A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

Kim, Sunuwe; Jo, Soo-Ho; Kim, Wongon; Park, Jong-Min; Jeong, Jin Gyo; Han, Yeongmin; Kim, Daeil; Youn, Byeng D.

doi:10.1109/access.2020.3027830

Cited by 24 publications

(8 citation statements)

References 52 publications

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“…Statistical-related methods, such as fuzzy theory [9], correlation analysis [10], hidden Markov model [11], support vector machine [12], and time series [13], have played an important role in transformer fault diagnosis models. With the development of machine learning techniques, artificial neural networks (ANN) [14], extreme learning machines (ELM) [15], back-propagation neural networks (BPNN) [16], and adaptive encoders [17], many other supervised and unsupervised neural network models are used in the field of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

et al. 2021

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Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

et al. 2021

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“…SOA performs the migration behavior using Eqs. ( 13), ( 15) and (17). Then, the linear inertia weight A (Eq.…”

Section: Tisoa 1) Summary Of Improvement Methodsmentioning

confidence: 99%

“…The above methods only reflect the health of transformers and cannot identify specific fault types, so the proposed method cannot be applied to accurate fault identification. [17]. Tahir et al produced an intelligent monitoring and classification algorithm for detecting transformer winding faults based on frequency response analysis (FRA).…”

Section: Introductionmentioning

confidence: 99%

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Sun

Zhang

et al. 2022

IEEE Access

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The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.INDEX TERMS Power transformer, fault diagnosis, kernel principal component analysis, support vector machine, hybrid improved seagull optimization algorithm.

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“…In 2020, a semi-supervised autoencoder with an auxiliary task (SAAT) was introduced by Kim et al to extract a health feature space for power transformer fault diagnosis considering DGA [92]. The DGA dataset was provided by Korea Electric Power Corporation.…”

Section: Dissolved Gas Analysis Using Deep Learningmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

Cited by 24 publications

References 52 publications

Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Deep Learning in High Voltage Engineering: A Literature Review

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