Toward a Comprehensive Evaluation on the Online Methods for Monitoring Transformer Turn-to-Turn Faults

Abstract

Transformer winding turn-to-turn fault is the prominent cause of transformer total failure, so detecting the winding fault in real time to stop the failure development in advance is imperative. However, existing techniques entailing periodic offline inspections fail to continuously monitor transformer winding states while causing extra costs due to the outage during inspections. This has driven researchers to consider effective continuous online monitoring methods from several technical perspectives, including typically port voltage current analysis, online frequency response analysis, and vibration analysis. Since these methods are conventionally evaluated with qualitative comparisons focusing only on feasibility, quantitative assessments indispensable for the targeted improvement of the methods and the most suitable method decision in specific scenarios are still missing. To this end, we conduct a comprehensive evaluation on the three methods by leveraging both experiment and theoretical analysis. Specifically, a customized experiment platform has been designed to support data acquisition under different operating conditions. As conventional feature mining algorithms cannot process the monitoring data produced by different methods in a uniform manner, a feature extraction algorithm leveraging image mining is proposed to extract data features after mapping the test data into a high-dimensional image. This novel algorithm allows us to fully assess several fundamental aspects (i.e., sensitivity, repeatability, and anti-interference capability) of these monitoring methods.