Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

Abstract

In this study, an inter-turn fault diagnosis method is proposed based on deep learning algorithm. 12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms. The selected features are convoluted with the original data, generating a one-dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto-encoder, sparsity of sparse auto-encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the interturn fault detection of the three-phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next-generation smart transformers.

Improved information entropy weighted vague support vector machine method for transformer fault diagnosis

“…Therefore, the more concentrated the volume fraction of the gas is distributed, the more representative it is of that fault type, that is the greater the weight of the gas. In summary, the definition of g here is opposite to that of the entropy value method, and therefore the inverse is taken here, as shown in Equation (18). The weighted vector P (F) of fault F is defined as Equation ( 20):…”

“…In addition, machine learning methods have been applied to fault diagnosis and achieved good results. For example, neural networks [10,11], fuzzy set theory [12], Bayesian networks [13], set pair theory [14], support vector machines [15,16], Parzen window [17] and other methods [18].…”

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

“…An, Liang et al [29] has proposed a modified hidden Markov model (HMM) fault detection method, which can suppress random and unknown interference and solve the diagnosis problem of RV reducer with complex fault. Duan, Hu et al [30], in order to improve the accuracy and robustness of transformer fault diagnosis, a three-phase transformer turn-turn fault diagnosis method based on deep learning algorithm was proposed. Experimental results showed that the method improved the accuracy of fault classification.…”

New correlation features for dissolved gas analysis based transformer fault diagnosis based on the maximal information coefficient

“…Intelligent methods are considered as suitable substitutes for improving the performance compared with rule-based DGA methods, such as artificial neural networks (ANNs) [12][13][14], support vector machines (SVMs) [15,16], and relevance vector machines (RVMs) [17]. With similar characteristics in terms of input, intelligent methods can achieve higher diagnostic accuracy than rule-based methods in certain databases [12,[18][19][20].…”