Role of insulating barriers during electrical treeing in composite dielectrics using Partial Discharge signature analysis based on Adaptive Probabilistic Neural Network

2012 ◽  
Author(s):  
S. Venkatesh ◽  
S. Mohamed Ghouse ◽  
P. V. Ramamoorthi ◽  
S. Natarajan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Signature Analysis ◽  
Electrical Treeing

Classification of Partial Discharge Signals using Probabilistic Neural Network

2007 ◽  
Author(s):  
Demetres Evagorou ◽  
Andreas Kyprianou ◽  
Paul L. Lewin ◽  
Andreas Stavrou ◽  
Venizelos Efthymiou ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge

scholarly journals 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

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
S. Venkatesh ◽  
S. Gopal ◽  
K. Kannan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Clustering Algorithms ◽  
Research Work ◽  
Experimental Studies ◽  
Multiple Sources ◽  
Graph Theoretic ◽  
Large Sets ◽  
Novel Approach

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.

A COMPOSITE PROBABILISTIC NEURAL NETWORK SYSTEM FOR PD PATTERN RECOGNITION

2005 ◽  
Vol 02 (02) ◽  
pp. 149-165 ◽  
Author(s):  
B. KARTHIKEYAN ◽  
S. GOPAL ◽  
M. VIMALA
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Network System ◽  
Knowledge Based ◽  
Neural Network System ◽  
Insulation Systems ◽  
Discharge Patterns

Partial discharge patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. This paper describes a method for the automated recognition of PRPD patterns using a novel composite neural network system for the actual classification task. This paper elucidates the possible methods of extracting relevant features from the PRPD data in a knowledge based way i.e. according to physical properties of PD gained from PD modeling. This allows the novel complex neural network (NN) system for classification. The efficacy of composite neural network developed using original probabilistic neural network is examined. This innovative methodology of giving inputs to the composite neural network compares favorably with the traditional network architecture used previously for PD pattern recognition.

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