Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation

TL;DR

This paper presents a data-driven approach combining machine learning and data fusion techniques to improve transformer loss of life estimation, enhancing accuracy for power system asset management.

Contribution

It introduces a novel integration of ANFIS and RBF networks with data fusion methods for transformer loss of life estimation using synthesized data.

Findings

Fused ANFIS and RBF outputs improve estimation accuracy.

Ordered Weighted Averaging and Kalman filter effectively combine model results.

Simulation confirms the method's effectiveness and potential for asset management.

Abstract

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process is proposed based on hourly transformer loading and ambient temperature values. This synthesized data is employed to estimate transformer loss of life by using Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function (RBF) network, which are further fused together with the objective of improving the estimation accuracy. Among various data fusion techniques, Ordered Weighted Averaging (OWA) and sequential Kalman filter are selected to fuse the output results of the estimated ANFIS and RBF. Simulation results demonstrate the merit and the effectiveness of the proposed method.