Fault Diagnosis of Power Transformers Based on Comprehensive Machine Learning of Dissolved Gas Analysis

Guo, Chenxi; Dong, Ming; Wu, Zihui

doi:10.1109/icdl.2019.8796553

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Thus, it could be said that the high acetylene values were due to OLTC gas contamination. Transformers 19,20,21,22,23,24, 25 and 27 were defined as contaminated.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…The DT classifier was the algorithm selected to be trained based on the accuracy results collected in [20,23,25]. The software used to develop the classifier algorithm was MATLAB R2018b [35].…”

Section: Algorithm Developmentmentioning

confidence: 99%

“…Machine learning (ML) techniques are widely used to assess the condition of a transformer [16][17][18][19][20][21][22] and identify potential faults in a transformer [23][24][25][26][27][28] using DGA, oil quality analysis (OQA), the furfuraldehyde (FFA) content of the oil, polarisation and depolarisation current (PDC) measurements and dielectric frequency domain spectroscopy (FDS). Through the application of ML algorithms, models are generated that create predictions of the transformer health status and automatically diagnose potential faults in the transformer insulation from the data provided.…”

Section: Introductionmentioning

confidence: 99%

“…According to [20,23,25], a decision tree (DT) classifier is easy to interpret because the output is a set of conditional if-then tests of the input data. In addition, it is easy to train, and it works better than other ML techniques.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Transformer Oil Contamination from the OLTC Gases in the Power Transformers of a Distribution System Operator

et al. 2020

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Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.

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“…Thus, it could be said that the high acetylene values were due to OLTC gas contamination. Transformers 19,20,21,22,23,24, 25 and 27 were defined as contaminated.…”

Section: Study Characteristicsmentioning

confidence: 99%

Section: Algorithm Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determination of Transformer Oil Contamination from the OLTC Gases in the Power Transformers of a Distribution System Operator

et al. 2020

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“…Although good results have been achieved, the problem of data coupling has not been solved. The diagnostic accuracy under the condition of less data needs to be discussed [9]. De Andrade Lopes S M et al introduced a new application based on deep neural network, sampling a few oversampling methods to solve the problem of less data sets [10].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment

Zhang

Chen

2022

IEEE Access

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Aiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA algorithm cascade MDS algorithm to process the DGA data is proposed. Subsequently, the TSSA-RF model is introduced to classify the DGA data. The DGA data is first mapped to a high-dimensional space. Next, the optimal feature subset is encoded using the SSA algorithm to reduce irrelevant and redundant features. In this study, the correlation between the optimal feature dimension and the transformer fault diagnosis accuracy is investigated. the expression of the optimal feature subset is obtained by decompiling the SSA operator. The pre-processed data are classified using the RF model, and the TSSA -RF model for classifying the DGA data is found with the highest accuracy through the comparison of different optimization algorithms. After the RF model is optimized using the TSSA algorithm, its accuracy increases by 7.89%, and the accuracy of the TSSA -RF model is obtained as 92.11%. The example results show that compared with the original data, the proposed data processing algorithm improves the diagnostic accuracy of transformer by 11.97 % in the RF model. Compared with multiple preprocessing methods, SSA-MDS has the highest accuracy. Compared with the original data, the accuracy of TSSA-RF model increases by 11.64 %.

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Classification of Fault and Stray Gassing in Transformer by Using Duval Pentagon and Machine Learning Algorithms

et al. 2022

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Fault Diagnosis of Power Transformers Based on Comprehensive Machine Learning of Dissolved Gas Analysis

Cited by 12 publications

References 2 publications

Determination of Transformer Oil Contamination from the OLTC Gases in the Power Transformers of a Distribution System Operator

Determination of Transformer Oil Contamination from the OLTC Gases in the Power Transformers of a Distribution System Operator

Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment

Classification of Fault and Stray Gassing in Transformer by Using Duval Pentagon and Machine Learning Algorithms

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