Open AccessNovel Probabilistic Neural Network Models Combined with Dissolved Gas Analysis for Fault Diagnosis of Oil-Immersed Power Transformers
Author(s) -
Yichen Zhou,
Lingyu Tao,
Xiaohui Yang,
Yang Li
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c01878
Subject(s) - dissolved gas analysis , artificial neural network , probabilistic neural network , smoothing , engineering , transformer , probabilistic logic , robustness (evolution) , reliability engineering , computer science , data mining , pattern recognition (psychology) , artificial intelligence , transformer oil , electrical engineering , time delay neural network , voltage , biochemistry , chemistry , computer vision , gene
Fault diagnosis technology of power transformers is essential for the stable operation of power systems. Fault diagnosis technology based on dissolved gas analysis (DGA) is one of the most commonly used methods. However, due to the lack of fault information, traditional DGA fault diagnosis techniques are difficult to meet increasing power demand in terms of accuracy and efficiency. To address this problem, this paper proposes a novel fault diagnosis model for oil-immersed transformers based on International Electrotechnical Commission (IEC) ratio methods and probabilistic neural network (PNN) optimized with the modified moth flame optimization algorithm (MMFO). PNN as a radial neural network has good utility and is often used in classification models, but its classification performance is easily affected by the smoothing factor (σ) of the hidden layer and is not stable. This paper addresses this issue using the MMFO to optimize the smoothing factor, which effectively improves the classification accuracy and robustness of PNN. The proposed method was validated by conducting the experiments with the real data collected from transformers. Experimental results show that the MMFO-PNN model improves the fault diagnosis accuracy rate from 70.65 to 99.04%, which is higher than other power transformer fault diagnosis models.