International Transactions on Electrical Energy Systems最新文献

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.

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.

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.

Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm.

In these modern times, the Z-source inverters (ZSIs) have become a revolutionary invention ever since the year 2002. The pulse-width modulation (PWM) technique used for most of the ZSIs is simple boost control PWM (SBC-PWM), and the SBC-PWM implies for a greater voltage stress on the inverter bridge and provides less boost factor. Likewise, many topologies for the basic Z-source topologies are evolved, and different PWM techniques are applied to them such as maximum boost control (MBC), maximum boost control with third harmonic injection (MBC-THI), maximum constant boost control (MCBC), and constant boost control with third harmonic injection (CBC-THI). All these mentioned PWM techniques are compared, and the converter opted in this paper is an enhanced ultrahigh gain active-switched quasi-Z-source inverter (EUHG-qZSI). The comparisons discussed in this brief are bridge stress, voltage gain, and voltage boost variation under each control strategy implementation. The theoretical and simulation evaluation for the abovementioned findings is presented in this paper, and the best PWM among them is maximum boost control (MBC).

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.

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.

Modular multilevel converters (MMCs) are widely applied to medium and high voltage occasions. The total power consumption of the submodule (SM) and the maximum power consumption of power devices in the SM are related to the operating costs and lifetime of the MMC. Existing literature only considers total power loss optimization or maximal power consumption optimization in MMC’s SM. In this article, a reduced loss and extended lifetime power loss optimum control (RLEL-PLOC) is introduced to inject the first-best second harmonic circulation into the MMC’s arm current. Compared with the conventional power loss optimization control, the proposed control could decrease the maximal power consumption of the semiconductor devices without increasing the total loss of the SM. According to study results of the MMC, compared with the circulating current suppression control (CCSC) method, the total power consumption of the SM could be reduced by 4.2% and the maximal power dissipation in the SM could be reduced by 5.4% with RLEL-PLOC. PSCAD simulation and MMC prototype experiment are also carried out, and the research results verified the availability of the put forward RLEL-PLOC for MMCs.

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity.

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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.