A Feature Matching Based Method for Fault Diagnosis in Power Systems Under Disaster Weather

doi:10.3969/j.issn.1000-7229.2018.10.005

Abstract

Abstract: Reliability of the communication system in a power system decreases under disaster weather, and then the possibility of communication distortions of alarm messages rises. As a result, it will become more difficult to accurately identify faults in the outage area（s）. The existing research has demonstrated that the fault risk degree of a transmission line can be obtained by analyzing the external environmental data during the line fault. However, this information source is rarely used in existing fault diagnosis methods. Given this background, a power system fault diagnosis method based on fault feature matching using external weather environment information is proposed. First, the fault probability of a transmission line is attained by employing the grey fuzzy theory on the basis of the external weather information collected during a fault. Then, the matching index of the fault feature related to the external weather condition is added to the objective function of the existing analytical model （optimization model） for fault diagnosis, and the fault hypotheses which do not match the corresponding fault features will be punished. Next, the simulated annealing genetic algorithm is used to solve the improved analytical model. Finally, the proposed method is demonstrated by employing fault scenarios in a regional power system in Guangzhou. Simulation results show that the improved fault diagnosis method is more fault-tolerant to communication failures. In addition, the fault diagnosis outcomes with and without employing external weather information are compared and the reasons for their differences analyzed.

Key words: power system, fault diagnosis, disaster weather, feature matching, analytical model

CLC Number:

TM 711

SHANG Huiyu, WANG Chongyu, CHEN Minghui, YANG Zeng, XIONG Wen, WEN Fushuan. A Feature Matching Based Method for Fault Diagnosis in Power Systems Under Disaster Weather[J]. Electric Power Construction, 2018, 39(10): 37-43.

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