Affiliation:
1. School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan
Town, Changping District, Beijing, 102206, China
Abstract
Background:
Oil-immersed distribution transformer is an important power transmission
and distribution equipment in the power system. If it fails, it will cause huge economic losses and
safety hazards. It is of great significance to identify and diagnose its faults, find potential faults in
time, and restore normal operation.
Objective:
To detect transformer fault, a transformer fault diagnosis method based on Graph Markov
Neural Networks for Particle Swarm Optimization algorithm (PSO-GMNN) is proposed.
Methods:
Five common dissolved gases in transformer oil are used to construct a 22-dimensional
feature set to be selected, and then the similarity between each feature vector is calculated by using
Mahalanobis Distance. The graph structure is constructed with feature vectors as vertices and similarities
as edges. Finally, the Particle Swarm Optimization algorithm is used to optimize the initial
weights of Graph Markov Neural Networks, and then transformer fault diagnosis is realized.
Results:
The experiments are performed in the environment of Python 3.7, PyTorch 1.6.0, and the
validity of the proposed method is verified by a comparative analysis of the detection accuracy between
the proposed method and existing mainstream methods.
Conclusion:
A transformer fault diagnosis method based on Graph Markov Neural Networks for
Particle Swarm Optimization algorithm is proposed to detect transformer fault, and the experimental
results demonstrate the effectiveness and advantage of the proposed method.
Publisher
Bentham Science Publishers Ltd.
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials