Analysis of the Influence of Various Types of Generation on the Formation of a Daily Schedule of Electrical Loads of the Power System, Taking Into Account Abnormal and Irregular Time Intervals
{"title":"Analysis of the Influence of Various Types of Generation on the Formation of a Daily Schedule of Electrical Loads of the Power System, Taking Into Account Abnormal and Irregular Time Intervals","authors":"N. Senchilo","doi":"10.1109/ElConRus51938.2021.9396394","DOIUrl":null,"url":null,"abstract":"Today, a complex-closed electric power system includes many generators of various capacities and types, as well as consumers with various load patterns. The balance of generated and consumed power is the most important parameter for the functioning of the system, therefore, in all countries, the practice of planning consumption and generation is applied, at least, a day in advance. However, emergency outages, abnormally high loads during mass events and other factors upset the balance of power systems. More accurately predict the behavior of the system during abnormal periods is an important task for the functioning of the power system. For such purposes, methods of mathematical modeling and machine learning are used in order to process large amounts of data and identify non-obvious patterns and further forecast anomalous periods in the load graph of the power system.","PeriodicalId":447345,"journal":{"name":"2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ElConRus51938.2021.9396394","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Today, a complex-closed electric power system includes many generators of various capacities and types, as well as consumers with various load patterns. The balance of generated and consumed power is the most important parameter for the functioning of the system, therefore, in all countries, the practice of planning consumption and generation is applied, at least, a day in advance. However, emergency outages, abnormally high loads during mass events and other factors upset the balance of power systems. More accurately predict the behavior of the system during abnormal periods is an important task for the functioning of the power system. For such purposes, methods of mathematical modeling and machine learning are used in order to process large amounts of data and identify non-obvious patterns and further forecast anomalous periods in the load graph of the power system.