{"title":"电能质量趋势数据的动态统计过程控制限制","authors":"Thomas A. Cooke, W. Howe","doi":"10.1109/ICHQP.2018.8378878","DOIUrl":null,"url":null,"abstract":"Statistical process control (SPC) is a well-known method to monitor behavior and control of process parameters through statistical analysis. In power quality (PQ), we can apply this method to PQ parameters such as harmonics, imbalance, and flicker to analyze when values are outside a normal range. However, the normal range for these PQ parameters can vary depending on known conditions relating to time of day, day of the week, or even time of year. To have a tighter set of continuous control during these periods, a dynamic set of limits would be preferred over one static limit to highlight unknown abnormalities. This paper analyzes methods to create dynamic statistical process control limits for PQ data.","PeriodicalId":6506,"journal":{"name":"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)","volume":"16 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Dynamic statistical process control limits for power quality trend data\",\"authors\":\"Thomas A. Cooke, W. Howe\",\"doi\":\"10.1109/ICHQP.2018.8378878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Statistical process control (SPC) is a well-known method to monitor behavior and control of process parameters through statistical analysis. In power quality (PQ), we can apply this method to PQ parameters such as harmonics, imbalance, and flicker to analyze when values are outside a normal range. However, the normal range for these PQ parameters can vary depending on known conditions relating to time of day, day of the week, or even time of year. To have a tighter set of continuous control during these periods, a dynamic set of limits would be preferred over one static limit to highlight unknown abnormalities. This paper analyzes methods to create dynamic statistical process control limits for PQ data.\",\"PeriodicalId\":6506,\"journal\":{\"name\":\"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)\",\"volume\":\"16 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHQP.2018.8378878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP.2018.8378878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic statistical process control limits for power quality trend data
Statistical process control (SPC) is a well-known method to monitor behavior and control of process parameters through statistical analysis. In power quality (PQ), we can apply this method to PQ parameters such as harmonics, imbalance, and flicker to analyze when values are outside a normal range. However, the normal range for these PQ parameters can vary depending on known conditions relating to time of day, day of the week, or even time of year. To have a tighter set of continuous control during these periods, a dynamic set of limits would be preferred over one static limit to highlight unknown abnormalities. This paper analyzes methods to create dynamic statistical process control limits for PQ data.