{"title":"基于能量差的电网扰动信号识别方法研究","authors":"P. Ji, Quan Zhu","doi":"10.1109/ICoPESA54515.2022.9754482","DOIUrl":null,"url":null,"abstract":"With the rapid development of clean energy generation technology, a large number of distributed power sources are integrated into the local power grid, resulting in the intensification of disturbance signals in the power grid. It is particularly important to effectively classify and identify the disturbance signals in the power grid. This paper presents a recognition method based on energy difference and support vector machine. The method extracts signal feature vectors by calculating the energy difference of power grid signals and sends them to support vector machine for classification. Experiments were carried out on the power grid signals with and without noise. Experiments show that this method can effectively classify and recognize the disturbance signals, and the recognition rate is greatly improved. The effectiveness of the method is proved. This paper provides a theoretical basis for the improvement of power grid quality and intelligent monitoring management in the future.","PeriodicalId":142509,"journal":{"name":"2022 International Conference on Power Energy Systems and Applications (ICoPESA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Power Grid Disturbance Signal Identification Method Based on Energy Difference\",\"authors\":\"P. Ji, Quan Zhu\",\"doi\":\"10.1109/ICoPESA54515.2022.9754482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of clean energy generation technology, a large number of distributed power sources are integrated into the local power grid, resulting in the intensification of disturbance signals in the power grid. It is particularly important to effectively classify and identify the disturbance signals in the power grid. This paper presents a recognition method based on energy difference and support vector machine. The method extracts signal feature vectors by calculating the energy difference of power grid signals and sends them to support vector machine for classification. Experiments were carried out on the power grid signals with and without noise. Experiments show that this method can effectively classify and recognize the disturbance signals, and the recognition rate is greatly improved. The effectiveness of the method is proved. This paper provides a theoretical basis for the improvement of power grid quality and intelligent monitoring management in the future.\",\"PeriodicalId\":142509,\"journal\":{\"name\":\"2022 International Conference on Power Energy Systems and Applications (ICoPESA)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Power Energy Systems and Applications (ICoPESA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICoPESA54515.2022.9754482\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Power Energy Systems and Applications (ICoPESA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoPESA54515.2022.9754482","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Power Grid Disturbance Signal Identification Method Based on Energy Difference
With the rapid development of clean energy generation technology, a large number of distributed power sources are integrated into the local power grid, resulting in the intensification of disturbance signals in the power grid. It is particularly important to effectively classify and identify the disturbance signals in the power grid. This paper presents a recognition method based on energy difference and support vector machine. The method extracts signal feature vectors by calculating the energy difference of power grid signals and sends them to support vector machine for classification. Experiments were carried out on the power grid signals with and without noise. Experiments show that this method can effectively classify and recognize the disturbance signals, and the recognition rate is greatly improved. The effectiveness of the method is proved. This paper provides a theoretical basis for the improvement of power grid quality and intelligent monitoring management in the future.