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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.

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.

Hydrothermal carbonization (HTC) of low quality, wet biogenic residues into intermediate bioenergy carriers can potentially contribute to a more flexible and stable renewable energy system and reduce environmental impacts compared to current residue disposal practices. This study quantifies the environmental impacts via life cycle assessment (LCA) of a novel hydrothermal process for the treatment on an industrial scale of application of three wet biogenic residues (paper bio-sludge, olive pomace, and orange peel) into bioenergy carriers, i.e., solid pellets and biogas. A comprehensive attributional cradle-to-gate life cycle assessment (LCA) was conducted; the life cycle impact assessment (LCIA) utilised the ReCiPe impact assessment method. A selection of 10 significant impact categories was prioritised. Reliability of this categorization was also ensured through a sensitivity analysis carried out using Monte Carlo simulation. Climate change, particulate matter formation and terrestrial acidification impact categories showed the highest reliability, while for freshwater ecotoxicity and freshwater eutrophication impact categories in the study suggest the need for more robust data and further investigation. The climate change impact category presents the following values, as kg CO2eq/tresidue: pulp and paper bio-sludge (PPB), 17.9; olive pomace (OP), −1290; orange peel (ORP), −1301. The LCA study compared electricity yields of the hydrothermal treatment process with conventional treatment processes for each of the target residue streams. The environmental performance of the proposed hydrothermal treatment benefits significantly from the combination of intermediate bioenergy carriers (pellets) from the solid fraction with biogas production from the liquid fraction. Avoided emissions due to the heat recovery provide further environmental benefits. The LCIA results show that the carbon footprint of the F-CUBED production system, as kgCO2eq/kWhe, accounts for –4.56, −0.63, and −0.25 for paper bio-sludge, olive pomace and orange peel, respectively.

Power transformers are one of the most expensive and important equipment in the power system. Significant differences exist in the insulation lifespan of transformers that have been in operation for more than 20 years, and using identical maintenance or scrapping methods may result in significant economic losses. However, most existing transformer life assessment methods only consider the impact of moisture content on the life decay rate without considering the impact of oil circulation cooling modes, which leads to some evaluation errors. In this study, we established a new transformer life assessment method that considers the influence of moisture content and oil circulation cooling modes, which is more accurate than most life assessment methods. Then, the proposed life evaluation method was validated on the on-site transformers, demonstrating its accuracy and effectiveness. The novelty of this study is that it establishes a new on-site transformer life assessment method that considers the comprehensive effect of moisture content and oil circulation cooling mode, which helps to evaluate the remaining lifespan of power transformers more accurately and thus extends the transformer lifespan systematically.

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

In the paper, an equation with two delay times (dual-phase lag Equation (DPLE)) in a version that takes into account the dependence of thermophysical parameters (volumetric specific heat and thermal conductivity) on temperature is considered. In particular, an analysis of the sensitivity of transient temperature field in relation to disturbances in delay times (the relaxation and thermalization times) is performed. The sensitivity model concerns the process of heating an ultrathin metal layer with a laser beam. First, the equation with two delay times in the case of temperature-dependent thermophysical parameters is presented. Next, the sensitivity equations with respect to delay times are derived using the direct method. The algorithms for solving the basic and sensitivity tasks are also briefly presented. At the stage of computations, an authorial program based on the implicit scheme of a finite-difference method is developed. In the final part of the paper, examples of numerical solutions (for layers made from gold and nickel) are presented. The research conducted here shows that disturbances in the temperature field are clearly visible and depend, on the one hand, on the thermophysical parameters of the material, and on the other hand, on the intensity of heating with an external heat source.

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities.

The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors’ knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis.

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.