International Transactions on Electrical Energy Systems最新文献

In this paper, a novel switched/modulated capacitor filter scheme is proposed for enhancing vehicle-to-house (V2G) battery-charging stations utilized in electric vehicles (EVs). The novel approach is tested on two controllers with a classical optimized PID controller. The technique, which employs modified multimode weighted charging modes for fast charging, improved power quality, and minimal inrush currents, results in reduced voltage transients on the DC side and less harmonics on the AC side. An intercoupled DC-AC capacitor interface that features dual complementary switching modes is used by the switched modulated filter as a way to provide optimal pulsing in both the tuned-arm filter and capacitive compensator modes of operations. This switched intercoupled AC-DC filter compensation approach leads to enhanced power usage in EVs, along with lower AC-DC voltage transients and inrush currents.

In high voltage direct current (HVDC) systems, the occurrence of short circuits results in a rapid rise in line current, adversely affecting the interconnected alternating current (AC) grid. Particularly in voltage source-based multimodular converter (MMC) HVDC networks, such transients pose a significant threat to power converter units. Traditional relaying algorithms prove inadequate for safeguarding AC-DC-linked HVDC networks. Both the direct current (DC) and alternating current (AC) segments of such networks demand robust protection mechanisms. Signal processing-based techniques offer valuable insights during fault events, yet challenges such as noise interference, mode missing, and harmonics generation during faults persist, leading to erroneous conclusions. To address this, we introduce Synchro Squeezed Transform (SST) in this study to mitigate ambiguity in relaying algorithm decisions. SST facilitates the extraction of amplitude and effective instantaneous frequency of AC signals. The proposed method employs the Rényi entropy of time-frequency representation (TFR) as the primary logic, followed by the estimation of the spectrum-based Teager–Kaiser Energy Operator (TKEO) for DC signals as the secondary logic. These combined logics enable the identification of various AC and DC faults in Voltage Source Converter (VSC)-based bipolar HVDC networks. Simulation results, including comparisons with existing approaches, demonstrate the efficacy of the proposed methodology in enhancing fault detection and classification accuracy in AC-DC-linked HVDC networks.

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real-time data anomaly detection system for small hydropower clusters (RDADS-SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real-time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real-time data anomaly detection algorithm for small hydropower stations integrated with the Z-score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived-based hot-standby RDADS-SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision-making.

A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H-bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery-based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases.

The dynamics of wind turbine (WT) behavior and the identification of factors influencing its performance are both highly complex and challenging. Additionally, component damage, along with sensor and actuator failures, can lead to system faults that significantly reduce performance. Understanding these impacts provides control system designers with valuable insights to develop strategies for mitigating faults and enhancing WT performance. This study presents a novel evaluation of rotor fault effects on output power and measured variables in WTs using Monte Carlo simulation and sensitivity analysis. Through comprehensive simulation and numerical analysis, the faults with the greatest impact on WT performance are identified. The results of this research not only provide a better understanding of the WT performance during faults but also identify the significant effects of these faults on the performance of wind turbines and specifically identify and prioritize the main faults. These results are instrumental in improving control strategies, developing preventive maintenance programs, and offering practical solutions to reduce operational costs and extend the equipment’s useful life.

One of the objectives of electrical distribution networks is to provide customers with access to high-quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter’s resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system’s frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers.

This paper presents a pioneering exploration into the optimization of Home Energy Management Systems (HEMS) through the novel application of the Bacterial Foraging Metaheuristic Optimization (BFMO) algorithm and Deep Reinforcement Learning (DRL). The study systematically addresses the pressing challenge of enhancing residential energy efficiency, focusing on dynamic appliance scheduling within HEMS. A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. The unique contribution of this research lies in the effective integration of the BFMO algorithm and DRL to orchestrate energy-conscious scheduling of home appliances within HEMS. The BFMO algorithm demonstrates its adaptability to fluctuating energy costs and consumption patterns by simulating the foraging behaviour of bacteria. At the same time, DRL enhances the system’s ability to learn and optimize scheduling decisions over time, showcasing their combined efficacy in real-world scenarios. The algorithms’ iterative application of chemotaxis, reproduction, elimination-dispersal, swarming, and learning consistently yields optimized appliance schedules. The main focus of this study resides in the evaluation metrics illustrating the tangible benefits of BFMO and DRL compared to traditional HEMS. Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms’ impact. Furthermore, the study delves into enhancing user comfort, integrating renewable energy sources, and the overall robustness of HEMS, all demonstrating the multifaceted advantages of the BFMO and DRL approaches. This research contributes methodologically by introducing and detailing these algorithms and provides a valuable dataset and evaluation metrics for future research in the domain. The findings underscore the immediate and long-term relevance of optimizing HEMS with BFMO and DRL, catering to researchers, practitioners, and policymakers involved in advancing smart grid technologies and sustainable residential energy management. In summary, this work establishes the BFMO algorithm and DRL as pioneering and versatile tools for energy-conscious appliance scheduling in HEMS, offering a substantial leap forward in the quest for efficient and sustainable residential energy management.

Smart grid is the primary stakeholder in smart cities integrated with modern technologies as the Internet of Things (IoT), smart healthcare systems, industrial IoT, renewable energy, energy communities, and the 6G network. Smart grids provide bidirectional power and information flow by integrating many IoT devices and software. These advanced IOTs and cyber layers introduced new types of vulnerabilities and could compromise the stability of smart grids. Some anomalous consumers leverage these vulnerabilities, launch theft attacks on the power system, and steal electricity to lower their electricity bills. The recent developments in numerous detection methods have been supported by cutting-edge machine learning (ML) approaches. Even so, these recent developments are practically not robust enough because of the limitations of single ML approaches employed. This research introduced a stacked ensemble method for electricity theft detection (ETD) in a smart grid. The framework detects anomalous consumers in two stages; in the first stage, four powerful classifiers are stacked and detect suspicious activity, and the output of these consumers is fed to a single classifier for the second-stage classification to get better results. Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. We also perform comparative analysis using adaptive synthesis (ADASYN) and independent component analysis (ICA). The simulation findings reveal that the proposed model outperforms with 97% accuracy, 97% AUC score, and 98% precision.

Electric power systems constantly encounter disturbances and faults, necessitating fast and precise identification and rectification of these issues. This is crucial for ensuring the stability and reliability of the system. This paper introduces a protection scheme for accelerating the second zone operation of the distance relay during internal faults. The proposed scheme exploits the locus of power with positive power characteristics to effectively distinguish between internal and external faults. This is achieved by detecting the remote circuit breaker operation (RCBO). The locus of power remains predominantly within regions 1 or 2, with occasional transfers between these regions due to internal faults prior to and following the RCBO. Conversely, in the case of external faults, regions 3 or 4 are implicated. This distinct variation in the locus of power is applied to derive the protection algorithm. This is affirmed through sequence network analysis of various faults in the transmission line. The cumulative rate of change in relative reactive power has been employed for single-phase RCBO detection. The proposed protection logic employs supplementary undervoltage logic to avoid single-phase operation during two-phase and three-phase faults. The simulations are conducted with meticulous consideration of key factors, such as fault type, fault resistance, fault location, fault inception angle, and power source angle. Simulation results demonstrate the effectiveness of the proposed protection scheme.

DC-DC converters are essential for integrating distributed energy resources into microgrid (MG) systems. These converters are designed to incorporate intermittent renewable energy sources such as solar photovoltaic (PV) panels, fuel cells (FCs), and battery energy storage systems (BESSs) into the grid. However, conventional DC-DC converters have limitations including lower efficiency, voltage ripple, insufficient voltage regulation, and compatibility issues. This article presents boost and bidirectional buck-boost converters for direct current microgrid (DCMG) applications, employing an adaptive neuro-fuzzy inference system (ANFIS) for control. These proposed converter configurations adeptly manage wide input voltage fluctuations from intermittent sources, consistently supplying power to the DC bus at 500 V and 120 V for boost and buck operations, respectively, with an efficiency of 98.8%. The output voltage result shows that the ANFIS-based boost converter has 10% overshoot as compared to 41% and 50% overshoot in proportional integral (PI) and fuzzy logic controller (FLC), respectively. In both buck and boost modes, the converters’ voltage gain is influenced by duty ratio adjustments only, not sensitive to dynamic input voltage and flexible manipulation of the output voltage for BESS charging. Moreover, the designed converters accommodate load variations within the MG. To assess the converters’ ability to regulate output voltage effectively, PI, FLC, and ANFIS controllers are implemented and compared. And the ANFIS controller demonstrates superior performance, offering faster response times and enhanced stability. Evaluations are conducted through simulations in the MATLAB/Simulink environment.