Power System Fault Diagnosis Method Based on Intuitionistic Fuzzy Sets and Incidence Matrices
Ning Shao, Qing Chen, Yuzhan Dong, Wei Ding, Lei Wang
Abstract
Fault diagnosis is critical to ensuring the stable and reliable operation of power systems. Currently, power system fault diagnosis faces two challenges. One is that fault diagnosis methods do not fully consider power system scale and topology changes. The other is that the uncertainty of alarm information yields inaccurate fault diagnosis results. To address the above problems, this article proposes a fault diagnosis method based on intuitionistic fuzzy sets and incidence matrices. First, the fault area is searched, and its incidence matrices are constructed. Then, the intuitionistic fuzzy numbers are assigned to the incidence matrices using proposed assignment rules to form intuitionistic fuzzy incidence matrices while considering the missing alarm information. Finally, the fault diagnosis model based on intuitionistic fuzzy incidence matrices and its reasoning process are utilized to diagnose the fault area. The case studies illustrate that the proposed method has high accuracy and fault tolerance. The fault diagnosis model is independent of the power system scale and can adapt to topology changes. Moreover, the reasoning process is low-complexity and easy to implement. The proposed method is demonstrated to satisfy the demand for online fault diagnosis in large-scale power systems.