{"title":"通过强化学习增强稳定性:负荷频率控制案例研究","authors":"Sara Eftekharnejad, A. Feliachi","doi":"10.1109/IREP.2007.4410552","DOIUrl":null,"url":null,"abstract":"A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).","PeriodicalId":214545,"journal":{"name":"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Stability enhancement through reinforcement learning: Load frequency control case study\",\"authors\":\"Sara Eftekharnejad, A. Feliachi\",\"doi\":\"10.1109/IREP.2007.4410552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).\",\"PeriodicalId\":214545,\"journal\":{\"name\":\"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IREP.2007.4410552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IREP.2007.4410552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stability enhancement through reinforcement learning: Load frequency control case study
A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).
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