Electrical Engineering最新文献

It is of importance to detect and locate the errors of the protection measurement loop of the relay protection device for ensuring correct and timely functioning. The complex and changeable power system environment makes error detection and localization challenging. To this end, this paper proposes an entropy weight method-Euclidean distance and Tanimoto similarity (EWM-EDTS)-based method that integrates Euclidean distance and Tanimoto similarity with the entropy weight method. The Euclidean distance algorithm and Tanimoto similarity algorithm are used to calculate and obtain the similarity values between two sequences of samples and then the entropy weight method is used to calculate the weighting coefficients to fuse the two sequences of similarity values to finally obtain the EWM-EDTS distance. By comparing the value of the EWM-EDTS distance with the distance threshold, potential errors in the measurement data can be accurately located and identified. The simulation based on PSCAD shows that the proposed method can significantly improve the accuracy of error detection and localization.

The microgrid energy management with renewable energy is efficiently integrating intermittent sources like solar and wind while ensuring grid stability and reliability is difficult. The gravitational sear search method is employed in MG energy management with renewable energy sources (RESs) to address these problems. The gravitational search technique is used in the proposed method (GSA). In order to build a database of control signals that take into account the power differential between the source and load sides, GSA is used to precisely identify the control signals for the system. The proposed technique’s main goal is to deliver the best performance at the lowest possible cost. The constraints are the availability of the RESs, energy consumption as well as the storage elements’ level of charge. Batteries are utilized as an energy source to steady and allow the renewable power system components to continue operating at a constant and stable output power. The proposed method cost is 1.1$ that is lower than the existing methods. The MATLAB platform is used to implement the proposed method, and its efficacy is assessed in comparison to established techniques like modified PSO (MPSO), genetic algorithm (GA), particle swarm optimization (PSO), and proportional integral controller (PI) (MPSO).

As power plants age, they will gradually lose their reliability, economic viability, and productivity. They will also emit more carbon dioxide when producing electricity. This study has addressed the retirement and recuperation of the power plants in order to tackle the generation expansion planning (GEP) problem. Recuperation is a factor that benefits the power generating company both environmentally and economically. These requirements have increased the complexity of the GEP issue. Therefore, the utilization of optimization techniques is necessary to address these intricate, limited, and extensive issues. The GEP problem for the Tamil Nadu power system was solved in this study by using one of the most successful optimization techniques, namely particle swarm optimization (PSO), and its variations, such as cooperative coevolving particle swarm optimization (CCPSO) and opposition-based learning competitive particle swarm optimization (OBLCPSO). The real-world GEP problem has been resolved for planning horizons of seven years (2020–2027) and fourteen years (2020–2034). The outcomes showed that the CCPSO algorithm outperformed the competition. The most favorable results have been attained in scenario 4. Compared to the GEP problem without retirement and recuperation, the total cost has dropped by 11.07% and CO₂ emissions by 9.48% once retirement and recuperation are considered. According to the simulation results, retirement and recovery are taken into account in GEP, which considerably lowers overall costs and polluting emissions.

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A sustainable society is thought to be greatly aided by hydrogen (H₂) energy as it is a clean and efficient energy source in light of the impending energy revolution and global climate change. Identifying and implementing green H₂ production methods is made considerably more difficult by the need for a gradual switch to renewable energy. To address these issues, this study proposes a novel energy management approach for hybrid renewable energy resources (RES) systems using multiple H₂ production methods. The proposed approach combines the osprey optimization algorithm (OOA) with a radial basis function neural network (RBFNN), known as the OOA-RBFNN technique. The principal purpose of the proposed strategy is to minimize net system costs. Specifically, OOA is used to lessen the operational cost of a hybrid microgrid consisting of RES. RBFNN is used to predict uncertain renewable energy generation and demand. This work aims to present a strategy for producing hydrogen from solar and wind energy while reducing system costs by using water electrolyzer. The OOA-RBFNN technique is used to define the optimal size and operating energy management of the system. The proposed technique was implemented in the MATLAB platform and compared with various existing techniques like the salp swarm algorithm, convolutional neural network and random forest algorithm. The computation time of the proposed approach is 0.8 s which is lower, and the cost for energy is 23.22$ which is lower than the existing methods.

Battery electric vehicles play a crucial role in reducing air pollution; yet, their adoption is hindered by range limitations. This study examines the impact of weather conditions and temperatures on BEV range and battery consumption on smooth roads using a MATLAB Simulink model. Four scenarios—summer, spring, rainy, and winter—were simulated using the world harmonized vehicle cycle over 2000s, measuring state of charge, mean speed, and distance covered. According to the results, spring offers the best circumstances for BEV efficiency at a distance of 3.35 km, with summer following closely behind at 3.349 km. Rainy weather, on the other hand, results in the largest battery use, which is over four times greater than in the summer and covers 3.2 km. With a distance of 3.31 km, winter circumstances also lead to decreased efficiency. The findings reveal that increased friction and lower temperatures in rainy and winter conditions notably increase battery consumption. These findings highlight the importance of integrating weather and temperature considerations into BEV design and standards for improving thermal management and battery technologies to advance sustainable transportation.

Recent years have seen a strong push to incorporate a wider variety of renewable sources (RS) into modern power systems. The intermittent nature of these renewable sources presents a vital challenge. Experts and researchers must develop adaptable and robust planning strategies to successfully integrate with security higher levels of wind and solar power into the grid. This research presents a stochastic optimal power flow (SOPF) strategy designed to mitigate the intermittent nature of multiple wind power sources by effectively coordinating them with multiple shunt (SVCs) based on FACTS technology. To accurately solve complex problems with multiple conflicting objective functions, a hybrid method combining the Pelican Optimizer (PO) and Coati Optimization Algorithm (COA) is effectively applied to optimize various objective functions, including total cost, power loss, voltage deviation, margin loading stability and contingencies. The main particularity of the proposed hybrid method, namely POCOA, compared to the standard PO and to the COA is related to its high ability to create flexible balance between exploration and exploitation during search process, which makes the POCOA more accurate to locate the near global solution at a competitive time. The proposed POCOA was validated on unimodal and multimodal benchmark functions, as well as the modified IEEE 30-Bus electric test system. Comparative study with other recent techniques confirmed its high competitive aspect in terms of solution quality and convergence behaviors.

Aiming at the problems of accuracy degradation and slow convergence speed of traditional intelligent optimization algorithms in solving distribution network fault localization. A spiral search based multi-strategy dung beetle optimization (SMSDBO) algorithm is proposed for active distribution network fault localization. First, the hierarchical topology model of distribution network with fault tolerance is constructed, and all the segments and nodes of the distribution network are divided into different regions according to the principle of equivalence. Second, the population is initialized by logistic-Tent chaotic mapping to make the population distribution uniform, and an improved sinusoidal algorithm is added to balance the global and local search ability. Then, incorporating the spiral search strategy into the algorithm helps the algorithm to jump out of the local optimum at a later stage. Simulation experiments on distribution networks in MATLAB. Simulation results show that the combination of the SMSDBO algorithm and the hierarchical model has superior localization capabilities in single-fault, multi-fault, and information distortion fault localization. The accuracy and speed are better than the comparison algorithm and traditional model.

The electrification and extension of conventional grid in remote areas is still a major challenge in developing countries. This can be addressed with an integration and management of renewable energy sources and energy storage systems to the remote network. This paper aims to develop a Rule-based Smart Energy Management System (RBSEMS) paradigm for Remote area power supply (RAPS) systems to implement simultaneous source-side and demand-side energy management. The uninterrupted power supply and reduction of electricity cost is the primary objective of the work. Besides, in the multi-objective framework, reduction of dependency on the grid is considered along with the primary objective. The remote area power system controllers are modelled to provide a seamless transition between different modes of operation of energy sources and respond to RBSEMS signals without much delay. The RAPS test system consisting of schedulable and non-schedulable loads, solar PV, wind energy system, battery energy storage system, utility grid along with its controllers are modelled in MATLAB Simulink to validate the RBSEMS. The comprehensive analysis and simulation results of various cases present the effectiveness of the proposed approach on the modelled RAPS system. Four performance indices are also presented to highlight the merits of the proposed work. The overall cost is decreased by 11.56% in case 2B, when primary objective is considered alone. When primary and secondary objectives are considered together, the overall cost is decreased by 3.69% in case 3B but the independent performance index is improved from 0.7815 to 0.836 indicating the reduced grid dependency. The response time of the modelled local controllers of the system is found to be 24.2 ms, which is acceptable for an interval of 0.25 s.

Industrial robots play a vital role in manufacturing systems, engaging in tasks such as welding, painting, and assembling. To prevent catastrophic manufacturing stoppage, it is essential to diagnose faults in control cables of robots in time. This paper proposes a hybrid fault diagnosis method that integrates a robot dynamic model with deep learning-based fault diagnosis to classify the severity of cable faults. Specifically, the proposed method incorporates both the measured torques obtained from measured currents and nominal torques from the dynamic model, achieving robust cable fault diagnosis under varying operating conditions. The measured cable current signals that contain the fault information are used to calculate the joint torques, and a robot dynamic model is used to obtain the nominal joint torques using joint angles and angular velocities. Subsequently, a stacked transformer encoder-based classifier is constructed with the obtained torque disparities as inputs and fault severity probabilities as outputs. Experimental results validate that the proposed fault diagnosis method provides higher accuracy compared to existing methods, highlighting the efficacy of integrating a dynamic model with learning-based fault diagnosis. Furthermore, we conducted a quantitative and qualitative comparison between our proposed method and other recent fault diagnosis methods.

High proportion of distributed photovoltaic integration into power system has led to power system presenting weak or extremely weak power grid state. Under weak power grid or grid harmonic background high penetration distributed photovoltaic GFL converters are prone to lead to system instability. To suppress distributed photovoltaics grid connection resonance, ILADRC method multiple parallel photovoltaic storage GFL VSG system control strategy is proposed. Firstly, stability analysis of single photovoltaic energy storage GFL VSG system and multiple parallel photovoltaic energy storage GFL VSG system is, respectively, performed. Through impedance stability analysis, it can be concluded that multiple parallel photovoltaic energy storage GFL VSG system is prone to resonance in weak power grid or grid harmonic background. Secondly, to suppress system resonance, ILADRC GFL VSG controller is designed, and ILADRC photovoltaic energy storage GFL VSG system impedance model is established for stability analysis. System output impedance amplitude of LADRC method is larger than that of LADRC/unimproved method, and it has a stronger ability to attenuate harmonics in the power grid. Finally, ILADRC multiple parallel photovoltaic energy storage GFL VSG simulation model and hardware in the loop experimental platform are established for tests. By tests shows that harmonic content of unimproved method system is, respectively, as high as 26.57% and 24.29% under weak power grid and grid harmonic background, LADRC method system harmonic content is, respectively, 6.78% and 10.57% under weak current and grid harmonic background, ILADRC method system harmonic content is, respectively, reduced to 1.55% and 2.55% under weak power and grid harmonic background. This indicates ILADRC method system has better resonance suppression ability under weak current net or grid harmonic background, compared to LADRC/unimproved method.