International Transactions on Electrical Energy Systems最新文献

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.

In modern generation expansion planning of power systems, installing grid-connected renewable energy systems is preferred than thermal units due to their low generation cost and environmental pollution. However, the expanded power system must have ability to resist against any outages influenced on the frequency response of the system. So, several frequency-constrained expansion planning models are extracted to provide a reliable infrastructure to manage the frequency behavior. The main distinction of our model with others is considering the failure of grid-connected renewables in the expansion planning models. Furthermore, due to the lack of information about the uncertainty of malfunctions, a distributionally robust optimization approach is applied to the problem under several ambiguity radiuses. The results of implementing the proposed method on the IEEE RTS96 case show that increasing the penetration of malfunctioned units can lead to more investment on the thermal units to prevent frequency violation under any outage in the system. With increase of the Kullback–Leibler divergence from zero (stochastic) to 3 (robust), the cost of the robust model is increased about 0.02%. The model is designed for the deregulated market to increase the competition of market through maximizing their benefit and line congestion management with local marginal pricing techniques.

Power transmission using a voltage source converter- (VSC-) based high-voltage direct current (HVDC) system offers autonomous control of real and reactive power, constant DC voltage polarity, and bidirectional power flow. This helps to realize the multiterminal VSC-HVDC system and its integration into renewable energy sources to meet the growing power demand. However, there is a risk of higher voltages and currents during a DC line fault. The barrier to the advancements of VSC-MTDC systems is the nonavailability of commercial, higher-rated DC circuit breakers. This necessitates research on alternative methods of DC fault-clearing schemes with available technologies. In this direction, a superconducting fault current limiter (SCFCL) is an alternative option to mitigate the problems encountered in VSC-MTDC system operation. Because of this, there are not many VSC-MTDC systems available worldwide. This paper discusses different issues associated with the transient performance of the VSC-MTDC system. A representative case involving resistive SCFCL for DC line protection is presented. The simulations are carried out in the PSCAD/EMTDC platform.

A prevailing problem in power and energy subsystems is the smooth operation of electric energy systems. This work presents recent, efficient, and reliable evolutionary algorithm for solving the optimal power flow (OPF) analysis. All various practical complex equality and inequality constraints, namely, bus voltages, real powers of the generator buses, tap settings of the transformers and the reactive power generations, shunt compensation, and emission, are considered for the real-world scenario. Primary feature in a gas power plant that raises a lot of computational shortcomings with nonlinear structure in fuel cost is valve point effect. The existing research works have not factored the valve point effect and lack the accuracy in the fuel cost minimization and do not reflect the various practical complexities such as valve point and emission effects in the OPF problem formulation. This paper, for the first time, introduces modified OPF problem formulation incorporating valve point effect and applies covariance matrix adaptation-evolution strategy (CMA-ES) for solving the modified OPF problem. The algorithm is scrutinised and tested on a modified IEEE-30-bus platform for various OPF objectives such as cost minimization, transmission loss, and total voltage deviation, subjected to practical constraints. Load flow analysis has been carried out using the Newton–Raphson method. This work aims to lay the foundation in such a way that it can be applicable in a real-world scenario for any number of buses.

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug-and-play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus-based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning-based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug-and-play activities of prosumers. Moreover, an optimization-based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning-based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning-based DAI is thoroughly evaluated using the DRES-based IEEE 14-bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.