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This article discusses the role of power electronic converters in smart grids and electric vehicle applications. The authors highlight the technical connection between renewable energy, smart grids, energy storage, and electric vehicles. The special issue includes twelve accepted papers that cover topics such as high gain DC-DC converters, power converters for electric vehicle and motor drive applications, and power system applications. The papers present various novel topologies and control techniques, indicating the rapid growth and future potential of power converters in these fields. The authors express their appreciation to the contributing authors and anonymous reviewers for their valuable contributions to this special issue. [Extracted from the article]

In the not-too-distant future, humans will return to the Moon and step foot for the first time on Mars. Eventually, humanity will colonize these celestial bodies, where living and working will be commonplace. Energy is fundamental to all life. The energy that people use to sustain themselves on Earth, and in particular on these other worlds, is the integrated, safe production of electrical power, day and night. This paper proposes a radically new solution to this problem: Solar Tracking by day and a Solar Rechargeable Calcium Oxide Chemical Thermoelectric Reactor by night. Called the "Robotic End Effector for Lunar and Martian Geological Exploration of Space" (REEGES) Day/Night Power Generator Station, this form of thermoelectric power generation is mathematically modeled, simulation is performed, and a concept model design is demonstrated in this paper. The results of the presented simulation show the maximum total system output capability is 9.89 V, 6.66 A, and 65.9 W, with an operating time of up to 12 h, through a scalable design. This research provides instructions to the Space Research Community on a complete and novel development methodology for creating fully customized, configurable, safe, and reliable solar/thermoelectric day/night power generators, specifically meant for use on the Moon and Mars, using the Proportional-Integral-Derivative++ (PID++) Humanoid Motion Control Algorithm for its operation on a computationally lightweight microcontroller. [ABSTRACT FROM AUTHOR]

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade. [ABSTRACT FROM AUTHOR]

This document discusses recent advances and challenges in emerging power systems, specifically focusing on the integration of renewable energy sources with conventional power networks. The development of renewable energy solutions has been driven by concerns about diminishing fossil fuels, increasing energy demand, pollution, and global warming. The integration of renewable energy sources presents challenges in terms of operation, control, and protection practices. The document highlights 16 research papers that cover various topics related to power systems and the challenges faced by emerging power systems. These papers explore concepts such as floating wind farms, coordinated control of fast charging stations and distributed energy resources, AI tools for voltage analysis, open power meters, fault location methods, load forecasting, inductive compensation, blockchain-based energy markets, vibration suppression in smart structures, and more. The document concludes by inviting further papers for a second volume on recent advances and challenges in emerging power systems. [Extracted from the article]

Science and technology, with their miraculous advancements, have fascinated human life to such an extent that imagining a world without these innovations is hardly possible. While technology is on the rise, an increase in immoral activities should also be noted. From a technical perspective, "power theft" is a non-ignorable crime that is highly prevalent and directly affects the economy of a nation. Today's life is unimaginable without electricity, almost all things, equipment, and appliances used daily rely on electric power to run smoothly. Solar energy is an alternative but has limitations, such as environmental changes and high initial costs. From rural to urban areas and from domestic to industrial sectors, the use of electricity has increased, but so has power theft. Detecting and eradicating such crimes with the help of the developing scientific field is the "need of the hour." With these views, the paper was conceived and designed. The paper provides a complete and comprehensive tool to stop power theft, which is extremely simple to understand and easy to implement. [ABSTRACT FROM AUTHOR]

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as "Start & Stop" systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. [ABSTRACT FROM AUTHOR]

Effective quality management plays a pivotal role in ensuring the smooth operation of smart city systems, which have significant implications for safety, accessibility, affordability, and maintainability. Dependability of autonomous systems is of utmost importance, as achieving satisfactory levels of availability and reliability poses considerable challenges. Smart cities are characterized by interconnected sub-architectures, encompassing vehicle monitoring, sidewalk monitoring, and building monitoring, all of which need to function efficiently. Analytical models such as Petri nets, Markov chains, and fault trees are well-suited for evaluating complex scenarios in the context of smart cities. This paper presents analytical models that utilize fault tree and Markov chain techniques to assess the availability and reliability of smart city monitoring systems. The model is divided into shared and non-shared components, with non-shared components being specific to certain contextual applications, while shared components, such as data processing and electrical power, are essential for all smart city monitoring and management systems. The study underscores the ease with which the fault tree model can enhance availability by modifying failure requirements and resources. Case studies provide concrete examples of how availability improved from 95.3 to 99.8% by varying a configuration known as "KooN" in multiple components. This paper takes a comprehensive approach to evaluating the dependability of smart city architectures and contributes advancements, such as hierarchical modeling, sequential sensitivity analysis, and the "KooN" analytic method. These contributions expand the existing knowledge and methodologies in smart city dependability analysis. Moreover, this work aims to serve as a practical tool to assist smart city managers in optimizing their proposals. All modeling aspects and parameters are detailed thoroughly to enable effective implementation of the proposed approach by anyone using it. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources. [ABSTRACT FROM AUTHOR]

As a deep connection between agriculture and energy, the rural integrated energy system (RIES) is a micro-scale supply-distribution- storage-demand network, which provides an important means to realize the utilization of rural clean energy. This paper proposes a day-ahead scheduling model of the RIES to improve its economical effectiveness, where three energy carriers, namely, biogas, electric power, and heat, are integrated. To address the source and load uncertainties composed of photovoltaic power, power load, and heat load, this paper develops a constrained distributionally robust optimization (CDRO), which optimizes the cost expectation related to the extreme distribution to enhance the robustness, while limiting the loss of cost expectation in the historical distribution to ensure economical effectiveness. In addition, an ambiguous set of the source and load uncertainties incorporating 1-norm and infinity-norm constraints is established, which realizes a flexible adjustment for the conservativeness of CDRO. The distributionally robust dispatch is formulated as a deterministic programming in a two-stage solving framework, where the subproblem uploads its extreme probability distribution to the master problem, and these two problems are iteratively optimized until the convergence. Finally, the numerical simulations in a modern farm park prove the performance of the constructed dispatch model and the flexibility of CDRO in balancing the economical effectiveness and robustness of the dispatch. [ABSTRACT FROM AUTHOR]

The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes a method of monitoring the condition of three-phase asynchronous induction motors running with no load based on computer analysis of the instantaneous active electrical power. This paper experimentally reveals some of the resources offered by the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with a squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor with no load, and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. This paper proposes a procedure for extracting this periodic component description (as a pattern by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g., a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments, which show that a slight change in the no-load running conditions of the motor (e.g., by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern. [ABSTRACT FROM AUTHOR]

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In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. [ABSTRACT FROM AUTHOR]

The coupling between modern electric power physical and cyber systems is deepening. An increasing number of users are gradually participating in power operation and control, engaging in bidirectional interactions with the grid. The evolving new power system is transforming into a highly intelligent socio–cyber–physical system, featuring increasingly intricate and expansive architectures. Demands for stable system operation are becoming more specific and rigorous. The new power system confronts significant challenges in areas like planning, dispatching, and operational maintenance. Hence, this paper aims to comprehensively explore potential synergies among various power system components from multiple viewpoints. It analyzes numerous core elements and key technologies to fully unlock the efficiency of this coupling. Our objective is to establish a solid theoretical foundation and practical strategies for the precise implementation of integrated planning and operation dispatching of source–grid–load–storage systems. Based on this, the paper first delves into the theoretical concepts of source, grid, load, and storage, comprehensively exploring new developments and emerging changes in each domain within the new power system context. Secondly, it summarizes pivotal technologies such as data acquisition, collaborative planning, and security measures, while presenting reasonable prospects for their future advancement. Finally, the paper extensively discusses the immense value and potential applications of the integrated planning and operation dispatching concept in source–grid–load–storage systems. This includes its assistance in regards to large-scale engineering projects such as extreme disaster management, facilitating green energy development in desertification regions, and promoting the construction of zero-carbon parks. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid filter topology that combines passive and active components to enhance harmonic filtering and resonance damping in electrical power systems. The design integrates a three-phase two-level voltage-source converter with a double-tuned passive filter in parallel, significantly reducing the power rating and operational costs while maintaining good harmonic filtering performance and reactive current compensation. Double-tuned passive filters, compared to single-tuned ones, offer improved harmonic attenuation at multiple frequencies, enhancing overall system efficiency. Moreover, when used with the proposed hybrid filter topology, the double-tuned version allows for even lower dimensions of the active part, thereby further reducing system cost. A state-feedback controller is designed to enhance the performance of the hybrid filter, proving particularly effective in environments with complex impedance conditions. This paper also examines the impact of variations in passive component parameters, demonstrating the design's robustness against potential deviations expected over the operational lifespan. The results indicate that the hybrid filter effectively mitigates harmonics and maintains operational stability under various transient conditions, as confirmed by analytical and simulation studies on a real industrial network model. These findings underline the hybrid filter's potential to significantly improve power quality in modern electrical networks. [ABSTRACT FROM AUTHOR]

The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach--the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model's characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals. [ABSTRACT FROM AUTHOR]

Introduction. Solar photovoltaic (PV) has recently become very important especially in electrical power applications for countries with high luminosity because it is an effectively unlimited available energy resource. Depending on solar radiation and temperature, the PV generator has a non-linear characteristic with a maximum power point (MPP). The novelty is the efficiency improvement of a PV energy module, it is necessary to track the MPP of the PV array regardless of temperature or irradiation circumstances. Purpose. This paper presents the modeling and the digitally simulation under MATLAB/Simulink of a Fuzzy Logic Controller based on Voltage Perturbation Algorithm (FLC-VPA) applied to PV battery charging system, which consists of PV module, DC-DC boost converter, MPP tracking (MPPT) unit and battery storage. Methods. The DSP1104 is then used to experimentally implement this MPPT algorithm for real-time driving. The obtained results show the high precision of the proposed FLC-VPA MPPT around the optimal point compared to the conventional VPA under stable and changing meteorological conditions. Practical value. The experimental results approve the effectiveness and validity of the proposed total control system in the PV system. References 30, tables 3, figures 17. [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]

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]

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]

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]

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]

A set S ⊆ V is referred to as a k-fault-tolerant power-dominating set of a given graph G = (V , E) if the difference S ∖ F remains a power-dominating set of G for any F ⊆ S with | F | ≤ k , where k is an integer with 0 ≤ k < | V | . The lowest cardinality of a k-fault-tolerant power-dominating set is the k-fault-tolerant power-domination number of G, denoted by γ P k (G) . Generalized Petersen graphs G P (m , k) and generalized cylinders S G are two well-known graph classes. In this paper, we calculate the k-fault-tolerant power-domination number of the generalized Petersen graphs G P (m , 1) and G P (m , 2) . Also, we obtain γ P k (G) for the subclasses of cylinders S C m and S B m . [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]

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation. [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 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]

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 traction converter modulation generates switching-frequencies current harmonics. The trapped filters can eliminate these switching harmonics, reducing total inductance and filter size. Nonetheless, in comparison with the typical inductor-capacitor-inductor (LCL) filter, the trap inductor needs a larger magnetic core. Moreover, the trapped filter has not been analyzed in the traction systems. This paper proposes a magnetic integrated inductor-trap-inductor (LLCL) filter to decrease the filter's size and investigate its application in traction converters. In fact, the application range of this filter is quite broad, and it can be used in various electrical power systems, including industrial power systems, renewable energy systems, transportation systems, and building power systems. The LC-trap may be formed by connecting the equivalent trap inductor, introduced through the magnetic coupling between inverter-side and grid-side inductors, in series with the filter capacitor. Furthermore, for H-bridge unipolar pulse width modulation (PWM) traction converters, the prominent switching harmonics are concentrated at the double switching frequencies. Therefore, the stability zone is expanded by moving the resonance above the Nyquist frequency. The presented filter's features and design are thoroughly analyzed. The proposed method is finally validated by the MATLAB/Simulink simulation and hardware-in-the-loop (HIL) experimental results. Compared to the discrete windings, the integrated ones can save two magnetic cores. Furthermore, the proposed filter can meet IEEE criteria with 0.3% for all the harmonics and total harmonic distortion (THD) of 2.15% of the grid‐side current. [ABSTRACT FROM AUTHOR]

High mechanical strength surge arresters (HPCAs) are essential components for protecting electrical power systems from overvoltage surges. However, HPCAs can fail under cantilever loading conditions, which is a rare but serious event that can have significant consequences for the reliability of the power system. This paper presents a failure analysis and optimization of HPCAs based on finite element analysis (FEA). A three-dimensional FEA model was developed and used to evaluate the cantilever strength of the arrester under various design configurations. The existing design configuration with 12 insulating rods of 5.8 mm diameter was found to fail to meet the cantilever strength criteria. Therefore, the deflection values of various configurations of insulating rod diameter (5.8, 8, 10, 12, 14, and 16 mm) and number of rods (12, 18, and 14) were evaluated. The depth of the insulating rods inside the aluminum end fittings (EFs) was also varied. This work further employs a ramping method in analysis to reduce processing time and applies adhesion boundary conditions to simulate the behavior of the insulating rods and aluminum EFs. These approaches have allowed us to accurately model the behavior of the HPCA under cantilever loading conditions and investigate the failure mechanisms of the system. The FEA results showed that the cantilever strength of the HPCA could be significantly improved by increasing the diameter of the insulating rods and/or increasing the number of rods. The proposed approach is used to develop a new HPCA design with improved mechanical strength, which is validated through experimental testing. [ABSTRACT FROM AUTHOR]

This research paper aims to investigate the application of a unified power flow controller (UPFC) for relieving power congestion on a 500/230 kV grid, Power congestion occurs when the demand for electrical power exceeds the transmission capacity of the grid resulting in potential stability issues and voltage fluctuations. The UPFC is a flexible and advanced power electronic device that can regulate power flow and voltage levels in real-time hence offering a potential solution to alleviate congestion. This study explores the principles of UPFC operation its control strategies and the impact of its installation on power system stability and reliability. The research methodology involves computer simulations and analysis of the results obtained. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement. [ABSTRACT FROM AUTHOR]

An important issue concerning the inspection of the technical condition of electrical power components and systems is thermal imaging investigation. This paper presents how the thermograms obtained from these measurements should be interpreted correctly according to different standards and how operators should react when detecting a specific anomaly. It is also a review article in which all currently applicable international standards are referred to. The motivation of the article relates to the fact that these standards seem to be too general and do not cover all practical situations, even though, in many countries, thermal imaging diagnostics of overhead lines or overhead outdoor and indoor power stations have been used for years based on industry standards or good practices. The article aims are precisely to encourage and provoke the global community of metrologists, scientists, and engineers involved in thermographic measurements to discuss, strengthen efforts, and establish relevant international standards for the interpretation of thermograms containing the relevant temperature anomalies. [ABSTRACT FROM AUTHOR]

Since the International Maritime Organization has paved the way towards zero emissions, more frequent applications of transition fuels in newly constructed ships have been observed. Fuels such as LNG or methanol are characterised by lower emissions when burned in engines. In order to diminish fuel consumption, and hence the rate of emission, support for the performance of a ship's engines with energy from renewable sources such as photovoltaic (PV) installations is increasingly common. The efficiency of PV panels decreases with an increase in temperature, and it is therefore important to cool them. However, the cooling systems for PV panels impose additional financial outlays as well as a higher demand for electric power, which is needed to run the circulating pumps. A natural cooling process can occur in some vessels via high sea waves, when the ship's decks are flooded by outboard water, although such cooling has an accidental and periodic character, as it results from weather conditions. Despite this, its potential demands a closer examination. This paper presents experimental research carried out with an experimental setup that allowed us to simulate the process of flooding of a PV panel with water waves of various frequencies, resulting from choppy periods at sea. Depending on the weather conditions and the character of flooding, our results indicate that an increase in the power of the PV module of about 18% is obtainable. Our research shows that when the intervals between flooding are longer, the periodic character of the temperature changes obtained by flooding the front surface of a PV panel with water is more distinctive. [ABSTRACT FROM AUTHOR]

The increase in the global temperature due to greenhouse gas emissions is a major concern to the world. To achieve the goal of zero emissions by 2050 in the USA the practical realization of all-electric vehicles, particularly all-electric aircraft (AEA), is important. For the design of electrical power systems (EPSs) in all-electric aircraft, a bipolar medium-voltage direct current (MVDC) system of ±5 kV is being investigated. However, several challenges manifest when using such voltages in a low-pressure environment. One of the main challenges is the partial discharge (PD) behavior of the insulation. It is important to study the PD behavior of the insulation by simulating the aviation environment in the lab. This work aimed to study the partial discharge behavior of air under a negative DC voltage in a needle-to-plane electrode geometry by simulating the aviation pressures in the lab. The partial discharge inception voltage (PDIV) and the breakdown voltage (BDV) show an obvious pressure-dependent variation. Regression analysis was performed to better understand the relationship between the PDIV and pressures. Plots were drawn for the average discharge current at each voltage step until breakdown. This paper's findings can provide valuable insight into the design of EPS for an AEA. To the best of our knowledge, such a study has not been carried out to date. [ABSTRACT FROM AUTHOR]

Summary: Autonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift. [ABSTRACT FROM AUTHOR]

The Autonomous Underwater Vehicle (AUV) is capable of autonomously conducting underwater cruising tasks. When combined with docking operations, the AUV can replenish its electric power after long-distance travel, enabling it to achieve long-range autonomous monitoring. This paper proposes a positioning method for the cruising and docking stages of AUVs. Firstly, a vision guidance algorithm based on monocular vision and threshold segmentation is studied to address the issue of regional noise that commonly occurs during underwater docking. A solution for regional noise based on threshold segmentation and proportional circle selection is proposed. Secondly, in order to enhance the positioning accuracy during the cruising stage, a fusion positioning algorithm based on particle filtering is presented, incorporating the Doppler Velocity Log (DVL) and GPS carried by the AUV. In simulation, this algorithm improves positioning accuracy by over 56.0% compared to using individual sensors alone. Finally, experiments for cruising and docking were conducted in Qingjiang, Hubei, China. The effectiveness of both methods is demonstrated, with successful docking achieved in four out of five attempts. [ABSTRACT FROM AUTHOR]

This paper analyzes the behavior of an electrical power system when N-1 contingencies occur in the transmission stage, which can be produced by incorrect operation of the protection relays, phenomena of natural origin, or increased loadability, which affect the operation and reliability of the electrical system. The operation output of a transmission line results in the variation of the nominal values of the electrical parameters involved because they disturb the stability of the generation, transmission systems, and the supply of electrical energy to the loads, such as voltages and angles of the nodes and the active and reactive power of the system. The proposed methodology was based on analyzing the different electrical parameters of the power system, quantifying the contingency index in a state of regular operation, and comparing it to operation in contingency N-1, with which the most severe contingency was determined and, therefore, achieved; identifying contingencies that can cause system collapses; improving the contingency index from 23.08555 to 22.9276624 for the L16–19 contingency and to 22.9795235 for the L21–22 contingency, which are the most severe contingencies determined with the proposed methodology. To test the proposed methodology, the IEEE 39 bus-bar test system was considered, and the elements that should be implemented to avoid the vulnerability of the power system to N-1 contingencies were determined. [ABSTRACT FROM AUTHOR]