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The stable operation of transformers is an important guarantee of power system security. The temperature distribution of main insulation is different in long‐term operation. The temperature difference between the local hottest spot temperature and the average oil temperature in the transformer exceeds 30 K. Therefore, the oil‐paper insulation inside the transformer will exhibit non‐uniform ageing after long‐term operation. At present, frequency domain dielectric response (FDS) technology is widely used in transformer ageing assessment. However, the existing assessment methods are only used for overall insulation ageing assessment. The average moisture content and polymerization degree of the overall insulation between each phase winding are obtained. But it still cannot be used to assess the non‐uniform ageing of the transformer's main insulation. The radial, axial, and two directions non‐uniform ageing models have been made in the laboratory in this study. The FDS and physicochemical properties of non‐uniform ageing models were tested. The variation law of FDS for non‐uniform ageing models was analysed. Meanwhile, the dielectric relaxation model of non‐uniform ageing for main insulation was established. The method of frequency division iterative calculation was proposed. Combined with the Fessler moisture balance formula of ageing oil‐paper insulation, the quantitative assessment method for ageing parameters of the non‐uniform ageing model can be realized. Compared the calculated results with the chemical analysis results, the relative error of the polymerization degree calculation is less than 4%, and the error of the moisture content calculation is less than 0.3%. The calculation results can meet the needs of the engineering apply. Therefore, this method can realize the quantitative assessment of transformer main insulation ageing, which is of great significance to assess the transformer insulation state. [ABSTRACT FROM AUTHOR]

Abstract: Occluded gaseous cavities within the insulating materials are potential sources of electrical trees which can lead to continuous deterioration and breakdown of materials. To determine the suitability of use and to acquire the data for the dimensioning of electrical insulation systems breakdown voltage of insulators should be determined. In this paper, fuzzy logic (FL) method is used to model breakdown voltages of White Minilex Paper samples based on experimental data generated in the laboratory. Different models are proposed with different shapes of the membership functions for the FL under both dc and ac voltage conditions. The cavities are created artificially with different dimensions. Low values of mean absolute errors of the estimated breakdown voltage of the test data show the effectiveness of such models. [Copyright &y& Elsevier]

Power capacitors are highly reliable due to their completely enclosed configuration. They are relatively trouble-free over the long term. However, their dielectric strength can be reduced due to some stresses, including overvoltage application or thermal degradation of the insulation material during long periods of operation; these events can result in dielectric breakdown of capacitors in extreme cases. Since almost all power capacitors with oil-impregnated paper have been operated for more than twenty years, some kind of failure should be expected. To prevent these incipient accidents, it is important to examine the insulation deterioration mechanism and evaluate the degree of deterioration before capacitor breakdown. To establish a diagnostic method of deterioration of the capacitors with oil-impregnated paper, we have conducted thermally and electrically accelerated deterioration tests and investigated the changes of their characteristics. Based on the experimental results we obtained, it can be concluded that the behavior of the oil-dissolved gases can be used as a useful index to diagnose the insulator deterioration of these capacitors. [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]

This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system's resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow. [ABSTRACT FROM AUTHOR]

Dielectric testing techniques, in both time and frequency domains, are currently widely used by power utilities for assessment of the condition of transformer oil-paper insulation systems. However, it has been reported that results of these tests are highly influenced by the operating temperature during measurements. The distribution, migration and equilibrium of moisture between oil and paper in a complicated insulation system is highly temperature dependent. It requires adequate experience and proper understanding to interpret the dielectric response results in the presence of temperature variations and thermal instability. Proper analysis of the dielectric test result is only possible with an understanding of the physical behavior of the insulation system in response to temperature. A circuit model, which describes the dielectric behavior of the transformers main insulation system, has been investigated in this paper. The values of the parameters of the model have been identified from the dielectric tests. A correlation has been observed between the operating temperature and the equivalent model parameters that can be used as additional information for better interpretation of the dielectric test results. This paper thus reports a detailed study on the effects of temperature on dielectric measurements of a transformer under controlled laboratory conditions. Some results of practical on-site testing are also presented to demonstrate the possibility of errors that may be introduced in dielectric test results analysis unless temperature effects are taken into consideration. [ABSTRACT FROM AUTHOR]

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply. [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]

The article mentions an overview of the power transformers as vital elements in a power system. Several well-known preventive maintenance techniques such as dissolved gas analysis (DGA), thermal monitoring, partial discharge measurement, dielectric response measurements and frequency response analysis help in upholding reliability of aging transformer population and determining a particular type of problem. A bibliographical survey of literature for electrical academic research is also presented including "Breakdown Initiating Mechanisms at Electrode Interfaces in Liquids," by T. J. Lewis, "Diagnosis of Power Transformer Insulation," by E. Gockenbach, and " Effect of Temperature on Sensitivity of Diagnostic Tests on Oil-Impregnated Paper Insulation," by R. J. Densley and B. K. Gupta.

Harmonic state estimation (HSE) is an useful technique for determining harmonic voltages and currents in electric power systems based on synchronized measurements and network impedances parameters. However, measurements and parameters data can be corrupted by gross errors yielding unsatisfactory estimation results. Despite recent methods have been proposed for HSE in power systems, there are few works considering both harmonic state variables and parameters estimation. In order to solve this problem, this paper presents a generalized harmonic state estimation considering uncertain network parameters and synchronized measurements. In the proposed approach, harmonic voltage and current phasors are obtained by measurement units allocated along the system. Network parameters, such as line and load impedances, are treated as inequality constraints in an optimization problem formulation considering both system state variables and parameters to be determined by the proposed HSE. The main contribution of this research paper is the use of a deterministic approach for estimating both harmonic state variables and parameters with reduced estimation errors when compared with the traditional weighted least squares (WLS) technique. Computational simulations are carried out using the IEEE test systems and results are also validated using RSCAD (Real-Time Simulation Software Package) software. [ABSTRACT FROM AUTHOR]

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With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint. Second, a convolutional neural network (CNN) combined with an attentionmechanism is proposed for training performance improvement. At last, an experiment is carried out to evaluate the estimation performance. Compared with the support vector machine method, CNN method and long short-term memory method, the proposed algorithm has the best load estimation performance. [ABSTRACT FROM AUTHOR]

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical and regulatory constraints, EV owners must participate in electricity mar-kets via aggregators. This paper proposes a robust optimal coordinated charging (OCC) model including bidding ancillary ser-vices for regulation and spinning reserve markets. The presented work handles the uncertain behavior of the electricity market that are ancillary service prices and their deployment signals by the robust optimization approach. The aim of optimization is the maximization of the aggregator's profits from V2G by joining the ancillary services markets. The recommended robust OCC model which is a robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. An assumed set of 10000 EVs in the electric reliability council of Texas (ERCOT) electricity markets is considered for doing simulations. Employing the presented model in this test system shows the efficacy of the proposed model in comparison to other deterministic and stochastic models. [ABSTRACT FROM AUTHOR]

This paper is based on modifications implemented in the protection and grounding systems of a large paper mill and describes selective ground fault protection for a 13.8-kV system with multiple bus-connected generators, synchronous bus ties, and utility interconnections. The ground fault current in the system is reduced from the existing 3400 A to 500 A, and a hybrid grounding system is implemented for each of the generators. As the ground fault currents are reduced to limit the fault damage, the sensitivity and selectivity of the ground fault protection become important. Directional ground fault relays with coordinating pickup settings are applied to achieve this objective. The new platform for directional elements (numerical relays) drives its performance from sequence impedance measurements. [ABSTRACT FROM AUTHOR]

Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs. [ABSTRACT FROM AUTHOR]

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper. [ABSTRACT FROM AUTHOR]

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

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode. Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line. Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets. [ABSTRACT FROM AUTHOR]

This paper presents a literature survey on various issues related to Electric Vehicles (EV) from an Electric Power Systems (EPS) perspective. For the past few years, the EPS has been transforming exceptionally, necessitating a substantial change in the network configuration, operation, and control of conventional power systems. Especially, distribution networks have undergone a significant transformation. Moreover, new loads like Electric Vehicles are going to be integrated with the electrical grid, and these EV loads will modify the consumption of electrical energy. Electric Vehicles will have a great impact on the distribution network because of their nonlinear behavior. At present, most of the existing EVs are connected to the residential distribution network for charging, which usually increases the demand for residential power. Consequently, unplanned charging of a huge number of EVs will result in an excessive energy demand that will result in several issues, such as power outages, voltage fluctuations, thermal stress on the lines, and harmonic pollution. [ABSTRACT FROM AUTHOR]

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend. [ABSTRACT FROM AUTHOR]

The main intention of Bidding strategies is to run the system in free and fair manner with desired quantity of power to the consumer's at most economical price through safe, secure and reliable operation of the power system. Bidding arrangement is properly designed in every country to take care of other abuse of market; these arrangements are kept on changing from time to time depending on the requirement for transparent and non-discriminatory electricity market. In this paper Bidding Strategy of Indian Power Markets, important Key Issues, Electricity Act's and Challenges in this field are analyzed and also explain about calculation of MCP (Rs/MWh) and Volume (MW) with the help of IEX on-line data. [ABSTRACT FROM AUTHOR]

Cellulose pressboard is used as a key element in various power equipment insulation structures and determines the operational safety of electric equipment and power systems. This paper aims to increase the application of a pressboard aging assessment method based on frequency domain spectroscopy, a promising nondestructive method for power equipment diagnosis, through in-depth theoretical analysis and accurate data fitting. To achieve this purpose, first, based on classic theoretical models in the field of dielectric physics, a mathematical deduction is proposed to separate each dielectric process in the overlapping frequency band. Second, a novel analysis approach that considers both the pressboard dielectric response and the thermally stimulated current is proposed and demonstrated to be effective in determining the physical meaning of pressboard microscopic dielectric processes. Third, a function model based on a specific microscopic mechanism is established to fit the frequency domain spectra of pressboard samples with different aging degrees. Based on this approach, the variations in charge carrier motion characteristics in the frequency domain with increasing pressboard aging degree are clarified. Moreover, the function model provides characteristic parameters that are clearly related to the degree of polymerization of the material, which represents a quantitative method for evaluating the aging degree of cellulose pressboard by indirectly obtaining its frequency response curve. [ABSTRACT FROM AUTHOR]