{"title":"Automatic fault detection and stability management using intelligent hybrid controller","authors":"","doi":"10.1016/j.epsr.2024.111075","DOIUrl":null,"url":null,"abstract":"<div><div>Microgrid allows the integration of remote sources and other flexible loads to raise security concerns. Thus, it is necessary to detect the type of fault to maintain the system's stability. Existing fault detection systems include limitations such as high detection times, inability to process noisy data and discretization issues. To address these issues, a spiking neural network with a self-organizing map is used to produce precise synaptic weights for fault detection in the microgrid. A feature exploration-based spiking neural network can accurately classify faults such as line-to-ground (LG), line-to-line (LL), double line-to-ground (DLG), and three-phase ground (TLG). To mitigate the impact of the fault, a voltage deviation estimation-based control method is used, which employs a three-degree of freedom fractional order proportional integral resonant (3DOF-FOPIR) controller. In order to stabilize the system frequency, the controller sends a control signal to the multi-level inverter based on the measured voltage deviation and fault auxiliary value. This ensures reduced distortions at the output voltage, and thus, it maintains the stability of the microgrid. As a result, when compared to graph-based convolution networks, the proposed method has a higher accuracy of 99.8 % and a lower error in system stability of 55.47 %.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S037877962400960X/pdfft?md5=f03317cd014f1101b76f8596f53de8db&pid=1-s2.0-S037877962400960X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S037877962400960X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Microgrid allows the integration of remote sources and other flexible loads to raise security concerns. Thus, it is necessary to detect the type of fault to maintain the system's stability. Existing fault detection systems include limitations such as high detection times, inability to process noisy data and discretization issues. To address these issues, a spiking neural network with a self-organizing map is used to produce precise synaptic weights for fault detection in the microgrid. A feature exploration-based spiking neural network can accurately classify faults such as line-to-ground (LG), line-to-line (LL), double line-to-ground (DLG), and three-phase ground (TLG). To mitigate the impact of the fault, a voltage deviation estimation-based control method is used, which employs a three-degree of freedom fractional order proportional integral resonant (3DOF-FOPIR) controller. In order to stabilize the system frequency, the controller sends a control signal to the multi-level inverter based on the measured voltage deviation and fault auxiliary value. This ensures reduced distortions at the output voltage, and thus, it maintains the stability of the microgrid. As a result, when compared to graph-based convolution networks, the proposed method has a higher accuracy of 99.8 % and a lower error in system stability of 55.47 %.
期刊介绍:
Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview.
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design.
• Substation work: equipment design, protection and control systems.
• Distribution techniques, equipment development, and smart grids.
• The utilization area from energy efficiency to distributed load levelling techniques.
• Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.