Performance Analysis of Sparse Recovery Models for Bad Data Detection and State Esti-mation in Electric Power Networks
Optimization and Control
2016-06-08 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1606.02066,
title = {Performance Analysis of Sparse Recovery Models for Bad Data Detection and State Esti-mation in Electric Power Networks},
author = {Junwei Yang and Wenchuan Wu and Weiye Zheng and Wenjun Xian and Boming Zhang},
journal= {arXiv preprint arXiv:1606.02066},
year = {2016}
}