{"title":"Multiclass transient event classification in hybrid distribution network based on co-training of fine KNN and ensemble KNN classifier","authors":"Sannistha Banerjee, Partha Sarathee Bhowmik","doi":"10.1080/23080477.2023.2256531","DOIUrl":null,"url":null,"abstract":"A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2256531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.
期刊介绍:
Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials