Research Area:  Machine Learning

Abstract:

This paper proposes a meta ensemble learning method to extract multiple electrical and non-electrical features, including acoustic signal, voltage, current and temperature, etc., for the status monitoring and fault diagnosis of direct-buried transformer substation (DBTS). The proposed method consists of a meta learning model and a sub-classifier model. In the meta learning model, a multi-input and multi-output back propagation neural network (BPNN) with multiple hidden layers is utilized to dynamically update the integrated weight coefficients of sub-classifiers based on the extracted multiple features of DBTS for accuracy improvements. The sub-classifier model involves four pre-trained classifiers using XGBoost for the fault type classification. Consequently, the fault type can be identified by weighted summation of integrated weights and classification results from the sub-classifier model. Comparative studies have been investigated to demonstrate the superior performance of the proposed meta ensemble learning method in improving the accuracy of DBTS fault diagnosis.

Keywords:  
Fault diagnosis
Backpropagation
Substations
Neural networks
Voltage
Feature extraction
Transformers

Author(s) Name:  Yang He; Bin Zhou; Siyuan Guo; Yun Yang; Yue Xiang

Publisher name:  IEEE

DOI:  10.1109/ICPSAsia52756.2021.9621612

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9621612