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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]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation. [ABSTRACT FROM AUTHOR]

Direct current microgrid (DCMG) clusters are gaining popularity in power systems due to their simplicity and high efficiency. However, DCMG clusters are susceptible to minor disturbances due to low system inertia. This paper proposes a method to enhance the small‐signal stability of a DCMG cluster by optimizing the main control parameters of the system. This paper presents a small‐signal state‐space model of a DCMG cluster system at the system level, considering a multi‐bus network topology. Then, the control parameters that significantly affect the small‐signal stability of the DCMG are selected using the participation factor method. To enhance the system damping, the Pareto‐optimal frontier of the bi‐objective problem was determined using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is determined by using the fuzzy membership function method to extract it from the generated Pareto optimal front. The proposed method has been verified on a three‐sub DCMG test system with droop control. [ABSTRACT FROM AUTHOR]

His current research interests include operation, management and cyber security analysis of smart grids, microgrid, smart city, electric vehicles, artificial intelligence, and machine learning. In recent years, due to the grid modernizations, high penetration of renewable energies, and using smart sensors in the main structure of the power grids, a large amount of data has been generated that can potentially lead to the complexity of the network. His current research interests include power system operation, reliability, resiliency, renewable energy sources, cybersecurity analysis, machine learning, smart grids, and microgrids. TOPIC 1: OPTIMAL OPERATION AND MANAGEMENT OF MULTI-MICROGRIDS USING BLOCKCHAIN TECHNOLOGY Paper 1 by Misagh Dehghani Ghotbabadi et al. investigates the optimal operation of a networked microgrid from the reliability perspective in a correlated atmosphere for the wind generators using an advanced machine learning technique. [Extracted from the article]

The report of the 20th National Congress of the Communist Party of China pointed out that it is necessary to accelerate the construction of digital China, and actively develop equipment intelligence, digitalization, and high‐end equipment manufacturing. With the construction of a new round of digital China, the safety of digital technology and high‐end equipment is particularly important for power grid. Transformer is an important link of power grid operation. The destructive thermal failure of transformer has become a hot research issue in the power industry. At the same time, there are more and more power electronic equipment in the power grid, which makes the regulation and control more and more complicated. Taking a 1000 kVA oil‐immersed transformer as an example, the magnetic, current, and thermal multi field coupling numerical analysis method is first used to simulate and analyse the operation status of the transformer. By comparing the simulation results with the monitoring data results, the error of the results is controlled within 5%. Finally, the multi‐state characteristic parameters of the transformer are monitored through sensors, and the numerical simulation analysis results are integrated with the state monitoring results to build a transformer thermal life loss assessment system. The method in this paper can evaluate and analyse the running state of transformer in real time, which is of great significance for the power company to formulate the treatment measures. [ABSTRACT FROM AUTHOR]

Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

This paper develops a technical framework for the next‐generation power grid transformer (NGPGT) for grid renewables. This framework is structured to overcome the environmental challenges produced by the explosive use of nonrenewable base energy generation sources. The use of these sources cannot meet the required electricity for the world's growing community due to their availability, cost, and lack of flexibility. However, modern energy systems focus on the use of renewable energy sources, where the grid transformer's interaction plays an essential role in their generation, transmission, and distribution. The lack of centralization, local monitoring, interoperability, authenticity, and precise bi‐directional flow may limit the application of current framework power grid transformers in grid renewables. In this paper, a new technical framework, called NGPGT, is developed by introducing some extended features for addressing the challenges shown in current‐generation transformers. This is structured by enabling some advanced technical features with the existing framework, which includes automatic condition monitoring, intelligent inverters, edge computing, automatic controlling, and intelligent management. This paper also illustrates the benefits and scope of the NGPGT compared to the existing transformer by assembling essential requirements and obligatory components. Additionally, this paper highlights a few difficulties of implementing NGPGT in terms of operational, communication, energy management, and economic points of view, which may enable further research scopes for the researchers. [ABSTRACT FROM AUTHOR]

In the three‐phase grid‐connected current‐source inverters (CSIs), the resonance result from the AC‐side CL filter and the quality of the grid‐current waveform under the unbalanced and harmonic grid voltage conditions are two issues deserving attention. To solve the two problems, a continuous control set‐model predictive control (CCS‐MPC) method based on the optimization theory is proposed in the two‐phase synchronous coordinate frame in this paper. The constructed cost function takes grid‐connected current and output current of the inverter as control target, and obtains its optimal solution under the least‐squares method, where the optimal inverter‐side current reference is composed of inductor‐current proportional feedback, capacitor‐voltage proportional feedback, and inverter‐side current steady‐state value feedforward. The proposed method can easily realize the normal operation of three‐phase grid‐connected CSIs because the method can be determined by only one parameter under the unbalanced and distorted grids. Furthermore, in the proposed method, the proportional feedback of capacitor voltage can effectively suppress the CL filter resonance. Finally, simulation and experimental results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

Power transformers are vital to delivering energy in power grids and they bear considerable maintenance costs annually. One of the major issues for power transformers is the ageing of the insulation paper used in windings that affects the life of transformers significantly. Utilising an appropriate procedure for loading of transformers can reduce the ageing rate of insulation and can improve the life expectancy. Therefore, it is crucial to obtain the values for the ageing rate which are optimum and to prevent transformer fleet from accelerated ageing. Although techniques are available to calculate the ageing rate and loss of life of a single transformer, utilities looking into the transformer fleet ageing need a better tool to improve the ageing rate of all transformers together. The main contribution of this study is to provide a method for the transformer fleet ageing optimisation rather than single unit ageing approach. This study proposes a novel approach utilising different optimisation techniques that are capable of representing different factors in order to obtain an optimum ageing rate for a fleet of transformers. The optimum ageing rate can provide a technical tool for asset managers, utility engineers and owners for better power transformer fleet management. [ABSTRACT FROM AUTHOR]

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system. [ABSTRACT FROM AUTHOR]

This paper presents a novel architecture to enhance the performance of grid‐connected photovoltaic (PV) systems through the introduction of several key novelties. Firstly, a packed U‐cell seven‐level (PUC7)‐based single‐phase solar active filter is implemented, offering a comprehensive solution for harmonics mitigation, reactive power compensation, and efficient power extraction from the PV source, while facilitating the injection of real power into the grid. Secondly, the p‐q power injection algorithm is modified to accommodate the extraction of solar power from the PV generator to the grid, simultaneously addressing the need for harmonic current injection to improve power quality. This modification ensures dynamic performance by extracting reference current with harmonic content and solar power information, thereby enhancing the system's overall efficiency. Lastly, the proposed architecture undergoes real outdoor testing, validating its performance in various key aspects including maximum power tracking, reduction of total harmonic distortion in comparison with previous work, operation at unity power factor, and testing the effective operation of the multifunction feature. These contributions collectively demonstrate the effectiveness of the proposed system in enhancing power injection quality and reactive power compensation under real outdoor conditions of PV systems connected to the grid. [ABSTRACT FROM AUTHOR]

The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced. [ABSTRACT FROM AUTHOR]

Literature shows that only 56% of Kenyan households had access to electricity, with rural areas having the lowest access rate at percent. The high cost of extending the power grid to remote areas and power losses on distribution are significant challenges facing rural electrification. In addressing power accessibility problems, especially in rural areas, there is a need for tapping hydropower generation through the invention and implementation of in‐duct turbines to maximize the utilization of already existing pressurized water ducts that supply water in various parts of Kenya for hydropower generation. Makueni County is endowed with gravity‐fed water ducts with high potential for hydropower which can innovatively be produced by application of in‐duct turbines. This paper focuses on the assessment of energy needs and applications in rural areas. The research design was exploratory and experimental in nature. It was exploratory because, through an assessment, it sought to explore and identify the potential areas within the water supply lines for the production of hydropower to supply hydropower in Makueni County. It was experimental because the researcher developed (designed and fabricated) a hydro turbine for use in the production of hydropower from gravity water ducts of a diameter raging 100 mm. The research revealed that 62% (98) used solar power for lighting their homes, while 17% (28), 12% (20), and 8% (12) used lanterns, electricity, and kerosene lamps, respectively. Among the fuels assessed was firewood which was identified as the most used fuel at 89% (140). This was followed at a distance far by paraffin at 6% (9) of respondents. The households at 100% (158) identified electricity as a potential source of lighting for their household. The study recommends harnessing hydropower to enhance reach to 100% of the rural communities. The energy availability will provide opportunities for communities and institutions in rural areas to open their minds to business development and engage in income‐generating activities like the rearing of poultry and the development of light industries like the gridding of maize and other cereals. [ABSTRACT FROM AUTHOR]

This paper sets out to develop an efficient probabilistic optimal power flow (POPF) algorithm to assess the influence of wind power on power grid. Given a set of wind data at multiple sites, their marginal distributions are fitted by a newly developed generalized Johnson system, whose parameters are specified by a percentile matching method. The correlation of wind speeds is characterized by a flexible Liouville copula, which allows to model the asymmetric dependence structure. In order to improve the efficiency for solving POPF problem, a lattice sampling method is developed to generate wind samples at multiple sites, and a logistic mixture model is proposed to fit distributions of POPF outputs. Finally, case studies are performed, the generalized Johnson system is compared with Weibull distribution and the original Johnson system for fitting wind samples, Liouville copula is compared against Archimedean copula for modelling correlated wind samples, and lattice sampling method is compared with Sobol sequence and Latin hypercube sampling for solving POPF problem on IEEE 118‐bus system, the results indicate the higher accuracy of the proposed methods for recovering the joint cumulative distribution function of correlated wind samples, as well as the higher efficiency for calculating statistical information of POPF outputs. [ABSTRACT FROM AUTHOR]

This paper proposes an effective control synthesis strategy to stabilize grid forming converters in the presence of disturbances. The conventional control system for a grid forming converter is based on paralleled and cascaded proportional‐integral (PI) controllers, which are widely used in the industry due to their simplicity and reliability. However, the sequential adjustment of PI gains in different control loops, without consideration of dynamic interconnections limits their performance and capability. To address this limitation, the present paper introduces a control design method based on static output feedback theory. The proposed control scheme reduces the dynamic control design problem to an effective gain scheduling problem. In the proposed methodology, without removing the existing practical control structure, its dynamic parts are moved to the open‐loop system, and simultaneous scheduling of all remaining control gains is achieved during one control design process. MATLAB simulations were conducted to evaluate the performance of the designed control system, and compare it with the conventional control one in terms of step response characteristics. The results demonstrate the superiority of the proposed controller, ensuring system stability and exhibiting improved performance. The proposed control framework provides several advantages, including keeping stability in various disturbance conditions, simultaneous tuning of all control parameters, and no need to change the existing simple control structure. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives. [ABSTRACT FROM AUTHOR]

The Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named "Indian Equatorial Electrojet (IEEJ) Model" is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html). Plain Language Summary: The equatorial electrojet (EEJ) is an intense current jet flowing in the upper atmosphere around 100 km. This is a unique phenomenon that occurs at only a few kilometers in height (e.g., 105–110 km) and with a horizontal (North‐South) width of a few hundred kilometers (e.g., ∼600 km) centered on the geomagnetic equator. This important geophysical phenomenon gives more insights into the equatorial ionospheric electrodynamical processes that have various impacts on satellite orbital dynamics, Global Positioning Systems and other satellite communication links, electrical power grids, etc. This study develops a new empirical model to predict the diurnal, seasonal, and solar activity variations in the intensity of this unique current system over the Indian longitude sector. Key Points: An empirical Equatorial Electrojet (EEJ) field model was developed using nearly two solar cycles of EEJ observations from the Indian sectorThe principal diurnal, semi‐diurnal, and ter‐diurnal components in the EEJ are modeled using suitable analytical functionsThis model can very accurately predict the diurnal, seasonal, and solar activity variations of monthly mean EEJ and CEJ fields at ground [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a three‐layer coordinated optimization strategy based on fuzzy control of renewable energy sources (RESs), vehicle‐to‐grid (V2G) and energy storage. Firstly, the overall structure of this strategy is introduced, which is composed of V2G management system, renewable energy dispatching system and energy storage (ES) real‐time balancing system. Secondly, the control strategy is proposed for different systems, among them, the V2G management system considers shortage of power grid, forecasted maximum output of RESs, and state of charge (SOC) of electric vehicles (EVs) to calculate the charging plan for each EV in next time interval by fuzzy control algorithm; The renewable energy management system gets the expected shortage state of the power grid in the next period according to the charging plan and the current state of the power grid, then combined with the battery state of the energy storage system, the renewable energy guiding power generation in the next period can be calculated by the fuzzy control algorithm; The energy storage real‐time balancing system can charge or discharge according to the real‐time frequency and shortage of power grid to ensure balance between supply and demand of power grid. Finally, the simulation proves that the three‐layer management strategy proposed in this paper can help grid to cut peak and fill valley effectively, improve the load characteristics of the regional power grid and reduce the amounts of the rejected renewable energy. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In the actual power grid environment, there are severe class imbalances in the data sets of various monitoring systems. The existing deep learning methods are highly dependent on the training data sets, and it is difficult to mine the minority sample features in the imbalanced data sets, resulting in Classification recognition accuracy is severely limited. This paper takes the voltage sag as the research object, and proposes a dataset enhancement method based on an improved balanced generative adversarial network. This method combines the advantages of autoencoder and generative adversarial network, learns the public knowledge in the data set through the initialization training of autoencoder, and uses the majority class sample features to assist in generating minority class samples. In addition, an embedding layer is introduced into the self‐encoder architecture to add label information to the eigenvectors, which solves the problem of strong coupling between the distribution of voltage sag eigenvectors and improves the generation effect of samples. At the same time, the Wasserstein distance optimized by the gradient penalty term is used to guide the model adversarial training, which overcomes the problems of vanishing gradient and mode collapse of the original model. Through experimental analysis, the model can fully extract the features of minority samples under unbalanced data sets, ensure the distribution consistency between generated samples and original samples, and take into account the authenticity and diversity of generated samples, which can effectively improve the recognition effect of deep learning model on minority classes and improve the overall recognition accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Wind power prediction (WPP) has an important impact on the security and reliability operations of the power grid. The major difficulty in power prediction of new, expanded, or reconstructed wind farms is the lack of operational data, which leads to insufficient training of the model and makes the prediction error of wind power become enormous. A short‐term WPP model based on stacked denoised auto‐encoder (SDAE) deep learning and multilevel transfer learning is proposed in this paper. First, the correlation coefficient between the samples of source wind farms and the target wind farm is calculated by using a network trained with the samples from the target wind farm. Second, the samples with high correlation coefficients in source wind farms are graded and migrated to the target wind farm to assist multilevel transfer learning. Finally, the samples from different grades are each used to train a layer of SDAE, and their weights and thresholds are migrated to the final network. The case study shows that the 24‐h‐day‐ahead normalized root‐mean‐square error (NRMSE) and 96‐h‐short‐term NRMSE obtained by the proposed method are 4.48% and 5.11% lower, respectively, compared with the model without transfer learning, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Although a recent report by the U.S. Energy Information Administration indicates that CO2 emissions from power generation have now fallen below emissions from the transportation sector, electricity use remains a significant part of human's environmental footprint since fossil fuels are the major energy resource for electricity generation worldwide. This explains the continual effort by policy makers to develop a remedy to mitigate carbon emissions. It is then critically important to properly account for the emissions associated with electricity generation, delivery, and consumption. In particular, it is critical to be able to allocate emissions to the relevant electricity consumers creating the demand that results in a polluting emission. Therefore, in this paper a virtual carbon emission tracing method in power systems is thoroughly presented to facilitate allotting carbon obligation. The method is developed based on the proportional sharing principle that has been used in power system operation. The proposed methodology is implemented on the IEEE 5‐bus and 9‐bus systems, two widely used and recognized power grid benchmarks. [ABSTRACT FROM AUTHOR]

This paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the fault current contributions of small‐scale single‐phase photovoltaic inverters under grid‐connected operation and their potential impact on the protection of distribution systems. To conduct this analysis, an autotransformer‐based voltage dip generator is proposed as a means to test the photovoltaic inverters' contribution to short‐circuit currents. Laboratory tests are then performed to obtain the short‐circuit current contribution of eight single‐phase photovoltaic inverters. Using the short‐circuit current data obtained, a behaviour model is developed and simulated on Matlab‐Simulink. The model is then applied to a real distribution system and case studies are conducted to simulate the potential impacts on the protection system. Results indicate that while the massive penetration of small‐scale single‐phase photovoltaic inverters can alter the protection system's operating time, the impacts are not significant. Only in very specific scenarios, such as events related to high impedance faults, some impact can be observed. In these situations, the presence of photovoltaic inverters further complicates the already difficult task of identifying high impedance faults through conventional overcurrent protections. This study provides valuable insights into the integration of photovoltaic inverters into distribution systems, and can aid in the development of effective protection measures for future grid designs. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

The UK net‐zero target requires the integration of significant renewable energy resources into the electrical grid. This, together with the projected growth in electricity demand imposes practical challenges on the power transfer capability of existing transmission feeders. For environmental and planning reasons, construction of new overhead lines is problematic, and where possible upgrading of existing substations and lines is preferred. This paper investigates the feasibility of transforming an existing three‐phase (3Φ) double‐circuit 400 kV line into a six‐phase (6Φ) feeder for a simulated future GB scenario. Results indicate a 6Φ feeder, operating at a 'phase‐adjacent phase' voltage of 400 kV can effectively solve the constraints of boundaries in transmission networks. When considering the various operating contingencies, associated with an important UK network boundary and its expected near‐future winter‐peak power transfer requirements, the thermal overload issues are mitigated by transforming a feeder from 3Φ to 6Φ. The paper demonstrates upgrading to 6Φ technology is a potential solution to certain future boundary constraints and in these cases avoids the need to build new underground or overhead AC/DC lines. [ABSTRACT FROM AUTHOR]

A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids. [ABSTRACT FROM AUTHOR]

At present, the MVDC distribution grid is developing rapidly and relay protection is the key technology to ensure stable operation. Therefore, this paper focuses on the fault line selection method. Firstly, the fault characteristics are analysed when a fault occurs in the line, and a novel single‐pole fault line selection method is proposed based on 0‐mode power. The polarity of current fault component measured at the terminal of each line is used to identify the internal and external faults. And the 0‐mode power is used to select the faulty line. Finally, to verify the correctness of the proposed method, a radial MVDC distribution grid is built in PSCAD to output fault data in different fault conditions, and the fault line selection algorithm is implemented in MATLAB. The simulation results show that the proposed method is fast, effective, reliable and it has a certain degree of tolerance to transition resistance with a strong anti‐data delay and anti‐interference ability. Moreover, the fault line selection method proposed in this paper can still correctly select the fault line in the case of serious faults such as multiple line faults, sudden removal of lines, and sudden changes in photovoltaic output power. [ABSTRACT FROM AUTHOR]

The operational stability of grid‐connected converters is affected by unbalanced grid voltage, which often leads to overcurrent. The traditional control method maintains the grid‐into current within the rated limit by adjusting the current reference value. However, this method limits the output active power of the converters and affects their reliability, which can cause active power shortage or even disconnection of the power grid. To deal with this issue, this paper proposes an effective control strategy. First, the interaction between AC and DC sides of the converters under unbalanced voltage conditions is analysed. Second, the resilience is defined and elaborated. Third, considering various constraints such as the grid‐into current harmonics, power fluctuations and DC side voltage fluctuations, the operation resilience range of the converters is calculated. Based on the resilience range, an optimal operating point is selected and an optimal strategy is developed. Compared with the traditional strategy, the proposed strategy allows improving the maximum active power output capacity and resilience of the grid‐connected converters under unbalanced grid conditions, while simultaneously limiting the power fluctuations and grid‐into current distortions within an allowable range. Last, simulation and experimental results show the effectiveness and feasibility of the proposed strategy. [ABSTRACT FROM AUTHOR]

Cascaded H‐bridge rectifier (CHBR) is widely used in high‐power fields such as STATCOM and power electric traction transformer (PETT) due to its high efficiency and easy expandability. With the high‐reliability operation requirements of power equipment, the fault‐tolerance capability has gradually become a research hotspot nowadays. In order to improve the reliability of the power supply, cascaded H‐bridge rectifier should restore as soon as possible after a fault. However, the traditional fault‐tolerant strategies have a great effect on the grid side and a long recovery time. This paper proposed a quasi‐hot standby fault‐tolerant control strategy. Compared with the traditional strategies, the quasi‐hot standby fault‐tolerant control strategy has a faster recovery speed after the malfunction, and its transient recovery process has less impact on the power grid. Simulation and experiment results show the effectiveness and good recovery performance of the proposed fault‐tolerant control strategy. [ABSTRACT FROM AUTHOR]

Multi‐terminal flexible DC power grid plays an important role in large‐scale new energy integration, which usually requires the installation of multi‐terminal DC circuit breaker (MTCB) at the intersection of multiple DC lines. At present, the research on multi‐terminal DC circuit breaker is mostly limited to theoretical simulation calculation, and there are few experimental studies and engineering applications. In this paper, a four‐terminal DC circuit breaker based on commutating path multiplexing is proposed, which combines with the compact natural commutation scheme of hybrid gap, greatly reducing the cost and volume of the circuit breaker. The commutation characteristics of the four‐terminal hybrid DC circuit breaker equivalent prototype at 10‐kV voltage level were tested. On this basis, the equivalent prototype of the circuit breaker test results shows that under 10‐kV voltage level, the four‐terminal circuit breaker topology can achieve breaking of 11 kA in 2.8 ms by the natural commutation method. [ABSTRACT FROM AUTHOR]

The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT‐LSTM is designed to extract highly representative power grid operation features considering the spatial‐temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT‐LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39‐bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

Controlling the Permanent Magnet Synchronous Motor (PMSM) can be challenging due to the nonlinearity of its dynamics, which makes it difficult to design control strategies that are both robust and effective. To address this challenge, this paper presents a novel control strategy rooted in the concept of passivity that combines field‐oriented control (FOC). This strategy compels the PMSM to accurately follow velocity and electrical torque trajectories. The approach, known as passivity‐based control (PBC), entails reshaping the inherent system energy while introducing the necessary damping to attain the desired objectives. A crucial aspect involves identifying workless force terms within the process model. Despite their presence, these terms do not impact the energy balance and stability properties. As a result, eliminating these terms is unnecessary. This simplicity in control architecture not only preserves system stability but also bolsters overall robustness. The system's overall stability and the current tracking error's exponential convergence have both been demonstrated analytically. In order to maintain stability, the controller accounts for the nonlinearities of the plant and approximates the unstructured dynamics of the PMSM. The proposed control is designed using the dq model of the PMSM, which avoids the model's structure destruction due to singularities, since the dq model does not depend explicitly on the rotor angular position. Experimental results shown further, illustrate speed and position control with a desired pair calculated by a filter or a proportional‐integral (PI) controller for speed control and a proportional‐integral‐derivative (PID) controller for position control. Also the correlation between practical and theoretical results is given as well as the robustness test in relation to the uncertainties of the PMSM's inertia moment. The results demonstrates the effectiveness of the proposed strategy in controlling the PMSM under different operating conditions, highlighting its potential for industrial applications. [ABSTRACT FROM AUTHOR]

To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

Integrating energy storage units (ESUs) into part of sub‐modules (SMs) enables the decoupling active power control for the modular multilevel matrix converter (M3C). The low frequency AC (LFAC) system based on M3C with symmetrically integrated energy storage (SI‐AM3C) can provide functions such as renewable power fluctuation smoothing and other power grid auxiliary services, which is attractive in engineering practices. This paper focuses on the design and control of SI‐AM3C. For the converter design, an evaluation method for the number of required active SMs (ASMs) is presented. On this basis, the influences of different system operating conditions on the ASM requirement are analyzed in case studies, and the effects of specific circulating injection strategies are also investigated. The control system targeted at decoupled AC side active power for SI‐AM3C is introduced. A modified inner‐arm capacitor voltage balance strategy is introduced to reduce the capacitor voltage deviation between ASMs and passive SMs (PSMs). The conclusions related to the presented estimation method and converter control strategy are verified by time‐domain simulations in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

In a high proportion of new energy grids or a 100% new energy source of power grid, the dynamic performance of PMSG under fault conditions is very important. However, the current small signal model lacks detailed and deep analysis of a permanent‐magnet synchronous generator (PMSG)‐based wind power generation under LVRT control. Here, a small signal model of a PMSG under pre‐fault, fault and after fault failure recovery stages are established. This paper employs linear analyses to characterize the dynamic performance of a PMSG‐based wind power generation system under LVRT control and practical generator characteristics. In particular, the conflict between frequency regulation and LVRT is discussed based on the constructed model. On this basis, the impact of dc‐link bandwidth on the system performance during failure recovery stage is discussed, and the active damping method is analysed to improve the performance of PMSG during failure and failure recovery stages. Finally, time‐domain simulations are utilized to verify the analyses and models discussed. The research results in this paper are helpful to promote and improve the performance of PMSG during LVRT conditions. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast‐charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high‐quality DR resources of VPP in the future. In this paper, a new 'spatial‐temporal' bi‐layer optimal control strategy is proposed to solve the daily real‐time DR dispatching problem of large‐scale EVs including fast‐charging ones in a multi‐area VPP oriented to China's dual carbon target. This proposed bi‐layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow‐charging EVs DR control with 1‐h temporal interval, the lower layer of the proposed strategy adds fast‐charging EVs in the real‐time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed 'spatial‐temporal' bi‐layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi‐area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations. [ABSTRACT FROM AUTHOR]