FRA-DiagSys: A Transformer Winding Fault Diagnosis System for Identifying Fault Types and degrees Using Frequency Response Analysis

TL;DR

This paper introduces FRA-DiagSys, a transformer fault diagnosis system using a neural network approach to analyze Frequency Response Analysis data, achieving high accuracy in identifying fault types and severities.

Contribution

The study develops a novel neural network-based system for automatic transformer winding fault diagnosis using FRA data, outperforming existing manual and automated methods.

Findings

Optimal models achieved over 99.7% accuracy for fault degrees.

Fault type diagnosis accuracy exceeded 90%.

FRA-DiagSys achieved 100% accuracy for a specific transformer case.

Abstract

The electric power transformer is a critical component in electrical distribution networks, and the diagnosis of faults in transformers is an important research area. Frequency Response Analysis (FRA) methods are widely used for analyzing winding faults in transformers, particularly in Chinese power stations. However, the current approach relies on manual expertise to interpret FRA curves, which can be both skill-intensive and lacks precision. This study presents a novel approach using a Multilayer perceptron model to directly model and analyze FRA data, simulating various winding fault types and degrees in 12-disc winding and 10-disc winding transformers with different connection configurations, resulting in three distinct datasets. Six different Multilayer perceptron architectures were developed, with optimal models achieving recognition accuracies of over 99.7% for diagnosing fault degrees and more than 90% for fault types. Hence, this paper has yielded a model architecture that exhibits commendable performance in diagnosing various fault types and their severities in different models of transformers when utilizing different FRA connection methods. Additionally, a specialized diagnostic system called FRA-DiagSys with two-stage model utilization was developed, achieving 100% accuracy in diagnosing fault types and degrees for a specific winding-10 power transformer, surpassing other diagnostic methods and strategies.