{"title":"基于人工神经网络的电力系统网络攻击保护","authors":"Md. Shahidul Islam, Ruet, Shafia Sultana, Md. Motakabbir Rahman","doi":"10.18034/ei.v7i2.478","DOIUrl":null,"url":null,"abstract":"Impacts of frequency and voltage disturbance on an isolated power system caused by cyber-attack have been discussed, and a neural network-based protective approach has been proposed in this research work. Adaptive PID controllers for both load frequency control and automatic voltage regulator have been implemented using an artificial neural network-oriented by genetic algorithm. The parameters of the PID controller have been tuned offline by using a genetic algorithm over a wide range of system parameter variations. These data have been used to train the neural network. Three input switch has been implemented to control governor speed regulation and amplifier gain. For load frequency control neural network tuned PID controller mitigate frequency disturbance 48% faster than manually tuned PID and for the automatic voltage regulator, neural network tuned PID controller mitigate voltage disturbance 70% faster than manually tuned PID during cyber-attack.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Protection of Power System during Cyber-Attack using Artificial Neural Network\",\"authors\":\"Md. Shahidul Islam, Ruet, Shafia Sultana, Md. Motakabbir Rahman\",\"doi\":\"10.18034/ei.v7i2.478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Impacts of frequency and voltage disturbance on an isolated power system caused by cyber-attack have been discussed, and a neural network-based protective approach has been proposed in this research work. Adaptive PID controllers for both load frequency control and automatic voltage regulator have been implemented using an artificial neural network-oriented by genetic algorithm. The parameters of the PID controller have been tuned offline by using a genetic algorithm over a wide range of system parameter variations. These data have been used to train the neural network. Three input switch has been implemented to control governor speed regulation and amplifier gain. For load frequency control neural network tuned PID controller mitigate frequency disturbance 48% faster than manually tuned PID and for the automatic voltage regulator, neural network tuned PID controller mitigate voltage disturbance 70% faster than manually tuned PID during cyber-attack.\",\"PeriodicalId\":49736,\"journal\":{\"name\":\"Nuclear Engineering International\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2019-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nuclear Engineering International\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.18034/ei.v7i2.478\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Engineering International","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.18034/ei.v7i2.478","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Protection of Power System during Cyber-Attack using Artificial Neural Network
Impacts of frequency and voltage disturbance on an isolated power system caused by cyber-attack have been discussed, and a neural network-based protective approach has been proposed in this research work. Adaptive PID controllers for both load frequency control and automatic voltage regulator have been implemented using an artificial neural network-oriented by genetic algorithm. The parameters of the PID controller have been tuned offline by using a genetic algorithm over a wide range of system parameter variations. These data have been used to train the neural network. Three input switch has been implemented to control governor speed regulation and amplifier gain. For load frequency control neural network tuned PID controller mitigate frequency disturbance 48% faster than manually tuned PID and for the automatic voltage regulator, neural network tuned PID controller mitigate voltage disturbance 70% faster than manually tuned PID during cyber-attack.