methods to account for uncertainties. [Extracted from the article]

Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently. [ABSTRACT FROM AUTHOR]

Ecofuels and their feedstock come in three main product classes: electrofuels (e-Fuels), biofuels, and non-biowaste-derived fuels. Ecofuels originate from non-fossil sources, derived from circular raw materials such as non-food organic waste, renewable hydrogen, and captured CO2 through a rapid process of carbon fixation. Proposed regulation drafts under discussion indicate that new fuels would need to reach a substantial degree of climate neutrality. The manufacture of all ecofuels, however, requires energy input to accomplish the conversion of the initial feedstock; their climate neutrality claims stem from the use of renewable electric energy and/or biomasses in the production process, but fossil fuels are still the main primary sources of global (and the EU's) electric power, and most biofuels consumed in the EU transport mix are still crop-based, with potential conflicts with food and land use. Furthermore, entirely neglecting GHG emissions from renewable energy generation is scientifically debatable, as the impact of the energy (and the related GHG emissions) embedded in the materials used to build renewable energy facilities is small, but not nil. The paper reports ecofuel trends according to the above-mentioned originating technologies and discusses the issues related to their development. [ABSTRACT FROM AUTHOR]

Automatic distribution master stations (ADMSs) are software platforms that can be used to monitor energy supply and consumption of power demand-side resources as well as to guarantee grid security. They play a vital role in maintaining the optimal performance and stability of electrical power systems. However, the stability of ADMSs is usually determined by the performance of the information interaction, which includes various challenges and complexities. Therefore, an efficient multiple-indicator filtering-based information interaction evaluation method for the ADMS is proposed in this paper. The proposed method first utilizes the principal component analysis (PCA) to filter the indicators of the ADMS. Then, the data envelopment method and the analytic network process (ANP) are employed to remove the invalid indicators and to determine the indicator weights. In this regard, the secondary screening of the indicators is completed, the effective indicators are obtained, and the accuracy of the evaluation system is improved. Results of the simulation show that the proposed method works well when evaluating information interaction performance, and it offers satisfactory performance evaluation of ADMSs. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique for a single-direction solar tracker. The proposed design is based on a sun sensor system that controls the position of the solar panel. The sun sensors of the proposed design contain four photodiodes that are placed on the solar panel in specific angles and directions. The proposed system has several advantages such as the simplicity of implementing the system. This system combines the real-time tracking of sunlight and the low cost of applying a single-direction tracker. The prototyping experiment and Simulink MATLAB were applied to show the advantages of applying a single-direction tracker by following the angle of sunlight during the day. Real-time sun position and irradiation data were applied. The experimental results show that the proposed single-axis sun sensor PV tracker system generates around 20 more electric power than a fixed-structure PV system. [ABSTRACT FROM AUTHOR]

This document provides an overview of the advancements in energy harvesting technologies and their integration into the Internet of Things (IoT) and wireless sensor networks. It includes 31 papers covering various energy harvesting devices, optimization techniques, and potential applications. The research aims to improve the efficiency and sustainability of wireless sensor networks while minimizing energy consumption. The articles explore different methods of energy harvesting, such as solar, thermal, vibrational, and radio frequency energy, and discuss their application in various fields, including wearable devices, solar energy harvesting, and IoT-enabled smart meters. Overall, this collection offers a comprehensive overview of the current research and advancements in energy harvesting for sensor networks. [Extracted from the article]

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the effects of electric current stressing on damping properties of Sn5Sb solder. Design/methodology/approach: Uniformly shaped Sn5Sb solders were prepared as samples. The length, width and thickness of the samples were 60.0, 5.0 and 0.5 mm, respectively. The damping properties of the samples were tested by dynamic mechanical analyzer with a cooling system to control the test temperature in the range of −100 to 100°C. Simultaneously, electric current was imposed to the tested samples using a direct current supply. After tests, the samples were characterized using scanning electron microscope, electron backscatter diffraction and transmission electron microscope, which was aimed to figure out the damping mechanism in terms of electric current stressing induced microstructure evolution. Findings: It is confirmed experimentally that the increase in damping properties is due to Joule heating and athermal effects of current stressing, in which Joule heating should make a higher contribution. G–L theory can be used to explain the damping properties of strain amplitude under current stressing by quantitative description of geometrically necessary dislocation density. While the critical strain amplitude and high temperature activation energy decrease with increasing electric current. Originality/value: These results provide a new method for vibration reliability evaluation of high-temperature lead-free solders in serving electronics. Notably, this method should be also inspiring for the mechanical performance evaluation and reliability assessment of conductive materials and structures serving under electric current stressing. [ABSTRACT FROM AUTHOR]

The development of energy recovery systems in vehicles is a form of synergy between energy efficiency and renewable energy to overcome the energy crisis and global warming problems. This paper addresses such challenges by introducing a novel design of a piezoelectric energy harvester (PEH) for a vehicle suspension system. The PEH is designed to capture the linear vibration of the suspension system subjected to road surface roughness and move on uniform bridges. The vibration is amplified by a pressurized liquid cylinder-piston mechanism which deforms the piezoelectric bar to produce electric power. An additional novel mechanism in the form of a piezoelectric protector is also proposed to avoid physical damage due to excessive unidirectional compressive force as a result of an unpredictable uneven road. Given the key design parameters, the electric power can be harvested up to 67.5 W for a PEH with a span of 150 m of the bridge, a velocity of 40 m/s, and a vehicle mass of 2,691.6 kg. [ABSTRACT FROM AUTHOR]

The development of wearable electronics is revolutionizing human health monitoring, intelligent robotics, and informatics. Yet the reliance on traditional batteries limits their wearability, user comfort, and continuous use. Energy harvesting technologies offer a promising power solution by converting ambient energy from the human body or surrounding environment into electrical power. Despite their potential, current studies often focus on individual modules under specific conditions, which limits practical applicability in diverse real-world environments. Here, this review highlights the recent progress, potential, and technological challenges in energy harvesting technology and accompanying technologies to construct a practical powering module, including power management and energy storage devices for wearable device developments. Also, this paper offers perspectives on designing next-generation wearable soft electronics that enhance quality of life and foster broader adoption in various aspects of daily life. [ABSTRACT FROM AUTHOR]

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements. [ABSTRACT FROM AUTHOR]

The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together. [ABSTRACT FROM AUTHOR]

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm's performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system's energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes. [ABSTRACT FROM AUTHOR]