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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]

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]

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]

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]

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]

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]

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 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]

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]

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]

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method. [ABSTRACT FROM AUTHOR]

During the operation of the ITER machine, hundreds of MW/Var of active and reactive power will be exchanged with the grid. The E‐STATCOM scheme composed of the Modular Multilevel Converter (MMC) and split supercapacitor energy storage has been proposed to improve the power compensation performance of the existing reactive power compensation system in the previous study. However, one of the main technical challenges which is lack of research is to precharge all submodule capacitors and supercapacitors from zero to their nominal voltage values efficiently during a startup process. As the capacitance and operating voltage of supercapacitors are much different from capacitors in each submodule, the startup of E‐STATCOM is a more complicated process. To coordinate the energy exchange between submodule capacitors and supercapacitors, submodule capacitors and the grid, this paper presents an optimized four‐stage AC side startup strategy for the E‐STATCOM. The proposed method minimizes the use of current‐limiting resistors while suppressing the surge current in the zero‐voltage startup process of supercapacitors, in addition to optimizing the energy consumption. The pulse current charging, constant current charging and constant power charging strategies of supercapacitors are adopted in different charging stages, and the detailed coordinated control scheme between submodule capacitors and supercapacitors are described and analyzed. The effectiveness and performance of the proposed method are verified by simulation results and hardware‐in‐the‐loop (HIL) experiments. [ABSTRACT FROM AUTHOR]

In order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the environment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self‐parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20–50 km. In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without a power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering. [ABSTRACT FROM AUTHOR]

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

Most of the existing methods for harmonic analysis are from frequency‐domain perspective. In fact, the essential factor for generating harmonics is non‐linear characteristic or time‐varying property. Therefore, the harmonic generation mechanism and the harmonic source location method are investigated from the time domain perspective in this paper. Firstly, a general model of non‐harmonic sources is established. The non‐harmonic sources will match well with the proposed general model while the harmonic source will not. Then correlation coefficient that reflects compliance degree between each device and the general model is defined for distinguishing harmonic sources from non‐harmonic sources. On this basis, a novel time‐domain harmonic source location method is proposed. Numerous simulations and experiments have demonstrated that the proposed method has good performance under fluctuations, saturations, pre‐distortions, transient process, and resonances. The defined correlation coefficient can reflect non‐linearity/time‐varying degree of the harmonic source, which can be used for evaluating harmonic emission level of each harmonic source. On this basis, a simple harmonic responsibility division method is proposed, which is immune to possible fluctuations, pre‐distortions, saturations, resonances, and impedance variation. [ABSTRACT FROM AUTHOR]

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

This paper presents various control strategies to improve operations in two interconnected areas connected by a VSC‐HVDC transmission line. The main focus is on designing a central control system (CCS) that coordinates control units in both areas. In area 1, an AC voltage control unit is connected to the CCS. In area 2, three control units including a load power control unit, a fault detection unit, and an AC voltage control unit are also connected to the CCS. The CCS receives inputs from these units and generates commands for the DC voltage and active/reactive power control units on both sides of the DC line. The first proposed strategy addresses permanent voltage drops caused by load fluctuations in area 2. It adjusts the transmitted power from area 1 based on voltage variations in area 2. The second strategy focuses on mitigating faults in area 2 by injecting active and reactive power from area 1 during such events. The third strategy resolves transient voltage oscillations in both areas by controlling the reactive power of stations on either side of the DC line. Simulations using MATLAB‐SIMULINK demonstrate that these mechanisms successfully achieve their objectives. [ABSTRACT FROM AUTHOR]

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]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability. [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]

The paper presents a new method for estimating the contribution of distortion sources based on the application of passive harmonic filters. The method does not require the measurement of harmonic network impedance and is based on the measurement of harmonic currents of the grid, consumers and passive harmonic filters. Evaluation of the consumer contribution using a passive harmonic filter is necessary to select the parameters and connection points for harmonic reduction devices. A feature of the method is the correct determination of share contributions, regardless of background harmonic distortions. Based on this method, a single consumer can evaluate both the harmonic contributions of its own loads, even in the presence of background distortions, and the harmonic voltage distortions in the case of installing harmonic reduction devices. The research results are confirmed in laboratory conditions with various combinations of electrical loads connected both at the grid side and at the consumer side. For such conditions, the proposed method was compared with existing methods, among which are methods based on the measurement of harmonic voltage vectors, harmonic current vectors and active harmonic power. The application of the developed method was demonstrated using the example of a gas production field. [ABSTRACT FROM AUTHOR]

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

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost. [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]

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]

In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%. [ABSTRACT FROM AUTHOR]

Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle. [ABSTRACT FROM AUTHOR]

Increasing the penetration level of distributed generation (DG) units requires overcoming the technical challenges associated with their integration into the distribution systems, especially protection problems. Change in the current profile of the distribution system due to the presence of DG units disrupts the operation of the conventional fuse saving coordination (FSC) scheme. The first objective of this paper is to provide an overview of the state‐of‐the‐art of FSC schemes in distribution systems with distributed generators that has not been systematically presented yet. In addition to comparing the features of reliability, cost, speed, implementation, calculation burden, and requirements, the impact of presence of distributed generations on the performance of the conventional FSC scheme is investigated in details. The second objective of this paper is to propose an FSC restoration scheme for minimizing the challenges of previous works. Using a quasi‐voltage current term, the proposed scheme modifies the adjustable time coefficient of the recloser in two ways of pro and plus. The former scheme provides an approximate FSC with a simple setting while the latter scheme provides complete coordination at the expense of a more complex setting. No need for voltage measurement makes its implementation practical in available distribution systems. The effective performance of the proposed FSC scheme is verified through extensive simulation studies in the ETAP environment. [ABSTRACT FROM AUTHOR]

Stability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk. [ABSTRACT FROM AUTHOR]

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]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network. [ABSTRACT FROM AUTHOR]

State estimation stands as one of the most crucial applications in energy management systems of control centres, ensuring the secure operation of the power system. The real‐time availability of accurate system variables, including voltages, currents, and power flows, presents an excellent opportunity to consider the actual network characteristics, including imbalances. This paper presents a linear and non‐iterative method to solve the three‐phase state estimation problem in transmission systems with unbalanced loading. The method utilises supervisory control and data acquisition measurements obtained from remote terminal units. The formulation is based on complex variables derived from traditional supervisory control and data acquisition measurements, with assumptions made regarding the availability of certain three‐phase bus voltages, as well as a number of three‐phase currents and active and reactive power flows. The proposed algorithm employs the classical weighted least squares technique for solving the three‐phase state estimation problem. Due to its straightforward and linear nature, the method ensures no convergence issues. To validate its effectiveness, the proposed method is validated on a three‐bus network and the IEEE 39‐bus network simulated using the three‐phase model within the DIgSILENT Power Factory environment. [ABSTRACT FROM AUTHOR]

This paper proposes an improved expanding interior algorithm (EIA) to estimate the region of attraction (ROA) of power systems with wind power generation based on sum of squares (SOS) programming. An ordinary differential equation (ODE) model is derived for the doubly‐fed induction generator‐based wind turbine (DFIGWT), which is named as an enhanced synchronous‐generator‐mimicking (ESGM) model. The ESGM model bridges the gap between the requirement of an ODE model in ROA estimation and the conventional differential‐algebraic equation (DAE) model of the DFIGWT system. The ESGM model is able to accurately reflect the low frequency dynamics of the DFIGWT. Moreover, an improved EIA is designed to estimate the ROA based on SOS programming, which has higher efficiency than the existing ROA estimation algorithms based on SOS programming. It is able to adaptively search for the Lyapunov function and obtain an optimal estimation of the ROA in an iterative process. The accuracy and efficiency of this algorithm are verified in three test systems composed of DFIGWTs and synchronous generators (SGs). The morphological changes in the ROA of the test systems caused by the penetration of DFIGWT are examined. [ABSTRACT FROM AUTHOR]

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability. [ABSTRACT FROM AUTHOR]

Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the "up‐pyramid" and "down‐pyramid" model of ANN into a "custom‐pyramid" model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases. [ABSTRACT FROM AUTHOR]

Autonomous microgrid is known to lack appropriate inertia and damping for grid stabilization. Due to this, virtual synchronous machine (VISMA) has been introduced to provide necessary ancillary services through control of power converters. In a multi‐VISMA (n‐VISMA) microgrid, relative rotor angle stability of the power system is dependent on the active power balance after small perturbation. Thus, the use of relevant analytical models are essential issues for microgrid stability analysis. This paper presents a comprehensive small‐signal stability analysis to study inherent electromechanical oscillations in the virtual rotors. The subsystems of the microgrid consisting of VISMA, network, load and the outer power control were all modelled in Synchronous Reference Frame. The small‐signal model (SSM) was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines on the network. To validate the developed numerical analytics, dynamic responses of the SSM are compared with those of the non‐linear (NL) system dynamics and the results reveal that the developed linearized SSM is sufficient to accurately characterize behaviour of the VISMA microgrid when operated in autonomous mode. Eigenvalues analysis and parameter sensitivities of the critical modes were investigated. Oscillatory participations of the VISMAs and steady state stability limit of the microgrid have also been investigated. [ABSTRACT FROM AUTHOR]

Harmonic resonance is caused by harmonic source matching resonance frequency and presents the characteristics of decentralization and whole network in modern grids. The current resonance suppression methods mainly focus on reducing the harmonic source, and are suitable for single‐site and small‐scale grids. This paper presents a new method for suppressing parallel resonance at system level. The holistic harmonic resonance is suppressed by reducing the harmonic impedance. In addition, new resonances are prevented by weakening the coupling between resonance and non‐resonance frequencies. The resonance modal analysis and resonance frequency shift (RFS) are employed to optimize harmonic impedance. Simulation results indicate that the capacitance parameter has higher sensitivity and is theoretically more suitable for RFS. Considering the accuracy of modal frequency sensitivity, a variable‐limit genetic algorithm for iteration is proposed. The validity of the proposed method was verified using an IEEE 14‐bus test network. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs. [ABSTRACT FROM AUTHOR]

Due to the increasing share of inverter‐based renewable energy sources and the increased retrofitting of conventional hydropower plants with power electronics, novel possibilities arise for providing damping power in case of occurring inter‐area oscillations in power systems. This paper presents a complete model of a variable‐speed hydropower plant (VSHP) in a single‐machine infinite bus system. The demonstrated analytical model is applied for small‐signal stability analysis that contains all relevant converter dynamics and is verified by an additional non‐linear time‐domain simulation. The damping controller approach located in the full‐size converter (FSC) consists of the classical power system stabilizer with additional phase compensation, to which an appropriate wide‐area input signal is fed via a phasor measurement unit device. Subsequently, the FSC‐VSHP with additional damping algorithm is examined with respect to the significantly higher damping performance with regard to inter‐area modes in a three‐machine model. [ABSTRACT FROM AUTHOR]

The linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real‐time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities. [ABSTRACT FROM AUTHOR]