Application of radial basis function neural network for differential relaying of a power transformer

Moravej, Zahra; Vishwakarma, D. N.; Singh, S. P.

doi:10.1016/s0045-7906(01)00033-7

Cited by 47 publications

(16 citation statements)

References 9 publications

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“…Its output is high when the input is close to the center and it decreases rapidly to zero as the input's distance from the center increases. The Gaussian function is a popular kernel function and will be used in this algorithm [14].…”

Section: Neural Network Approachmentioning

confidence: 99%

Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

Souahlia¹,

Khmais²,

Châari³

2012

IJCA

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This paper presents an intelligent fault classification approach for power transformer dissolved gas analysis (DGA). Fault diagnosis methods by the DGA and artificial intelligence (AI) techniques are implemented to improve the interpretation accuracy for DGA of power transformers. The DGA traditional methods are utilized to choose the most appropriate gas signature. AI techniques are applied to establish classification features for faults in the transformers based on the collected gas data. The features are applied as input data to fuzzy logic, artificial neural network (ANN) and support vector machine (SVM) classifiers for faults classification. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to evaluate the performance of proposed method. The results of the various DGA methods are classified using AI techniques and the results are compared with the empirical test. In comparison to the results obtained from the AI techniques, the ratios DGA method has been shown to possess the most excellent performance in identifying the transformer fault type. The test results indicate that the SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification.. General Terms

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Section: Neural Network Approachmentioning

confidence: 99%

Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

Souahlia¹,

Khmais²,

Châari³

2012

IJCA

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“…The commercial tools generally address a widespread area rather than a specific area, so extra efforts are needed to apply to a specific area. [5][6][7][8][9][10][11][12][13]. An educational tool that demonstrates useful ANNs' algorithms and structures, such as Incremental Back Propagation, Incremental Back Propagation with momentum, Back Propagation, and Back Propagation with momentum, have been developed by Bayindir and et al [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Tool for ANN Based Power Factor Correction

Sesveren¹,

Bayındır

2013

2013 12th International Conference on Machine Learning and Applications

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This paper describes a case study of a power factor correction technique based on the Artificial Neural Network (ANN). In order to accelerate the training process of ANNs, four learning algorithm, Incremental Back Propagation (IBP), Batch Back Propagation (BBP), Resilient Back Propagation (RBP), and Quick Back Propagation (QBP), were modeled and software that has a graphic user interface was developed. Using the developed software, the training actions of ANNs can be performed according to the inputs. Results show that the developed software can be used as a visual educational tool for training power factor correction using a synchronous motor.

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“…A radial basis function neural network (RBFNN) is the most commonly used neural network for pattern recognition problems, and it is also widely used for fault diagnosis. Moravej et al [9] applied a RBFNN to the differential relaying of a power transformer.…”

Section: Introductionmentioning

confidence: 99%

MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA

Souahlia¹,

Bacha²,

Châari³

2012

International Journal of Electrical Power & Energy Systems

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Application of radial basis function neural network for differential relaying of a power transformer

Cited by 47 publications

References 9 publications

Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

An Intelligent Tool for ANN Based Power Factor Correction

MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA

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