Orthogonal least square center selection technique – A robust scheme for multiple source Partial Discharge pattern recognition using Radial Basis Probabilistic Neural Network

Venkatesh, S.; Gopal, S.

doi:10.1016/j.eswa.2011.01.115

Cited by 25 publications

(9 citation statements)

References 42 publications

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“…However, this aspect has been circumvented successfully in recent times since versions which have been implemented with appropriate modifications have been developed. Recently, the authors of this research have also successfully utilized a few variants of such modifications for multi-source PD pattern classification [39,40].…”

Section: Probabilistic Neural Network and Its Adaptive Versionmentioning

confidence: 99%

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Venkatesh

Gopal²,

Kannan

2012

Journal of Electrical and Computer Engineering

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Partial discharge (PD) is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

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Section: Probabilistic Neural Network and Its Adaptive Versionmentioning

confidence: 99%

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Venkatesh

Gopal²,

Kannan

2012

Journal of Electrical and Computer Engineering

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“…In [11], the authors reached a maximum of 92% classification accuracy by employing different versions of Probabilistic Neural Network (PNN) with (ψ,q,n) characteristics (phase/time of PD occurrence (ψ), PD pulse magnitude (q) and number of PD counts (n)) as input features. During the training phase, neural network-based studies showed better convergence.…”

Section: Introductionmentioning

confidence: 99%

Development of Hypergraph Based Improved Random Forest Algorithm for Partial Discharge Pattern Classification

et al. 2021

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“…PNN is frequently utilized in many applications, e.g. : medical diagnosis and prediction [18], [19], [20], [21], image classification and recognition [22], [23], [24], multiple partial discharge sources classification [25], interval information processing [26], [27], phoneme recognition [28], email security enhancement [29], intrusion detection systems [30] or classification in a time-varying environment [31].…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

Kowalski¹,

Kusy²

2017

Annals of Computer Science and Information Systems

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Abstract-In this article, the problem of determining the significance of data features is considered. For this purpose the algorithm is proposed, which with the use of Sobol method, provides the global sensitivity indices. On the basis of these indices, the aggregated sensitivity coefficients are determined which are used to indicate significant features. Using such an information, the process of features' removal is performed. The results are verified by the probabilistic neural network in the classification of medical data sets by computing model's quality. We show that it is possible to point the least significant features which can be removed from the input space achieving higher classification performance.

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Orthogonal least square center selection technique – A robust scheme for multiple source Partial Discharge pattern recognition using Radial Basis Probabilistic Neural Network

Cited by 25 publications

References 42 publications

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Development of Hypergraph Based Improved Random Forest Algorithm for Partial Discharge Pattern Classification

Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

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