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Frequency domain spectroscopy (FDS) is employed to evaluate the moisture content and aging state of oil-paper insulation in power transformers. Oil-paper insulation samples with different moisture content and aging states were primarily prepared in the laboratory. A method was proposed based on a study of the influence of the aging state and moisture content on the frequency domain dielectric property of oil-paper insulation. This method distinguishes the influence of moisture content and aging state on FDS through the imaginary part of complex relative permittivity ( εr″). Relevant characteristic parameters that characterize the moisture content and aging state of oil-impregnated pressboards were also extracted. Results reveal that the extracted characteristic parameters are sensitive to moisture content in the range of 10−1 Hz to 101 Hz and in the range of 10−3 Hz to 10−1 Hz for the aging state. An obvious exponential relationship exists between the maximum values of the characteristic parameters in the given frequency ranges and degree of polymerization and moisture content. The new method can be utilized for a non-destructive condition assessment of oil-paper insulation of transformers. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller. [ABSTRACT FROM AUTHOR]

Despite there are significant advancements in modern power systems, blackouts remain a potential risk, necessitating efficient restoration strategies. This paper introduces an innovative concept for power system restoration, focusing on balancing active and reactive power while ensuring voltage stability. For instance, this paper employs an agglomerative clustering technique, which partitions the power system into segments with balanced reactive power, facilitating swift restoration postblackout. Central to this methodology is the use of the line stability factor, which assesses the voltage stability of individual lines, identifying the system's stronger and weaker sections based on voltage stability levels. This paper demonstrates the effectiveness of the proposed methodology through case study analysis, comparing voltage stability levels across agglomerative clusters and their geographical locations. The power system is divided into two stable partitions, considering the number of black-start generators, available reactive power, and voltage stability levels. This partitioning reveals that the clusters formed by the agglomerative method are inherently stable, suggesting enhanced system stability, dependability, and availability during the restoration phase following a blackout. In addition, this paper discusses the potential causes of blackouts, offering insights into their prevention, and finishes with a novel clustering methodology for power systems, considering reactive power and voltage stability. This method facilitates the parallel restoration of the system's independent partitions, significantly reducing restoration time; it addresses critical challenges and outcomes, underscoring the methodology's potential to revolutionize blackout recovery processes in modern power systems. [ABSTRACT FROM AUTHOR]

Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

SUMMARY Characteristic study of cavity discharge is important in detecting and diagnosing internal failure of power transformers. Based on the kernel principal component analysis method, eight new feature parameters are extracted from 29 statistical parameters, which are commonly used in the characteristic study of partial discharges. Redundancy and computational complexity for applications are greatly reduced. Using cluster analysis method, new parameters are proved to be effective in the application of stage classification in the cavity discharge development. Four stages are classified: incipient discharge, unstable discharge, consistent discharge, and disruptive discharge. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a grey-box model of a DC/DC boost converter for PV energy systems. The proposed model contains a white-box model part and a black-box model part together to prepare a better model for the PV boost converter. The white-box model part is used for knowledge of the circuit by mathematical equations since the black-box model part is used for unknown parameters such as temperature and electromagnetic interference. The black-box part of the proposed model is created by a nonlinear system identification of a real boost converter circuit with an artificial neural network. The precision of the mathematical model and the advantages of the fast prediction ability of the artificial neural network were used together. The proposed grey-box model is compared with the existing state-space and black-box models and experimental results. The results of the study showed that the average correlation between the proposed grey-box model output and the experimental results is 97.52%. Therefore, the proposed model can be used for analyzing DC/DC boost converter output characteristics before field applications. [ABSTRACT FROM AUTHOR]

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system. [ABSTRACT FROM AUTHOR]

In view of the fact that the influence of positive and negative sequence decomposition, which is widely used in positive and negative sequence decoupling control in control system, on the fault current calculation process is not deeply considered in the existing transient analysis methods of permanent magnet direct-drive wind farm short circuit current, this paper proposes a transient short circuit current calculation model that takes into account positive and negative sequence decomposition. The influence of the transient characteristics of positive and negative sequence decomposition on the control system is studied, and the mechanism of its action on the transient change of short circuit current is revealed. The positive and negative sequence decoupling processes of the circuit equation are modified, and the characteristics of the coupling equation are analyzed. The difference in the converter output voltage between the circuit equation and the control equation and the depth of its influence on the calculation process is revealed. On the basis of quantifying the difference at the converter output voltage, the circuit equation and the control equation are combined to form a short-circuit current calculation model with positive and negative sequence decomposition, which accurately characterizes the transient characteristics of fault current under different voltage drops and effectively improves the accuracy of the calculation results. [ABSTRACT FROM AUTHOR]

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability. [ABSTRACT FROM AUTHOR]

EVs suffer from short driving range because of limited capacity of the battery. An advantage of EVs over internal-combustion vehicles is the ability of regenerative braking (RB). By this advantage, EVs can develop energy by RB which can be stored in the battery for later use to increase the driving range of EVs. There are different motors that can be used in EVs, and the control during RB mode is dedicated for certain motor types. However, the previous studies for EV-based IM drives consider the motor-speed control without considering its RB. This paper proposes a robust control of induction motor (IM) during RB mode of EVs. The proposed control system is simple and depends only on mathematical calculations. The obtained results confirm the effectiveness and accuracy of the suggested control strategy with a good dynamic behavior under different operating conditions. Also, the results assure the robustness of control capabilities under parameters uncertainties during the RB mode of EV-based IM drives. [ABSTRACT FROM AUTHOR]

This paper presents a unique control system to regulate power exchanges and load bus voltage in a networked microgrid (NMG) system comprising AC and DC microgrids. During the islanding of a microgrid in this NMG system, load voltage and power balance can get disturbed. A control system and associated converter and inverter control methods are presented to rectify these issues. An efficient model predictive control (MPC) method, which gives a tracking error of 50% lower than a conventional proportional-integral (PI) controller, is used to control multiple inverters in the NMG system. Simulation studies are conducted to test the NMG in islanding and load change scenarios. With the help of these studies, it is verified that the MPC-controlled inverters can provide better tracking accuracy in achieving desired power flows in the NMG system. [ABSTRACT FROM AUTHOR]

The escalating growth of electric vehicle (EV) load has emphasized the growing importance of effective scheduling strategies. Due to the discrepancies among EV owners, their responses to scheduling can differ significantly. Therefore, to achieve better scheduling results, it is crucial to consider the impact of these discrepancies on the optimal scheduling. This paper proposes a classified scheduling method for different types of EV owners. According to charging and vehicle-to-grid (V2G) data of EV owners, the K-means clustering algorithm (K-means) is used to classify EV owners, and the demand response (DR) model is established based on the classification results. The DR model is designed to account for the diverse responses of different EV owners, and the price elasticity, time gap elasticity, and preference time elasticity are important factors in the model. This paper adopts the maximization of smart grid's revenue as the optimization objective through three approaches: (1) modifying the charging and V2G of EV; (2) obtaining V2G prices for all types of EV; and then (3) adjusting the power output of each unit. To evaluate the proposed method, the IEEE 10-unit system is employed for simulation, and the optimization problem is solved using the CPLEX solver. Compared to previous studies, the proposed classified scheduling method exhibits significant improvements in terms of revenue maximization, load distribution among different types of EVs, generation cost savings, and load variance reduction. [ABSTRACT FROM AUTHOR]

For achieving the carbon peaking and carbon neutrality goals, the renewable energy and the battery energy storage will be developed more rapidly. The participation of the solar-wind-battery renewable energy system (SWBS) in black-start can improve the resilience of the power grid. Aiming at the balance between the capacity demand of SWBS participating in black-start and the benefit of SWBS, this paper proposes a positive-sum game-based SWBS capacity configuration planning method. First, a levelized cost of energy- (LCOE-) based full life cycle index is presented and constructs an economic benefit model of SWBS considering the participation of wind and solar power plants in carbon trading. Second, a black-start capacity demand model is constructed considering the continuous power demand on the auxiliary devices of the operated thermal power generator. Taking the economic benefit of SWBS and the black-start capacity requirement into account, this paper establishes a positive-sum game-based SWBS capacity optimal configuration model, in which maximizing the economic benefits of SWBS as an objective. Third, the simulation analysis is carried out by using a real power system, and results verify that the proposed method can achieve the optimal balance between the feasibility of black-start and the economic benefit of the SWBS. [ABSTRACT FROM AUTHOR]

In the next decade, the demand for copper is expected to grow by 25%, posing a challenge for mining facilities to increase their production by opening new mines or upgrading existing ones. To optimize the electrical infrastructure of mining facilities, the integration of flexible AC transmission systems (FACTSs) can be instrumental in mitigating voltage stability issues that may arise during the installation of new gearless motor drives (GMDs). The purpose of this paper is to conduct a comparative analysis of the dynamic performance of static VAR systems (SVSs) as a means of enhancing voltage stability in mining expansion projects, particularly in the context of the integration of new GMDs into the system. This paper presents a case study of an existing three crushing-line mine configuration that has been upgraded with two new GMD systems. The primary contribution of this research is a comprehensive methodology designed to enhance the stability of a mining system through the integration of an SVS, which includes the sizing of the SVS system, an analysis of costs, as well as a determination of the required installation surface and optimal placement of the SVS within the system. The simulation results conclusively demonstrate the effectiveness of the SVS systems in reducing the voltage drop by 2% upon activation of new GMDs, as well as mitigating the adverse impact of transient disturbances on the system. Specifically, the first oscillation voltage peak value is improved by 3.5%, following a three-phase short circuit of 1 second duration, while overvoltage is reduced by 1% in response to sudden load changes. When compared with the system without an installed SVS, these findings highlight the significant advantages and benefits of integrating SVSs into mining operations. [ABSTRACT FROM AUTHOR]

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system's reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system's load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven. [ABSTRACT FROM AUTHOR]

Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power system. Using collaborative optimization during the flexibility resource planning stage can significantly improve the overall economics and flexibility. Therefore, a bilevel operation-planning joint optimization model for flexible capacity allocation is proposed in this paper. The aim is to optimize the annual total cost and flexibility of the system. The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. On this basis, the lower level simulates production to estimate daily operating costs. In addition, the model is solved iteratively using the nondominated sorting genetic algorithm-II (NSGA-II) and the linear programming method to obtain the Pareto solutions. Case studies are carried out based on a practical town area, and the results verify the validity and rationality of the proposed bilevel capacity allocation model. [ABSTRACT FROM AUTHOR]

Under the "carbon peak and carbon neutrality" goal, the construction of an efficient, low-carbon, and economical energy supply system is of great significance for advancing a dual carbon strategy. In allusion to the integrated energy systems (IES) with hydrogen energy coupling, a hydrogen energy coupling IES low-carbon optimization operation strategy that took account of green certificate and ladder-type carbon joint trading and dual-incentive demand response was proposed in this paper. First, a hydrogen energy multiuse system composed of an electrolyzer, a hydrogen fuel cell, a methane reactor, and hydrogen energy storage was constructed to make full use of the low-carbon cleaning characteristics of hydrogen energy. Besides, a combined model of hydrogen mixed with natural gas was established to improve the utilization efficiency of hydrogen energy. Second, a dual-incentive demand response model including price incentives and subsidy incentives was constructed to fully use the ability to adjust demand-side resources. Next, in view of the carbon emission reduction mechanism of the green certificate, a green certificate and ladder-type carbon joint trading mechanism was constructed. In addition, a green certificate trading mechanism and a reward and punishment tiered carbon trading mechanism had been introduced separately in the IES optimization operation model to reduce carbon emissions of the system. The calculation simulation sets up different scenarios for comparative analysis. As shown by the results, the proposed model could effectively improve renewable energy consumption capacity and energy utilization efficiency. The effectiveness of hydrogen energy utilization, demand respond, and green certification carbon trading mechanism in improving system economy and low-carbon efficiency is verified. [ABSTRACT FROM AUTHOR]

Out-of-step (OOS) condition is a potential problem in the power system and uncontrolled islanding is one of the severe consequences of out-of-step condition which leads to cascaded tripping of the system. To avoid this undesired cascaded tripping, early prediction of out-of-step condition is essential before losing the synchronization of generators and between the system areas. Controlled islanding is the last emergency action that can be taken by splitting the system intentionally into coherent islands. Conventionally, OOS condition has been detected with impedance-based measurement techniques which have their own limitations. With the implementation of a wide-area measurement system with synchrophasor measurement units (PMUs) in the power system, it is now possible to measure the bus voltage angle at a much faster rate than with SCADA. In this paper, synchrophasor-based bus voltage angle measurement has been used for early prediction of OOS condition in power systems. A new algorithm has been formulated for both generator and tie lines based on the trajectory of first and second derivatives of the bus voltage phase angle for early detection of OOS condition. The prediction speed of proposed method is found to be between 5% and 70% of time to OOS, after disturbance clearance. The proposed algorithm has been mathematically formulated and evaluated with three benchmark systems in real-time simulation environment with an OPAL-RT real-time simulator and a HYPERSIM platform. [ABSTRACT FROM AUTHOR]

Nowadays, microgrids (MGs) are receiving a lot of attention. In an economical MG, the battery energy storage system (BESS) plays an important role. One of the biggest challenges in MGs is the optimal choice of the BESS that can lead to better performance of the MG, which will be more flexible, efficient, and effective than traditional power systems. In this paper, we present the modeling and simulation of different energy storage systems including Li-ion, lead-acid, nickel cadmium (Ni-Cd), nickel-metal hybrid (Ni-Mh), and supercapacitor (SC), for renewable energy applications, and more specifically for MGs. The results of simulation show that Li-ion batteries have a better response time than lead-acid batteries, Ni-Cd batteries, and Ni-Mh batteries and thus are more suitable for combination with supercapacitors. Li-ion batteries are the best option for fast-charging applications in MGs. The discharge phase ends with SOC ≤ ±94%, SOC ≤ ±95%, SOC = 95%, SOC < 95%, and SOC < 60%, respectively, for Li-ion, lead-acid, Ni-Cd, Ni-Mh, and supercapacitor. Moreover, the use of the battery management system (BMS) can significantly improve the performance of BESS, leading to higher levels of SOC and longer life span. The obtained results have shown that with an optimization algorithm for energy storage systems, more specifically for the battery-charging mode, the response time of BESSs can be further improved. The effect of ambient temperature has also been investigated on the functional capacities of the batteries. The obtained results demonstrated that extreme temperatures (80°C to −80°C) have a significant impact on battery performance and capacity, especially for Li-ion batteries, with a drop in capacity of up to 50% at −40°C. This highlights the importance of considering the ambient temperature in the design and operation of MGs. Overall, our study provides valuable insights into the optimal selection of BESS and the impact of ambient temperature on their performance, which can help in the development of more efficient and reliable MGs. [ABSTRACT FROM AUTHOR]

The cyber-physical power system (CPPS) is a modern infrastructure utilising information and communication technology that has become more vulnerable to cyberattacks in recent years. The attack magnitude injected by the adversary is stealthier and it cannot be detected using conventional bad data detection techniques. Protecting sensitive data from data integrity attacks (DIA) is essential for ensuring system security and reliability. A tragic event will occur if the attack goes unreported. Therefore, DIA detection is highly vital for the operator in the control centre to make important decisions. This paper addresses the attack impact on WAC applications and attack detection using the model-based method and data-driven-based methods. On the basis of the validation of performance indicators, various detection approaches are simulated and compared to determine the best detection strategy. Simulation results show that in the model-based anomaly detection method, the recursive polynomial model estimator (RPME) has better detection performance than the recursive least square estimator (RLSE). The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). On the WSCC 3 machine 9-bus system, the efficacy of the suggested methods is evaluated. [ABSTRACT FROM AUTHOR]

In a grid-connected photovoltaic system, the quality of energy injected by the photovoltaic system into the grid is directly linked to the topology of the inverter used and to the efficiency of its control technique. This paper addresses this problem for a two-level grid-connected photovoltaic inverter operating under low irradiance conditions. The aim is to reduce the harmonic distortion on the electrical network and therefore improve its power quality. To achieve this goal, a control strategy was set up considering the nonlinearity of the dynamic system, and the high dimension of the system model. Thus, a nonlinear controller designed using the backstepping technique is proposed. The effectiveness of this control strategy was evaluated by simulation in MATLAB/Simulink. Results show that the proposed control technique significantly improves the power quality of the grid-connected photovoltaic system by minimizing the current harmonic distortion rates in low irradiance conditions. The current harmonic distortion rates obtained for solar irradiance of 1000 W/m2, 750 W/m2, 500 W/m2, and 250 W/m2 are 0.48%, 0.78%, 1.22%, and 2.16%, respectively. The power factor is 0.988, and the DC bus voltage is maintained at its reference voltage of 600 V with a very low response time during the transient phases. A comparison of our simulation results with those found in the literature on other control techniques such as proportional-integral-derivative (PID) and synergetic controls shows the efficiency, superiority, and satisfactory performance of the proposed control scheme to minimize harmonic distortion under low irradiance conditions. The robustness and better dynamic performance of the proposed backstepping controller under varying irradiance conditions have also been shown. [ABSTRACT FROM AUTHOR]

In recent years, a sharp increase in integration of renewable energy sources (RESs) in power system network has been observed. High penetration of RES interfaced with power electronics converters-inverters with reduced or no inherent inertia compromises modern power system's overall stability. Due to low inertia, voltage and frequency deviations far off the allowable threshold occur. To overcome this challenge, an adaptive inertia control strategy based on optimization technique is proposed. The improved particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques-based PID controller has been used to generate the appropriate virtual inertia coefficient for effective emulation of inertia in the presence of energy storage system. The conventional PSO suffers local optima stagnation, resulting in premature convergence during searching process in order to achieve global and local position. To address this issue, the velocity update equation was modified on inertia weight (w) using an additional exponential term with linear decreasing inertia weight PSO (LDIW-PSO). In this paper, exponential power is taken strategically instead of squaring it in order to reduce the number of iterations for faster convergence. Finally, a microgrid based on wind and solar energy is simulated using MATLAB/Simulink where three cases, 2% disturbance, 3% disturbance, and 4% disturbance, have been considered. Here, the evaluation of proposed system is carried out based on four main performance indices (ITAE, IAE, ISE, and ITSE). Furthermore, validation was done through hardware prototype to get experimental results in real time. The results from MATLAB simulation and experimental setup are in sync. [ABSTRACT FROM AUTHOR]

This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail. [ABSTRACT FROM AUTHOR]

Demand response (DR) is one of the most cost-effective and logical smart grid terms for power systems that can be used during peak load hours, whereas load shedding (LS) is the last and most expensive solution in emergency grid situations. The aim of them is to satisfy equilibrium constraints between consumption and generation and restore the power system frequency to normal bounds at the least possible time. The rapid increase in power grid costs and limitations on electricity generation resources have resulted in the increasing need for industrial customers' participation as an alternative solution to peak load spikes. Therefore, this paper considers the estimation of imbalance active power in a smart strategy to introduce a critical DR (CDR) in heavy industries (pulp and paper, cement, and medium density fibreboard (MDF)) based on direct load control (DLC) technique in order to reduce the need for LS in frequency restore. To evaluate the proposed model, a real case study including the typical pulp and paper, cement, and MDF industries processes with actual power consumption data is considered. The numerical and graphical results confirm that the proposed CDR can be used as an inexpensive solution to replace the costly spinning reserves and avoid load shedding. [ABSTRACT FROM AUTHOR]

Summary: The aim of the paper is to provide seamless transition between the grid connected to an islanded mode and vice versa by switching the control of the load connected VSC (LCVSC) ensuring uninterruptable power supply. In the grid connected mode, a TOQSG (third‐order quadrature signal Generator) based method is applied for power quality improvement at the grid. During synchronization, the LCVSC adapts the phase and frequency of the grid voltage for seamless transition. The paper presents a smart microgrid, which manages the mode of operations and generation of reference signals for the smooth operation. The proposed approach is validated through test results. The grid current and CCP voltage distortion are as per the IEEE‐519 and the IEEE‐1547 standards. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the national carbon emission trading market, new energy system participates in the carbon emission trading market. Analysing the potentiality of virtual power plant trading in carbon emission trading market, this paper designs a two-stage joint trading mechanism for electricity and carbon market with a weekly cycle according to their characteristics, which contain multiple transaction types for both markets. In addition, this paper introduces a carbon capture system (CCS) in gas turbine, which reduces the actual carbon emissions and increases the carbon market income of virtual power plant. Furthermore, it improves the comprehensive and flexible operation capability by adjusting the operation level of CCS, which is conducive to the timely consumption of renewable energy. Aiming at the uncertainty of renewable energy output and electricity price, the paper adopts a multiscenario analysis method to deal with it and establishes a stochastic optimization model to maximize joint earnings. Finally, through example analysis with GAMS, the effectiveness of the scheduling model is verified with simulation results. The overall income of the virtual power plant is improved, and the low carbon power is realized. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) reconfiguration is an effective solution for reducing the hot spot effect caused by partial shadows on PV arrays. This paper proposed an efficient atom search optimization- (ASO-) based PV reconfiguration method. The analysis and comparison with the other four reconfiguration methods were performed by three evaluation criteria, which are mismatch loss, fill factor, and standard deviation, respectively. The conclusion can be drawn that the efficiency, rapidity, and reliability of ASO are superior to those of other methods. Besides, a moving cloud shadow mode of 9 × 9-scaled PV array is designed in this paper, which can be widely recommended to thoroughly study a PV reconfiguration approach. [ABSTRACT FROM AUTHOR]

The efficiency of on-site consumption of new energy and the economy of dispatching strategy for that in modern microgrids are increasingly concerning, which are closely related to the microgrid control model with source-load uncertainty. To this end, this paper proposes the multiagent hierarchical IQ (λ)-HDQC regulation strategy to realize the source-load-storage-charging collaborative control of the microgrid model with high-permeable new energy. The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. The interaction of the two-layer strategies realizes the source-network-load-storage-charge global dynamic interactive regulation of microgrids. Indicators of energy efficiency are constructed in this paper to measure the simulation results. And the superiority of the proposed strategy is verified through the simulation results of the microgrid system. [ABSTRACT FROM AUTHOR]

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

In this paper, DM EMI filters including LC, LC with damping, and Pi and LCL filters are designed for DC-DC Cuk converter operated in BCM. Although, DM EMI is so important for any power converter, the effect of DM filter is not investigated in detail in literature with respect to control characteristics and DM noise reduction. The main contribution of this paper is comparisons of DM EMI filter topologies regarding control characteristics and EMI noise reduction which is not presented in the literature. Another contribution is to give a practical design methodology for EMI filter design avoiding complex mathematical procedures. Besides, the paper designs an LCL filter, popular for inverter applications, for Cuk converter that is not introduced in the literature. Examining the filter effect on the converter from the control point of view is a very complicated issue because a linearized model of the converter which is naturally nonlinear should be obtained. To do so, the converter's small signal analysis must be derived. Therefore, for examining the effect of filters, a small signal analysis of the Cuk converter is made through an average state-space model. After that, transfer functions of each filter topology are obtained. Lastly, the entire transfer functions of the system using both the Cuk converter and each filter are acquired, separately. By using the transfer functions of overall systems, the effect of each filter topology is investigated by root locus and bode plots. Moreover, experimental verification of all filters is realized over a 15 W Cuk converter with a 25 kHz switching frequency. It is validated by applications that the LCL filter provides better control characteristics on the Cuk converter, the total inductor value is reduced by 30% and the total capacitor value is reduced by 25% compared to other filters. However, with respect to DM noise Pi and LC filters give better results. [ABSTRACT FROM AUTHOR]

This paper presents an advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motor's stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75% and 41.26% as well as a rigorous motor health monitoring. [ABSTRACT FROM AUTHOR]

This paper seeks to improve mobile communications performance by integrating artificial intelligence (AI) techniques. The study is centered on device-to-device (D2D) communication, which has emerged as a significant aspect in the 5th generation of mobile networks (5G) and is expected to be extended to the 6th generation (6G). Based on the fuzzy system, the proposed solution tackles the critical issue of energy harvesting in D2D communication. The solution presents a support system that selects the best relay for the transmitter-receiver communication to minimize energy consumption and maximize the network's lifetime. Minimizing energy consumption optimizes the network's lifetime, thus providing a reliable and efficient communication system. The approach taken in this study offers a novel perspective in addressing the energy harvesting challenge in D2D communication. It is expected to have a significant impact on the performance of mobile communications. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand. [ABSTRACT FROM AUTHOR]

Researchers are paying attention to the current trend of electric vehicles (EVs), but there are not many places to charge them and serve as electric vehicle charging stations (EVCSs). In order to prevent the lack of EVCS for battery EVs (BEVs) on any road with all modes of operation, this paper proposed the design of a novel configuration and control structures for the isolated charging station (ICS) based on the solar PV system for efficient energy utilization to charge battery EVs (BEVs) with flexible double function cascaded converter (MPPT and DC-link voltage regulation). This was done with the aid of a battery energy storage system (BESS) as a backup/auxiliary source. The PV system is reliant on solar radiation and the daily load profile. Therefore, the BESS is employed to provide continuous power to BEVs and overcome PV's intermittent nature. The cascaded converter is used to achieve the MPP and to regulate the DC-link voltage to its reference value (100 V), while the buck-boost matching bidirectional DC-DC voltage converters (MBDVCs) are used for charging and discharging mode/buck and boost mode, respectively, of BEVs and BESSs. Additionally, the MBDVCs are used as an interface between the ICS sources with the load through the developed control systems. The outcomes of various operating modes show that the system functions effectively. To verify the system performance, the MATLAB/Simulink application builds the system setup. [ABSTRACT FROM AUTHOR]

An upgraded Black Widow Optimization algorithm for obtaining the ideal switching angles of a reduced structure multilevel inverter is proposed in this paper with minimum voltage THD. The conventional BWO algorithm has been modified and proposed to solve the THD minimization problem with faster convergence rate, lesser tuning variables, and lesser number of equations than the other existing nature inspired algorithms. The reduced structure MLI is designed with three asymmetrical DC voltage sources of ratio 1 : 2 : 4 to generate 15-level output voltage which requires reduced number of switching devices in comparison with the existing topologies of equivalent voltage level. The asymmetrical DC voltage sources to the MLI are derived from a single DC voltage source using a multisecondary winding DC-DC converter in order to provide the isolation between the load side and source side. The proposed modified BWO algorithm reduces the complexity of solving the nonlinear equations to achieve the best result by avoiding the local optima. To verify the effectiveness of the upgraded BWO algorithm, the optimization problem for three phase fifteen level MLI has been solved using the genetic algorithm, BFO algorithm, and PSO algorithm. The results for the above three applied algorithms are compared with the upgraded BWO and presented. The proposed algorithm has minimized the output voltage THD to 1.83% at 0.88 modulation index. The developed scheme has been corroborated with the help of MATLAB simulation and the results have been verified through a hardware model developed in laboratory. [ABSTRACT FROM AUTHOR]

The predictability of their manufacturing lines allows industrial facilities to optimize their production scheduling and to participate in demand response (DR), in day-ahead, real-time pricing (RTP) electricity markets. Battery energy storage systems (BESSs) make the electrical demand of industrial facilities more flexible and increase their potential to benefit from DR. The BESS sizing problem, for industrial facilities participating in RTP DR, is complex due to the discreteness of their manufacturing lines and the stochastic nature of electricity pricing. In this paper, an approach to BESS sizing is proposed. Scenario extraction using k-means clustering is used to reduce the problem complexity, and the extracted scenarios are preprocessed to reduce the search space for the optimal size of the BESS. The steps involved in the proposed approach are demonstrated, in detail, through a case study that uses a generic model of an industrial unit. The results of the case study show the effectiveness and validity of the problem reduction techniques used and highlight the role of electricity storage in maximizing the profits of the industrial unit. Finally, a sensitivity analysis is carried out to illustrate the impact of the BESS installation cost on the results. [ABSTRACT FROM AUTHOR]

Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system's performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers' utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers' welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models. [ABSTRACT FROM AUTHOR]

Megatrends, such as the proliferation of distributed generation, electrification, and the appearance of aggregator companies, put the low voltage power grids under intense pressure. Since the network infrastructure developments cannot keep up with the trends, distribution system operators turned to alternative solutions. Smart grid assets, such as on-load tap-changing distribution transformers or serial low voltage regulators, are promising solutions. However, the energy transition cannot be handled with the network expansive on the distribution level. Control centers are predicted to expand to this voltage level in the near future, and distribution system state estimation could be an enabler of all functionalities. On the low voltage level, data scarcity is a great challenge in observability; therefore, research must focus on the creation of pseudomeasurements and integration of available data sources. This paper examines the inclusion of smart assets from the conceptual point to the application on two sites, based on data from operational environments, both with a pseudomeasurement and an integrated metering point approach. The results showed that integrating smart assets could considerably mitigate voltage fluctuations, and reduce estimation errors by two magnitudes on the low voltage network. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

This paper investigates the irreversible demagnetization (ID) of a large capacity line-start permanent magnet synchronous motor (LSPMSM) considering influence of permanent magnets' (PMs) temperature. The phenomenon of ID occurs on the pull-in stage and close to the cusp of magnet on the direct-axis direction. The PMs' temperature causes the demagnetization curve to move down and the knee point to shift up. The extensive area ID of magnet will lead to pulling in synchronism failure and operation at the asynchronous state. A prototype is fabricated to validate the analysis that temperature of PMs will bring more serious ID risk and degradation of motor performance. [ABSTRACT FROM AUTHOR]

To supply resident loads far from the grid, a stand-alone wind system with a small-scale wind turbine and battery storage can be used. The traditional configuration of the system has a permanent-magnet synchronous generator (PMSG). Other alternative configurations use doubly fed induction generator (DFIG). The systems with DFIG have variable speed operation with a limited speed range which reduces the captured power from the wind turbine. Also, there is a rotor-side converter (RSC) which carries the reactive power of DFIG, in addition to the slip power. In this paper, an improved system configuration with DFIG is controlled by an advanced control scheme. By this advanced scheme, the speed range is increased such that maximum power operation of wind turbine is obtained for complete range of wind speed, and volt-ampere (VA) requirements of RSC are reduced by the operation at nearly zero-slip power. [ABSTRACT FROM AUTHOR]

One of the main challenges of a four-switch three-phase rectifier (FSTPR) is a DC imbalance in capacitor voltages. On the other hand, under unbalanced grid voltage conditions, unbalanced three-phase input current and the DC-link voltage affect the performance of the FSTPR. Although many papers focus on designing a controller to balance DC-link capacitor voltage, a few papers are available to cope with the imbalance of DC-link capacitor voltages and input current simultaneously under unbalanced grid voltage. In this paper, first, the operation of the FSTPR under unbalanced grid voltage conditions is investigated. It can be seen that under these conditions, the oscillatory parts of the active and reactive input power, i.e., sin and cos components, are the leading cause of the problems that can severely degrade the FSTPR performance of the controller. Therefore, this paper presents a promising control technique to eliminate the mentioned oscillation components. Aiming at this purpose, the current control loops in the dq axis are divided into two positive and negative sequences, i.e., idq+ and idq−. Simulation results in MATLAB/SimPowerSystem™ show that the proposed controller can reduce the output voltage ripple, the total harmonic distortion, and the unbalancing of input current compared to a conventional controller. Under these conditions, the DC-link capacitor voltages are more balanced, significantly reducing the voltage limiter of the FSTPR. [ABSTRACT FROM AUTHOR]

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]