Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN

Liu, Yongxin; Song, Bin; Wang, Linong; Gao, Jiachen; Xu, Rihong

doi:10.3390/app10134440

Cited by 34 publications

(18 citation statements)

References 33 publications

(35 reference statements)

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“…Distribution and power transformers operate in multifarious environments that makes them susceptible to generic failures whose consequence is prolonged episodes of power outages and disrupted economic activity [29]. Unlike overhead conductors that are easy to troubleshoot and repair, transformers are factory sealed thus depriving engineers and technicians the opportunity to inspect their internal components.…”

Section: Methods and Modelingmentioning

confidence: 99%

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

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This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

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Section: Methods and Modelingmentioning

confidence: 99%

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

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“…The differential negative sequence can be expressed as Equation ( 7). I d2 = 1 2 I da + a 2 I db + aI dc (7)…”

Section: Differential Negative-sequence Protectionmentioning

confidence: 99%

“…Power transformers (PTs) are essential to maintain the stable operation of a power grid. There is a massive number of transformers in service, and internal faults occur in the long-term ageing process [1]. However, conducting maintenance with system outage or assessment of the transformer means considering the expenditure and time due to production loss.…”

Section: Introductionmentioning

confidence: 99%

Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm

2021

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A power transformer (PT) in power generation or transmission is critical to maintaining electrical continuity. Fault detection on a PT is needed, especially of incipient faults, which are often caused by a turn-to-turn fault (TTF) before it develops into a more severe fault. We use a hybrid algorithm between conventional and modern techniques to detect a developing fault in a PT. The current response signals from a negative sequence current directional algorithm, extended park vector algorithm (EPVA), differential negative sequence current, and EPVA-fuzzy system are combined to distinguish the possibility of a TTF. The subalgorithms are combined using a hybrid detection algorithm to distinguish the faults. The model is a 10 MVA, three-phase PT with Δ-Y configuration 150/300 kV, simulated using MATLAB Simulink software. The results show that by combining the subalgorithms, several limitations are distinguished within the TTF with a slight increase in accuracy.

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“…The power transformer is one of the most expensive and important pieces of equipment in the electrical system. As such, it plays a major role in the safe and stable operation of the power grid [1]. The failure of a power transformer during operation can result in a serious breakdown of the power grid, leading to interruptions in the transmission and distribution of electricity, environmental damage, risks of explosion and fire, and costly financial losses due to repairs or replacements [2].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

2022

IJIES

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As a major piece of equipment in the electrical power transmission and distribution, the rapid and accurate assessment of emerging or existing internal faults in power transformers is a key factor in the safe and stable operation of the power grid. This paper proposes a hybrid approach to fault diagnosis for these transformers. This approach is based on ensemble bagged tree classification and training subsets obtain by a conventional pre-processing method. Two pre-processing approaches are performed, the first based on the maximum concentrations of the dissolved gas samples and the second based on the minimum concentrations of the dissolved gas samples. For each training subset, an ensemble classifier is constructed with as inputs the Rogers ratios, Gouda ratios, dissolved gas concentrations and their associations. The proposed hybrid methods are established with 475 samples of training dataset, tested on 117 samples dissolved gas analysis (DGA) data and validated on International Electrotechnical Commission (IEC) TC10 database. The performances of the proposed diagnostic models are evaluated and a comparison is done compared with the following diagnostic methods: IEC ratios method (IRM), Rogers ratios method (RRM), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found by computer simulations carried out by the matrice laboratory (MATLAB) software show that, of the two pre-processing approaches used, the one based on the minimum sample concentration gives better results than the one based on the maximum concentration. In terms of fault type, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 94.02%, compared to 92.31% for the best diagnostic model using the maximum concentrationbased pre-processing approach. This is lower than the 97.25% for SOM clusters and 96.58% for GT but higher than the 59.83% for RRM, 81.19% for IRM and 93.16% for TRT. In terms of fault severity, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 81.20%, compared to 74.36% for the best diagnostic model using the minimum concentration-based pre-processing approach. This result is lower than the 83.76% for TRT and 89.74% for GT but higher than the 49.57% for RRM, 66.67% for IRM and 78.90% for SOM clusters.

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Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN

Cited by 34 publications

References 33 publications

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

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