Discrimination Between Internal Faults and Other Disturbances in Transformer Using the Support Vector Machine-Based Protection Scheme

Shah, Ashesh M.; Bhalja, Bhavesh R.

doi:10.1109/tpwrd.2012.2227979

Cited by 105 publications

(60 citation statements)

References 19 publications

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“…Hence, in view of the professionalism, empiricism and complexity of transformer fault diagnosis, the application of EPS-based diagnosis methods has unique advantages [47][48][49][50][51]. Recently, several other approaches or techniques have been proposed for fault diagnosis of transformers, such as Rigatos and Siano's [82] proposed neural modeling and local statistical approach to fault diagnosis for the detection of incipient faults in power transformers, which can detect transformer failures at their early stages and consequently can deter critical conditions for the power grid; Shah and Bhalja [85] and Bacha et al [5] both proposed support vector machine (SVM)-based intelligent fault classification approaches to power transformer DGA. Furthermore, the random forest technique-based fault discrimination scheme [84] for fault diagnosis of power transformers, as well as the multi-layer perceptron (MLP) neural network-based decision [46], vibration correlation-based winding condition assessment technique [86], and induced voltages ratio-based thermodynamic estimation algorithm [73] have been proposed consecutively.…”

Section: Contentmentioning

confidence: 99%

“…In addition to the five main categories of research approaches and techniques summarized above, there are some other intelligent algorithms, such as artificial immune algorithm (AIA) [72,163,176], GA [67,68,196], improved artificial fish swarm optimizer (IAFSO) [197][198][199], PSO [69,77,80], dynamic clustering (DC) [79,81], WA [83,[124][125][126][127], SVM [5,68,72,77,80,85,154,169,170,188,199], BN [87,[166][167][168], information fusion technology [200][201][202], extreme learning machine (ELM) [203][204][205], DL [70,71,105,206,207], optimized neural network [208,209], and evident...…”

Section: Application Of Other Intelligent Algorithms In Dga-based Tramentioning

confidence: 99%

“…Analogously, Liao and Zheng proposed a PSO-least squares support vector regression-based forecasting model on dissolved gases in oil-filled power transformer, in which the least squares-SVM regression model with RBF kernel is established to facilitate the forecasting model, and the PSO is employed to optimize the hyper-parameters needed in least squares-SVM regression. Shah and Bhalja [85] implemented discrimination between internal faults and other disturbances in transformer using the SVM-based protection scheme. Sima et al [154] combined the GRA with fuzzy SVMs to form a novel concentration prediction model of dissolved gases in transformer oil, which has considered the influence of oil temperature and loads on oil-dissolved gases.…”

Section: Ml-based Transformer Fault Diagnosismentioning

confidence: 99%

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Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Cheng

2018

Energies

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Abstract:Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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Section: Contentmentioning

confidence: 99%

Section: Application Of Other Intelligent Algorithms In Dga-based Tramentioning

confidence: 99%

Section: Ml-based Transformer Fault Diagnosismentioning

confidence: 99%

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Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Cheng

2018

Energies

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“…However, it requires a lot of training samples and its convergence rate is too slow. Also, support vector machine(SVM) has advantages in dealing with nonlinear and small sample data classification problems [6,7]. Thus it has made some achievements in transformer fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

et al. 2017

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Abstract.Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO) algorithm and relevance vector machine(RVM). RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.

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“…Many literatures have been reviewed for research proposed. Algorithm for fault diagnosis such as neural network (1)(2) , fuzzy logic (3)(4) , support vector machine (5)(6)(7)(8) , and etc. has been used in many researches.…”

Section: Introductionmentioning

confidence: 99%

Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer

Klomjit¹,

Thongsuk²,

Ngaopitakkul

2014

Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014

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This paper proposes the proper kernel function in a support vector machine (SVM), which is used to classify the internal fault type in power transformer. The Gaussian kernel and polynomial kernel that are two types of non-linear kernel parameter are compared in terms of the average accuracy and time of training process. The results are shown that the polynomial kernel parameter of SVM algorithm is able to classify the internal fault type with satisfactory accuracy, and it takes the average time less than the other. The benefit of using polynomial kernel can be applied for fault diagnosis in future.

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Discrimination Between Internal Faults and Other Disturbances in Transformer Using the Support Vector Machine-Based Protection Scheme

Cited by 105 publications

References 19 publications

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer

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