Power transformers fault diagnosis using AI techniques

Rokani, V.; Kaminaris, Stavrοs D.

doi:10.1063/5.0032820

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…The Rogers Ratio Method involves the examination of four gases that are produced in the oil of a PT, serving as indications of faults [8]. The chemical compounds hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), and acetylene (C 2 H 2 ) are of interest in this context [13,14].…”

Section: The Rogers Ratio Methodsmentioning

confidence: 99%

“…PTFD is accomplished via the use of a coding system that is developed from the boundaries of gas concentrations, expressed in ppm, as given in Table 1. Table 2 provides the gas ratio code for each fault type, indicating the presence of 12 distinct fault categories [8,13]. [8,13].…”

Section: The Rogers Ratio Methodsmentioning

confidence: 99%

“…This occurs when more than one fault arises in a transformer or when the concentration of gases is near the threshold. In order to address this issue and improve the dependability of defect detection, the current research initiative employs an artificial neural network (ANN) [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…To enhance the diagnostic precision of traditional DGA, an artificial neural network (ANN) was developed to improve the diagnostic model's resilience and accuracy. Moreover, it was selected to combine the conventional DGA technique, Roger's Ratio Method, with an ANN, as this method is widely used in the literature and gives accurate predictions for power transformer faults [7,8]. Furthermore, an optimization procedure is conducted to maximize the accuracy and decrease the architectural complexity of the proposed network.…”

Section: Introductionmentioning

confidence: 99%

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Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Rokani,

Kaminaris,

Karaisas

et al. 2023

Mathematics

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Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis.

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Section: The Rogers Ratio Methodsmentioning

confidence: 99%

Section: The Rogers Ratio Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Rokani,

Kaminaris,

Karaisas

et al. 2023

Mathematics

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“…Support vector machine classified sample points near the decision boundary, while a decision tree classified the remaining samples. Study [24] implements an artificial neural network (ANN) to improve the accuracy of Rogers ratio method. Study [25] utilized logistic chaos mapping to enhance the parameters of particle swarm optimization (CPSO) and develop the chaotic particle swarm optimization (CPSO) algorithm.…”

Section: Fault Diagnosismentioning

confidence: 99%

Review on fault diagnosis of power transformer

Geng,

Li,

Gan

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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The power transformer serves as a vital apparatus in the power system, with its steady function being significantly essential for the fluid execution of voltage conversion and distribution. The efficiency and precision of fault diagnosis methodologies can substantially aid in maintaining the consistent and stable operation of the transformer. This paper addresses the progression of transformer winding monitoring technology, touching upon the current investigative status concerning data purification, monitoring parameter forecasting, and the fault diagnosis model amidst the transformer fault diagnosis. It is imperative to fortify the research direction of transformer fault diagnosis technology in forthcoming future endeavors. Delving more profound into the realm of transformer fault diagnosis research has substantial implications in bolstering the efficiency and precision of fault diagnosis, thereby enhancing the operational stability of power transformers.

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