performance analysis of sparse recovery models for bad data detection and state estimation in electric power networks

• DOI: 10.48550/arxiv.1606.02066
• Publication Date: 2016-07-01
• Authors: Junwei Yang; Boming Zhang; Wenchuan Wu; Weiye Zheng; Wenjun Xian
• Keywords: Optimization and Control (math.OC); 0211 other engineering and technologies; FOS: Mathematics; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Mathematics - Optimization and Control

This paper investigates the sparse recovery models for bad data detection and state estimation in power networks. Two sparse models, the sparse L1-relaxation model (L1-R) and the multi-stage convex relaxation model (Capped-L1), are compared with the weighted least absolute value (WLAV) in the aspects of the bad data processing capacity and the computational efficiency. Numerical tests are conducted on power systems with linear and nonlinear measurements. Based on numerical tests, the paper evaluates the performance of these robust state estimation mod-els. Furthermore, suggestion on how to select parameter of sparse recovery models is also given when they are used in elec-tric power networks.