RIA: High-Breakdown Point Estimation in Electric Power Systems

鲁棒估计理论可以极大地提高电力系统的安全性和经济性。它主要关注的是估计器的设计，它是健壮的，可以防止偏离假设，例如由于糟糕的测量。提出的研究的第一个目标是将这一理论扩展到电力系统，这是由涉及稀疏矩阵的模型表示。特别是，击破点的重要概念，它被定义为估计器可以处理的污染的最大分数，必须推广，以解释稀疏回归模型的局部特性。基于这一概念，将开发出将高击穿点估计器应用于电力系统的算法。这里，将考虑重采样技术和模拟退火方法，目标是开发可以在控制中心实时环境中实现的算法。提出的研究的第三个目标是调查仪表放置方法，这是最优的坏数据识别。这确实是一个重要的问题，因为在电力系统中，测量的冗余度非常低。初步结果表明，通过适当的测量重新分配，可以显著改善不良数据分析的不良测量配置。

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.