{"title":"基于模糊逻辑的感应电机软起动器起动参数控制","authors":"Sharith Dhar, Md. Saiful Islam","doi":"10.1109/ECCE57851.2023.10101647","DOIUrl":null,"url":null,"abstract":"The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor\",\"authors\":\"Sharith Dhar, Md. Saiful Islam\",\"doi\":\"10.1109/ECCE57851.2023.10101647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.\",\"PeriodicalId\":131537,\"journal\":{\"name\":\"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECCE57851.2023.10101647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECCE57851.2023.10101647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor
The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.