AUTHOR=Xie Jingyi 

TITLE=Data-Driven Traction Substations’ Health Condition Monitoring via Power Quality Analysis

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.873602

DOI=10.3389/fenrg.2022.873602

ISSN=2296-598X

ABSTRACT=Electrified railway traction substation is an important part of transportation system, the health of its operation condition affects indirectly national economic. Generally, traction substations’ conditions is studied from its power quality, while the nonlinearity of loads and effects from outside environment are factors mainly affecting the accuracy of condition monitoring. In order to recognize the status of traction substations intelligently and governing them with fast measurements, this paper proposed an data-driven approach for recognizing types of power quality problems, and developed a system with intelligent governance strategies. The proposed approach contains two parts. Firstly, it developed a double discrete Fourier transform (DDFT) algorithm to extract valid feature vectors from power data. Then, a well-known data-driven method, support vector machine (SVM), is applied to build classifiers. Finally, based on classification results, a strategy library for power quality problems was built. Industrial data of a real traction substation in Wuhan, China, was tested in experiment. Compared with traditional methods, the proposed approach is validated useful to improve the classification performance of power quality problems, and fast and effective for governance in traction substation.