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RIA: High-Breakdown Point Estimation in Electric Power Systems

RIA：电力系统中的高击穿点估计

基本信息

批准号：
9009099
负责人：
Lamine Mili
金额：
$ 7万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1990
资助国家：
美国
起止时间：
1990-08-01 至 1993-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9009099&HistoricalAwards=false
关键词：
RIA Breakdown Point Estimation Electric

项目摘要

The security and the economic operation of an electric power system can benefit greatly from robust estimation theory. Its major concern is the design of estimators which are robust against departures from the assumptions, due to bad measurements for instance. The first objective of the proposed research is to extend this theory to electric power systems, which are represented by models involving sparse matrices. In particular, the important concept of breakdown point, which is defined as the maximum fraction of contamination that an estimator can handle, has to be generalized in order to account for the local properties which characterize sparse regression models. Based on this concept, algorithms which apply high-breakdown point estimators to power systems will be developed. Here, the resampling technique and the simulated annealing method will be considered, the objective being the development of algorithms which can be implemented in a real-time environment of a control center. The third objective of the proposed research is the investigation of meter placement methodologies which are optimal for bad data identification. This is indeed an important issue since in power systems the redundancy in the measurements is very low. Preliminary results showed that a poor measurement configuration for bad data analysis can be dramatically improved through appropriate measurement re-allocation.

电力系统的安全与经济运行可以大大受益于鲁棒估计理论。其主要关切是设计的估计，这是稳健的偏离例如，由于测量结果不佳，第一这项研究的目的是将这一理论扩展到电力系统，其中所代表的模型，稀疏矩阵特别是，分解的重要概念点，定义为污染物的最大分数，一个估计器可以处理，必须被概括，以说明描述稀疏回归模型的局部性质。基于这一概念，应用高崩溃点的算法，将开发电力系统的估计器。在这里，技术和模拟退火方法将被考虑，目标是开发可以实现的算法在控制中心的实时环境中。第三个目标建议的研究是调查仪表放置这些方法对于不良数据识别是最佳的。这是这确实是一个重要的问题，因为在电力系统中，测量值非常低。初步结果显示，用于不良数据分析的测量配置可以显著通过适当的测量重新分配来改善。