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The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer. [ABSTRACT FROM AUTHOR]

Epoxy‐impregnated paper transformer bushings are prone to water absorption due to the presence of water‐attracting moieties in the cellulose chemical structure and epoxy resin, leading to moisture‐induced insulation defects. To understand the mechanism of moisture's impact on epoxy‐impregnated paper, this study developed a testing platform that integrated moisture diffusion, adsorption, and dielectric properties of insulating materials. Insulation specimens made of epoxy resin and epoxy‐impregnated paper were prepared and their water diffusion characteristics were investigated using the Fick diffusion model and the Langmuir model, respectively. The Guggenheim–Anderson–de Boer equation was used to examine the vacuum vapour adsorption characteristics of the materials, providing a theoretical foundation for examining the form of water present within them. The dielectric response of materials with varying moisture levels was then tested to study the effect of water on the dielectric spectrum curve. Using the Extended Derivative Method, the characteristic frequency reflecting the water content of the epoxy‐impregnated paper was extracted. The results demonstrate that 0.01‐Hz characteristic frequency provides superior accuracy for moisture assessment in epoxy resins compared to 50 Hz, and extracting and fitting the relaxation peak characteristic frequency in epoxy‐impregnated paper moisture evaluation yields higher accuracy than using tan δ at the power frequency. [ABSTRACT FROM AUTHOR]

TiO2 nanoparticles can improve the electrical performance of oil paper insulation. In this study, 10 nm TiO2 nanoparticles were prepared and dispersed into transformer oil, and then immerse pressboards in it to obtain nanofluid impregnated pressboards (NP). Its thermal aging was accelerated together with the reference sample at 130°C for 36 days. Analyzing the acid value in the oil, polymerization degree, surface topography, AC breakdown strength, surface flashover characteristics and surface potential decay characteristics. It was observed that TiO2 nanoparticles can reduce the acid value and improve AC breakdown strength of oil. However, the degree of polymerization and surface appearance of NP and oil impregnated pressboards (OP) during thermal aging did not differ significantly. During the aging process, the energy level of pressboard surface traps shifted first towards the shallow and then gradually towards the deep. During all the aging stages, the surface flashover voltage of NP was always higher or close to that of OP. The mechanism of TiO2 nanoparticles improving the breakdown and surface flashover characteristics of oil–paper insulation during thermal aging is discussed from the perspective of water molecules and aging by‐products adsorption and introduction of shallow traps by nanoparticles. [ABSTRACT FROM AUTHOR]

Arc discharge in the power transformer can bring enormous economic losses and safety risks. The transformer protection system for arc discharge faults is still not ideal. The physical characteristics of the whole process from partial discharge defects transition to arc faults are not fully recognized, mainly because it is difficult to establish and sense the relevant characteristic parameters. This paper built a research platform for studying the transition process from partial discharge to early arc discharge based on fibre‐optic ultrasonic, temperature, pressure sensors with traditional electrical voltage and current sensors. Five discharge models are designed and the changing pattern of multiple parameters in the transition process of different models is studied. The experiment results show that the amplitude and frequency of acoustic waves drop significantly after the arc breakdown. Ultrasonic and high‐frequency current sensing in the early warning of arc fault each has its own limitations. The pressure caused by the arc is not only related to the arc energy but also related to the discharge types. The overall oil temperature change after the early arc discharge is small and hard to detect. This work highlights the promise of using fibre‐optic sensors for physical process research within a harsh electromagnetic environment. [ABSTRACT FROM AUTHOR]

In order to solve the problems of oil‐paper insulation deterioration of oil‐immersed power transformers, including the instability of performance degradation quantity at the early stage, non‐linear deterioration process and uncertainty of life prediction, an improved Wiener stochastic process based on strong tracking filter (STF) is proposed to predict the remaining life of transformer oil‐paper insulation in this paper. Firstly, in order to grasp the dynamic process from healthy state to current deterioration state, a deterioration model is established based on Wiener random process. Secondly, in order to improve the accuracy, the initial values of model parameters are estimated based on expectation maximization (EM) algorithm, then with the increase of monitoring data, model parameters are updated by using STF algorithm, and the probability density function of remaining life is derived to predict the remaining life of oil‐paper insulation. Finally, the accuracy of the proposed method is verified by using furfural content as performance degradation index and accelerated thermal aging experimental data. Compared with the Bayesian–Wiener algorithm, the MSE of the proposed algorithm is reduced to 0.2138 and its MAPE is reduced more than 4%. Besides, the proposed method has the advantages of strong robustness and low uncertainty of residual life prediction. [ABSTRACT FROM AUTHOR]

In order to cope with the current engineering demand for increasing the current‐carrying capacity of resin impregnated paper (RIP) bushing, an optimization scheme of RIP bushing was proposed in this paper. Simulation and experimental studies were carried out to verify the rationality of the scheme. An improved simulation method was proposed. To ensure as much accuracy as possible, the physical model of simulation considers the influence of heat conduction, thermal convection and thermal radiation process according to the actual operation environment. To boost the computational efficiency, the improved method simplified the relationship between temperature and heating power of the current‐carrying conductor as a fitting formula to avoid the iterative calculation between electromagnetic field simulation and temperature field simulation. The temperature rise test was carried out according to the engineering requirement and the standard international electro technical commission (IEC) 60137:2008. The simulation and experimental results show that the RIP bushing can carry a current with the heating power of its conductor equivalent to that generated by a 50‐Hz current of 6736 A while running in a converter station. It is a breakthrough of the current‐carrying capacity of high voltage bushing. Meanwhile, the regularity of temperature distribution of the RIP bushing was discussed. [ABSTRACT FROM AUTHOR]

The introduction of solid dielectrics to electrode surfaces will change the motion behaviours of free metallic particles and in turn affect partial discharge (PD) characteristics in transformer oil. To estimate the influence, the metallic particles movement in flowing oil was compared between different cases with and without insulating paper covering electrodes here. When the electrodes were bare, particles executed up‐down movements repeatedly in the vertical direction. As the electrodes were paper‐covered, the particles would settle on the paper surface for a period of time. Simultaneously, the PD characteristics caused by metallic particles were measured. When the electrodes were bare, the discharges concentrated in 0–90° and 300–360° and the PD magnitude reached 390 pC. When the double electrodes were paper‐covered, the PD magnitude decreased by 67% and the concentration phases of PDs would migrate to 45° and 160°. In order to analyse the experimental results, a simulation model with a paper‐covered boundary was constructed. Simulated trajectories of particles were in accord with the experiment results. Base on the model, the particle motion characteristics were obtained and the electric field distribution between the particle and electrode was calculated. Finally, the influence of paper‐covering on PD characteristics was discussed. [ABSTRACT FROM AUTHOR]

The heterocharge accumulated in the oil‐paper insulation system of the converter transformer will enhance the local electric field and cause insulation deterioration. Numerical simulation is an effective method to analyze heterocharge. Currently, the numerical simulation of heterocharge is mainly based on the bipolar charge transport (BCT) model and the impurity ion transport (IIT) model. However, the effects of temperature, conductivity, and thickness of oil‐impregnated paper on charge transport have not been considered in these models. In view of this, a modified charge transport (MCT) model by introducing the Maxwell–Wagner model and heat conduction equation is proposed in this paper based on the BCT model and IIT model. Then, the influence of multi‐factors (temperature gradient, moisture content, and oil‐impregnated paper thickness) on heterocharge accumulation is investigated by using the MCT model. The simulation results obtained by the MCT model are more consistent with the experimental results than the BCT model, which proves the accuracy and superiority of the MCT model. Moreover, the increase in temperature gradient, moisture content, and oil‐impregnated paper thickness will enlarge the ion dissociation rate, conductivity, and trap density, and thus promote the generation of heterocharge. In that respect, the MCT model is expected to optimize the insulation design and provide a reference for space charge dynamic simulation. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

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 distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time. [ABSTRACT FROM AUTHOR]

The weak AC grid in a modular multilevel converter modular multilevel converter based high voltage direct current (MMC‐HVDC) receiving‐end system requires faster reactive power support from the receiving‐end MMC. However, under the MMC traditional constant AC voltage dual‐loop control strategy, the increase in MMC's reactive power support speed will reduce the system stability. The reactive power cascade control strategy is a novel MMC control strategy. The analysis in this paper demonstrates that it possesses advanced performance under faster reactive power support, which is required by the MMC connected to a weak AC grid, but, some of its control links and operations can cause impacts exceeding that a weak AC grid can undertake. To solve this problem, based on the original control strategy, this paper proposes an improved reactive power cascade control strategy for weak AC grid conditions. The complete framework and the detailed design of primary control links of the improved cascade control strategy are displayed in this paper. Finally, a digital simulation has been conducted to validate the analysis and the proposed improved control strategy in this paper. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It emphasizes the need to improve the reliability and resilience of modern power systems in the face of various threats. The editorial highlights the importance of modeling, assessing, and enhancing the reliability and resilience of power grids that are increasingly incorporating grid-edge resources. The document also provides a summary of the papers included in a special issue of the journal, which cover topics such as planning reliable energy systems, analyzing power grid operations, and enhancing service restoration and grid-support load response. Additionally, the text discusses the issue of equity in power distribution system restoration processes and proposes a model called the equity-aware power distribution system restoration model. The model aims to balance equity and resilience in restoring power after outages by focusing on fair distribution of energy resources while maintaining operational efficiency. The text also includes brief biographies of several authors who have contributed to the field of power electronics and power system reliability. [Extracted from the article]

With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

The coupling capacitance between the line and the transducer cannot be ignored when making non‐contact voltage measurements on low‐voltage three‐phase power lines. This leads to an increase in the number of unknown capacitances, and the number of correlatable response voltage equations is less than the number of unknown coupling capacitances. In this paper, a simplified decoupling method is proposed based on the characteristics of triangular three‐phase lines erected in distribution areas. Firstly, the equivalent circuit measured by the voltage sensing mechanism during three‐phase operation is established, and the three‐phase line response voltage equation is derived. Secondly, this paper proposes a three‐phase line response voltage decoupling algorithm after designing the decoupling matrix. Then, this paper uses the least squares method to solve the coefficients in the equations. Finally, the three‐phase voltage fluctuations of the actual low‐voltage stations are simulated by simulation. The measured sensor voltage values are substituted into the algorithm to invert and calculate the three‐phase line voltage. The three‐phase phase error is less than 2°. The amplitude error is less than 1.77%. The experiment verifies the stability and accuracy of the proposed decoupling method. [ABSTRACT FROM AUTHOR]

Simulation results on the modified IEEE 73-bus test system including wind farms generation prove the efficiency of the proposed algorithm as the impacts of energy storage system modules on the grid-scale system flexibility, investment plans, and power system economics. TOPIC B: SHORT-TERM GENERATION/LOAD FORECASTING OF IES Ge et al., in their paper "Short-Term Load Forecasting of Integrated Energy System Considering the Peak-Valley of Load Correlations" propose an integrated energy system (IES) short-term load forecasting method based on load-correlation peaks and valleys. The increasing necessity of tearing down the barriers between different energy-related disciplines and developing effective means to coordinate and integrate various energy systems has given rise to the concept of integrated energy system (IES), which have received great attention from multiple technical communities in recent years. TOPIC E: STATE ESTIMATION AND MODEL IDENTIFICATION/CALIBRATION OF IES Xu et al., in their paper "A Real-Time State Estimation Framework for Integrated Energy System Considering Measurement Delay" propose real-time state estimation framework for the gas-electricity coupled system. [Extracted from the article]

However, curtailment compensation costs eventually surpass upgrade costs, making network upgrades a practical option once the HC level reaches 118%, 106%, and 97% for rural, suburban, and urban regions, respectively. Keywords: distribution networks; renewable energy sources EN distribution networks renewable energy sources 3763 3764 2 09/05/23 20230901 NES 230901 Decarbonization policy leads to an increasing trend of integration of renewable energy sources (RESs) in distribution grids. [Extracted from the article]

This paper proposes an optimization approach for allocating power quality monitors (PQMs) aiming to monitor all harmonic resonance conditions while taking power system uncertainties into account. The placement approach utilizes the frequency scan response for calculating impedances over a range of frequencies and consequently determining harmonic resonance conditions. Thereby, it is capable of building binary matrices, which include harmonic resonance conditions. Also, by utilizing the union operator at binary matrices, power system uncertainties such as photovoltaic generation and load level can be considered in the allocation method. The placement approach is expressed as a linear problem that determines the best locations of PQMs and their optimal number so that they monitor all harmonic resonance conditions. Besides, by considering the area of harmonic pollution of non‐linear loads in the proposed method, owners of electrical networks can find a solution with fewer PQMs to monitor harmonic resonance orders inside a particular area of the network. The performance of the presented approach is demonstrated using the 15‐node distribution network and a real electrical network, as well as a real large electrical network in Iran. Results show that the proposed method suggests fewer PQMs to monitor harmonic resonance conditions compared to previous methods. [ABSTRACT FROM AUTHOR]

The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two‐fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year‐long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the major development directions of large‐scale photovoltaic (PV) power system. In order to achieve low‐cost, high‐efficiency and long‐distance transmission of PV power, this paper adopted a DC grid‐connected topology by using multi‐modular cascaded DC‐DC converters, forming an input‐independent and output‐series (IIOS) system. In this system, an LLC type DC‐DC converter based on IGCT and magnetically integrated transformer was proposed in this paper, which can significantly increase the capacity, efficiency and power density of DC‐DC converter. And the equalization unit based on half‐bridge topology was adopted for the case of uneven irradiation of the PV inputs. In addition, this paper proposed an equalization control method based on the Nearest Level Modulation (NLM), which greatly simplifies the design of the acquisition and control system. Furthermore, the parametric design of a 50MW PV system was presented, and the simulation was carried out to verify the feasibility and advantages of the system in this paper. The proposed IIOS system is more suitable for high‐capacity, long‐distance PV power transmission, which can serve as a reference for the development of large‐scale photovoltaic systems. [ABSTRACT FROM AUTHOR]

One of the main reasons for the failure of transformer bushings is moisture penetration. Here, an online monitoring method of moisture in transformer bushings based on an improved humidity indicator card, which belongs to the group of passive physical and chemical monitoring methods, is proposed. First, the operating principles and monitoring system are explained. Next, by means of a moisture simulation device, the moisture migration characteristics between the gas and oil, the influence of the interface radius on the response characteristics of the monitoring device, and the characteristics of the humidity indicator card are studied experimentally. Then, the moisture migration process in micro‐scale is simulated by molecular dynamics simulation. Finally, by changing the mass fraction of the cobalt chloride in the humidity card, the warning values of relative humidity at different temperatures are obtained. The simulation results show that water molecules can migrate from the oil–paper system to the air. Moreover, once the moisture enters the transformer bushing, the proposed humidity indicator card can correctly display the relative humidity at different temperatures. In conclusion, the proposed method provides a new way for online moisture monitoring of transformer bushings. [ABSTRACT FROM AUTHOR]

The inherent constraints of grid‐forming converters affect their ability to maintain voltage source characteristics within a specific operational range. In response to these limitations, ongoing pilot projects involving grid‐forming technology have adjusted their current thresholds up to three times the rated current, significantly exceeding the traditionally threshold of 1.2 times the rated current. This paper aims to investigate the effects of current thresholds on the dynamic responses of grid‐forming converters under large disturbances, with the intention of identifying an optimal current threshold that meets the grid's demands for maintaining voltage source characteristics and overall stability. A novel power angle relationship is introduced, integrating current limits and anti‐windup voltage controls into the equivalent circuit models of these converters. Analytical expressions for stable and unstable equilibrium points are derived, improving insights into the converters' dynamic behaviour and their resilience to phase jumps across various current thresholds and short‐circuit ratios. Findings indicate that higher current thresholds substantially improve phase jump limits and withstand capabilities. The paper concludes with proposed configurations and optimization strategies for current thresholds, all supported by experimental validation. [ABSTRACT FROM AUTHOR]

This paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models. [ABSTRACT FROM AUTHOR]

Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder. [ABSTRACT FROM AUTHOR]

The development of renewable energy will increase the demand for flexible resources in power systems due to the strong uncertainties. To allocate resources and cope with these uncertainties, it is beneficial to apply robust coordinated energy storage and transmission expansion planning (ES&TEP). The large candidate line set can significantly increase the computational complexity of robust optimization models. However, there is currently no suitable network search space reduction (NSSR) method to address this problem. This paper designs a NSSR method based on redundancy constraint identification that only requires information on component power ranges rather than fixed power curves, which can be well adapted to robust optimization. Additionally, this method requires only basic numerical calculations and ensures convergence within a finite number of iterations. The classical stochastic robust ES&TEP model and the column and constraint generation (C&CG) algorithm are used to validate the effectiveness of the NSSR method. In the case studies, the numerical results confirm the rationality and effectiveness of the approach proposed in this paper. By selecting appropriate parameters, the candidate line set can be reduced by over 70% and the computational efficiency can be improved by an order of magnitude. [ABSTRACT FROM AUTHOR]

The Fractional Frequency Transmission System (FFTS) operates with low frequency to decrease the electrical length of AC transmission lines, consequently enhancing transmission capacity. The integration of renewable energy sources via FFTS will significantly impact power system planning. This paper presents a Fractional Frequency Transmission Expansion Planning (FF‐TEP) model, which considers the interaction between FFTS and the industrial‐frequency power grid. This model addresses the challenges posed by the integration of offshore wind power through FFTS, and takes several key factors into account, including the active power coupling of Modular Multilevel Matrix Converter (M3C), inverter capacity, and converter transformer capacity. To address the non‐convexity caused by AC power flow, this paper introduces a Genetic‐Particle Swarm Optimization algorithm (G‐PSO) with dynamic penalties tailored to the M3C structure. This approach incorporates both crossover and mutation operators from Genetic Algorithms (GA) into Particle Swarm Optimization (PSO). The penalty function dynamically adapts according to the M3C structure, resulting in improved global optimization while maintaining computational efficiency. The results of two case studies demonstrate that FFTS can significantly enhance transmission capacity while reducing cable charging power. This reduces investment costs and decreases voltage offset issues. [ABSTRACT FROM AUTHOR]

Bird's nest defect is an important cause of transmission line faults. To achieve accurate detection of bird nest defects in complex scenarios, a bird nest defect detection model for transmission lines was proposed that combines domain knowledge and occlusion reasoning networks. On the one hand, the model utilized the domain knowledge of the location of the bird's nest, using edge detection to obtain tower area information to constrain the location of candidate frames. This helps to reduce the false detection caused by complex backgrounds. On the other hand, on the basis of analyzing the occlusion characteristics of bird nests, the model employed occlusion reasoning networks that randomly erase features at the feature level to simulate the occlusion of bird nests in real scenes and improve the model's detection capability for occluded targets. Additionally, a multi‐scale feature fusion algorithm was designed in this paper to adapt the model to the scale variations of bird nests in aerial images. Experimental results demonstrate that the model outperforms advanced target detection models and other bird nest defect detection methods, with an AP50 of 78.8% and an AR10 of 72.4% for defect detection. [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]

The present paper simulates cyberattacks in DIgSILENT for energy management and state estimation. Assuming hackers have adequate information to alter input data, they can alter the power market and the available transmission capability (ATC). Detection programs may fail if false information is coordinated in the presence of error information. The minimum number of network measurements before the state estimation program converges is 54% of the total measurements. In the new method, weighted least squares improve the state estimation speed. The new method can also improve the speed of ATC. In addition to identifying the cyberattack, the algorithm in the present paper can be used to identify the target of the attack. It also uses complete and correct information and even incorrect measurements to report state estimator errors. The present paper shows that ATC values follow the inverse pattern of load and loss. A real sample network is presented at the end of the paper to test the suggested method. [ABSTRACT FROM AUTHOR]

In the context of extremely uneven sources and loads in China, several large‐scale power transmission systems have been established. Affected by the small equivalent impedance of the system, the increase of negative sequence currents during open‐phase operation has seriously threatened the stable operation of synchronous generators. The necessity of negative sequence current suppression is particularly prominent. To address the above issues, this paper analyzes the feasible negative sequence suppression capacity of the grid‐connected inverter under the principle of positive sequence priority. Using the geometric analysis method to determine the optimal suppression capacity. The none‐phase‐locked loop (PLL) control method is used to improve the response speed and form the negative sequence current suppression strategy adapted to the open‐phase operation period. A simulation model was built in PSCAD to verify the effectiveness of the proposed strategy. The result shows that the suppression strategy has a faster response than the conventional PLL control. In scenarios with large negative sequence currents to be suppressed, a maximum suppression effect of approximately 63% can be achieved. Compared with the traditional suppression capacity selection, the strategy in this paper can reduce the residual of negative sequence current by about 10% at most. The negative sequence currents intruding into the generator can be effectively suppressed without additional devices. [ABSTRACT FROM AUTHOR]

In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability. [ABSTRACT FROM AUTHOR]

Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time‐frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves. [ABSTRACT FROM AUTHOR]

Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply. [ABSTRACT FROM AUTHOR]

The nearest level modulation (NLM) is used to automatically generate ac side voltages of sub‐modules in modular multilevel converter (MMC). Existing literature seldom presents models that describe complex NLM processes. This paper proposes an equivalent model of the NLM to simply describe and simulate the complex NLM process. The main contribution of this study is to build the model of NLM with fast simulation speed for the application focuses on the simulation accuracy at the millisecond level. The duty cycle of the average model generated by the NLM in sub‐modules of the MMC is divided into two components, which are the stable component for generating the bridge arm voltage and the fluctuation component for balancing the dc side voltage of sub‐modules, respectively. The stable component is generated by the modulation wave of the MMC control system. By adding a voltage control closed loop to the sub‐module, the fluctuation component for balancing the dc side voltages of sub‐modules can automatically adjust the ac side voltages of sub‐modules. The proposed equivalent model of NLM improves the simulation speed and has the same effect with the traditional NLM within the low‐frequency range or with the time step of millisecond level. [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]

Multi‐terminal high‐voltage direct current technology based on voltage‐source converter stations (VSC‐MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small‐signal stability analysis. The paper analyses two control strategies based on the modulation of active‐power injections (P‐WAF) and reactive‐power injections (Q‐WAF) in the VSC stations which were presented in previous work. Both control strategies use global signals of the frequencies of the VSC‐MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both large‐ and small‐disturbance angle stability. Small‐signal stability analysis (in Matlab) has been compared with non‐linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC‐MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical‐oscillation damping. The main conclusion of the paper is that transient‐stability‐tailored supplementary controllers in VSC‐MTDC systems can be tuned to damp inter‐area oscillations too, maintaining their effectiveness. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) is a good solution for stabilizing the power system with high penetration of renewable energy. However, in case of serious unbalanced voltage disturbance/fault, the conventional VSG may lose voltage, inertia, and damping support characteristics to the grid and even can cause disconnection of renewable energy. This paper proposes an improved fault control strategy for VSG with the coordination of STATCOM. The proposed method can provide sufficient voltage support while keeping continuous system inertia and damping support under severe unbalanced fault. In the paper, an improved VSG and STATCOM control topology based on positive and negative sequence current control are first proposed so as to keep the damping and inertia support to grid during the grid fault. Secondly, the voltage support control method for the VSG with improved topology during unbalanced fault is introduced, which can achieve multiple control objectives, in terms of voltage support, current limitation, and active power output simultaneously. Then a coordination control scheme of improved VSG and STATCOM is developed so as to optimize the maximum control objectives in all possible scenarios, especially in the case of severe unbalanced fault. Finally, the effectiveness of the method is verified by using the MATLAB/SIMULINK simulation platform. [ABSTRACT FROM AUTHOR]

In recent years, the interest in using DC microgrids has greatly increased due to their higher efficiency, less complexity, and greater transmission power compared to AC microgrids. To address challenges in DC microgrids in the presence of electrical vehicles (EVs) and the uncertainty of charging EVs, researchers have used PV/EV combination systems with energy storage systems (ESS). Controlling DC microgrids, including PV/EV/ESS, is crucial to cope with the existing challenges. On the other hand, the research on DC microgrids' protection systems is in the early stages, and there are still many challenges in this field. In addition, differences in control systems can pose different challenges for the protection system. The simulation results in this paper demonstrate that considering the best case (use of unipolar resistance grounding system) the DC bus voltage is improved by 22.3% (based on MAPE% data comparison). In the protection part, the empirical‐mode decomposition (EMD) method and selecting a suitable intrinsic mode functions (IMF) have been used to protect the DC microgrid. The proposed protection method has been tested under pole‐to‐pole and pole‐to‐ground fault conditions. The results show that the proposed method is capable of detecting various fault types in the studied microgrid. [ABSTRACT FROM AUTHOR]

The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. [ABSTRACT FROM AUTHOR]

In addition to providing mobility, plug‐in hybrid electric vehicles (PHEVs) provide a two‐sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all. [ABSTRACT FROM AUTHOR]

In this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non‐parametric kernel‐based learning method having the ability to provide correct predictions with uncertainty in measurements. The proposed model provides an hourly and monthly load forecast for an Australian city and four Indian cities in the Maharashtra state. Twelve GPR models are trained with historical datasets including hourly load and environmental data. To evaluate the trained model, the actual and predicted load demand curve is plotted and mean average percentage error (MAPE) is calculated corresponding to different kernel functions of the GPR model. To the best of the author's knowledge, the prediction of load demand using GPR for Indian cities of Maharashtra state has been made for the first time. The calculated MAPE in LF is 0.15% for Australia and 0.002%, 0.209%, 0.077%, and 0.140% for Indian cities viz. Nasik, Bhusawal, Kolhapur, and Aurangabad, respectively. The test results illustrate that minimum MAPE in load prediction is obtained using the proposed model that is GPR with 'Exponential' kernel functions. Furthermore, the comparative analysis with the existing approaches confirms the dominance of the proposed model. [ABSTRACT FROM AUTHOR]

Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction. [ABSTRACT FROM AUTHOR]

Since compact high temperature superconducting (HTS) transformer has a small size and high efficiency, it can meet the requirements of high‐voltage transformers for vehicle‐mounted mobile substation. However, due to the small internal insulation size of the compact HTS transformers, it is necessary to improve the insulation strength to ensure the insulation safety of the transformer. Reasonable main insulation structure can greatly improve the insulation level of transformer. Based on the design theory of main insulation structure, this paper designs a main insulation structure model "small liquid nitrogen gap—barrier" suitable for compact HTS transformer. The insulation withstand voltage experiment of the main insulation structure shows that when the liquid nitrogen gap distance is less than 8 mm, the insulation strength of liquid nitrogen in the structure is improved. When the liquid nitrogen gap distance is too small, the size and thickness of the barrier will affect the insulation strength of the structure. According to the insulation strength of main insulation structures with different liquid nitrogen gaps, the feasibility of "small liquid nitrogen gap—barrier" main insulation structure is verified. The small liquid nitrogen gap structure can be adopted for the main insulation of compact HTS transformer. [ABSTRACT FROM AUTHOR]

State estimation is critical for railway power supply systems (RPSSs). Pseudo‐measurement is commonly used in state estimation. However, the fluctuations of renewable generations and railway traction loads in RPSS may introduce data noise, which will jeopardize the accuracy of the generated pseudo‐measurements and thus impact the state estimation. Additionally, when learning the historical measurement data sequences, the traditional pseudo‐measurement model is likely to have overfitting, which will further impact the accuracy of pseudo‐measurements, thereby affecting the accuracy of state estimation. To address these issues, this paper proposes a high‐accuracy pseudo‐measurement‐based state estimation approach for RPSSs. Firstly, a denoising autoencoder‐based method is used to mitigate the impact of data noise on the accuracy of pseudo measurements, and a gated recurrent unit‐based method is used to adaptively learn the historical measurement data sequence, thereby improving the accuracy of pseudo measurements. Next, the pseudo‐measurement weights are obtained by generating pseudo‐measurement variances using the Gaussian mixture model. Finally, the pseudo measurements and real‐time measurements are integrated by weighted least squares to realize the state estimation of RPSS. The effectiveness and accuracy of the proposed method are verified by simulation on a modified IEEE 33‐node system which includes a railway traction substation and renewable generations. [ABSTRACT FROM AUTHOR]