{"title":"ROBUST STATE ESTIMATION METHOD OF DISTRIBUTION NETWORK FOR STOCHASTIC VARIABLESWITH DYNAMIC INTERVAL VALUES","authors":"Li Xin, W. Jiekang, Zeng Shunqi, Cai Jinjian","doi":"10.2316/j.2022.203-0393","DOIUrl":null,"url":null,"abstract":"The uncertainties in measurement make the estimation accuracy of traditional methods difficult to meet the dispatching requirements of distribution systems. At the same time, the measurement system often has bad data during the acquisition and transmission process, which seriously affects the accuracy of state estimation. The method in this paper is used to improve the influence of the state estimation on measurement uncertainty and bad data, and improve the robustness and accuracy of estimation. Interval analysis method is used to describe the measurement problem with uncertainties, the interval constraint model of state variables in distribution network is established, and the feasible region of state variables is obtained using linear programming method. Based on the measurement uncertainty theory, a robust state estimation optimization model with the highest measurement point accuracy as the objective function is established. The precise value of the state estimation is solved by the interior point method. With the feasible region of state variables as constraints and the median value of the interval as the initial value, it is unnecessary to take the calculation results of power flow as the initial value, thus reducing the scope of solving state variables and reducing the amount of calculation. Compared with the traditional weighted least square, this method has a significant improvement in accuracy and resistance. The feasibility and effectiveness of this method are verified by IEEE30 and IEEE118 systems.","PeriodicalId":43153,"journal":{"name":"International Journal of Power and Energy Systems","volume":"1 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Power and Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2316/j.2022.203-0393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The uncertainties in measurement make the estimation accuracy of traditional methods difficult to meet the dispatching requirements of distribution systems. At the same time, the measurement system often has bad data during the acquisition and transmission process, which seriously affects the accuracy of state estimation. The method in this paper is used to improve the influence of the state estimation on measurement uncertainty and bad data, and improve the robustness and accuracy of estimation. Interval analysis method is used to describe the measurement problem with uncertainties, the interval constraint model of state variables in distribution network is established, and the feasible region of state variables is obtained using linear programming method. Based on the measurement uncertainty theory, a robust state estimation optimization model with the highest measurement point accuracy as the objective function is established. The precise value of the state estimation is solved by the interior point method. With the feasible region of state variables as constraints and the median value of the interval as the initial value, it is unnecessary to take the calculation results of power flow as the initial value, thus reducing the scope of solving state variables and reducing the amount of calculation. Compared with the traditional weighted least square, this method has a significant improvement in accuracy and resistance. The feasibility and effectiveness of this method are verified by IEEE30 and IEEE118 systems.
期刊介绍:
First published in 1972, this journal serves a worldwide readership of power and energy professionals. As one of the premier referred publications in the field, this journal strives to be the first to explore emerging energy issues, featuring only papers of the highest scientific merit. The subject areas of this journal include power transmission, distribution and generation, electric power quality, education, energy development, competition and regulation, power electronics, communication, electric machinery, power engineering systems, protection, reliability and security, energy management systems and supervisory control, economics, dispatching and scheduling, energy systems modelling and simulation, alternative energy sources, policy and planning.