具有动态区间值的随机变量配电网鲁棒状态估计方法

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Power and Energy Systems Pub Date : 2022-01-01 DOI:10.2316/j.2022.203-0393

Li Xin, W. Jiekang, Zeng Shunqi, Cai Jinjian

{"title":"具有动态区间值的随机变量配电网鲁棒状态估计方法","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":"{\"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}","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

摘要

由于测量中的不确定性，使得传统方法的估计精度难以满足配电系统的调度要求。同时，测量系统在采集和传输过程中往往存在不良数据，严重影响状态估计的准确性。利用该方法改善了状态估计对测量不确定性和不良数据的影响，提高了估计的鲁棒性和准确性。将区间分析方法用于描述具有不确定性的测量问题，建立了配电网中状态变量的区间约束模型，并利用线性规划方法得到了状态变量的可行域。基于测量不确定性理论，建立了以测点精度最高为目标函数的鲁棒状态估计优化模型。采用内点法求解状态估计的精确值。以状态变量可行域为约束条件，区间中值为初始值，无需将潮流计算结果作为初始值，从而减小了求解状态变量的范围，减少了计算量。与传统的加权最小二乘法相比，该方法在精度和阻力方面都有显著提高。通过IEEE30和IEEE118系统验证了该方法的可行性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ROBUST STATE ESTIMATION METHOD OF DISTRIBUTION NETWORK FOR STOCHASTIC VARIABLESWITH DYNAMIC INTERVAL VALUES

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Power and Energy Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

1.00

自引率

0.00%

发文量

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.