Fuzzy Logic Application in DGA Methods to Classify Fault Type in Power Transformer

Genc, Secil; Karagöl, Serap

doi:10.1109/hora49412.2020.9152896

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Artificial intelligence (AI)-based methods have been introduced to improve the diagnosis accuracy and remove the inherent uncertainty in DGA. These methods have been proposed with explorations of artificial neural networks (ANNs) [4,5], fuzzy logic (FL) [6,7], support vector machines (SVM) [8,9], decision trees (DTs) [10], and K-nearest neighbours (k-NNs) [11]. Scikit-learn is well-known as a popular machine learning (ML) library for classification, regression, and clustering problems and is widely used with the Python programming language.…”

Section: Introduction *mentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

2023

JST: SSAD

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The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC (International Electrotechnical Commission) ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, k-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS (California State University Sacramento) method. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most effective type of the input vector and ML classifier for precisely detecting faults in power transformers.

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Section: Introduction *mentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

2023

JST: SSAD

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“…Artificial intelligence (AI) based methods have been introduced to improve the diagnosis accuracy and remove the inherent uncertainty in DGA. These methods were proposed with the use and exploration of artificial neural networks (ANNs) [6,7 ], fuzzy logic (FL) [8,9], support vector machine (SVM) [10,11], decision tree (DT) [12,13] and K-nearest neighbours (KNN) [14,15]. ANNs have been extensively used in ISSN: 2734-9373 https://doi.org/10.51316/jst.160.ssad.2022.32.3.8 Received: June 29, 2022; accepted: August 13, 2022 applications of pattern recognitions as they are adaptive, capable of handling highly nonlinear relationships, and can generalise solutions for new sets of data (unseen data).…”

Section: Introduction *mentioning

confidence: 99%

Dissolved Gas Analysis of Insulating Oil for Power Transformer Fault Diagnosis with Bayesian Neural Network

2022

JST: SSAD

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Dissolved gas analysis is widely used for preventative maintenance techniques and fault diagnoses of oil-immersed power transformers. There are also various conventional methods of dissolved gas analysis for insulating oil in power transformers including methods of Doernenburg ratios, Rogers ratios and Duval’s triangle. The Bayesian techniques have been developed over many years and applied to a range of different fields including the problem of training in artificial neural networks. In particular, the Bayesian approach can solve the problem of over-fitting of artificial neural networks after being trained. The Bayesian framework can be also utilised to compare and rank different architectures and types of artificial neural networks. This research aims at deploying a detailed procedure of training artificial neural networks with the Bayesian inference, also known as Bayesian neural networks, to classify power transformer faults based on Doernenburg and Rogers gas ratios. In this research, the IEC TC 10 databases were used to form training and test data sets. The results obtained from the performance of trained Bayesian neural networks show that despite the limitation of the available dissolved gas analysis data, Bayesian neural networks with an appropriate number of hidden units can successfully classify power transformer faults with accuracy rates greater than 80%.

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“…Researchers are mainly interested in the dynamic evolution of neural systems. In [25,26], fuzzy logic and neural network control problems are explored. As we know, in the early 1980s, Hopfield combined statistical mechanics with systematic learning and proposed the classical Hopfield neural network model, and this model can also be derived through microelectrical components.…”

Section: Introductionmentioning

confidence: 99%

Robustness of Hopfield Neural Networks Described by Differential Algebraic Systems of Index-1 under the Conditions of Deviation Argument and Stochastic Disturbance

Liu

Yang

et al. 2021

Journal of Mathematics

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Robustness refers to the ability of a system to maintain its original state under a continuous disturbance conditions. The deviation argument (DA) and stochastic disturbances (SDs) are enough to disrupt a system and keep it off course. Therefore, it is of great significance to explore the interval length of the deviation function and the intensity of noise to make a system remain exponentially stable. In this paper, the robust stability of Hopfield neural network (VPHNN) models based on differential algebraic systems (DAS) is studied for the first time. By using integral inequalities, expectation inequalities, and the basic control theory method, the upper bound of the interval of the deviation function and the noise intensity are found, and the system is guaranteed to remain exponentially stable under these disturbances. It is shown that as long as the deviation and disturbance of a system are within a certain range, there will be no unstable consequences. Finally, several simulation examples are used to verify the effectiveness of the approach and are described below.

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Fuzzy Logic Application in DGA Methods to Classify Fault Type in Power Transformer

Cited by 9 publications

References 9 publications

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

Dissolved Gas Analysis of Insulating Oil for Power Transformer Fault Diagnosis with Bayesian Neural Network

Robustness of Hopfield Neural Networks Described by Differential Algebraic Systems of Index-1 under the Conditions of Deviation Argument and Stochastic Disturbance

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