RIA: High-Breakdown Point Estimation in Electric Power Systems

稳健估计理论对电力系统的安全和经济运行具有重要意义。它的主要关注点是估计器的设计，这些估计器对假设的偏离是稳健的，例如，由于糟糕的测量。该研究的第一个目标是将这一理论推广到电力系统，电力系统由包含稀疏矩阵的模型来表示。特别是，为了解释稀疏回归模型的局部性质，必须推广定义为估计者可以处理的最大污染比例的重要概念--断裂点。基于这一概念，将开发出将高故障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法，目标是开发可在控制中心的实时环境中实现的算法。这项研究的第三个目标是调查最适合不良数据识别的仪表放置方法。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，可以显著改善用于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.