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Classification of Partial Discharge Signals using Probabilistic Neural Network

Classification of Partial Discharge Signals using Probabilistic Neural Network
Classification of Partial Discharge Signals using Probabilistic Neural Network
Partial Discharge (PD) classification in power cables and high voltage equipment is essential in evaluating the severity of the damage in the insulation. In this paper, the Probabilistic Neural Network (PNN) method is used to classify the PDs. After the algorithm has been trained it uses the input vector, which contains the features that would be used for classification, to calculate the probability density function (pdf) of each class and together with the assignment of a cost for a misclassification the decision that minimizes the expected risk is taken. The maximum likelihood training is employed here. The success of this particular method for classification is asserted. This method has the advantage over Multilayer Neural Network that it gives rapid training speed, guaranteed convergence to a Bayes classifier if enough training examples are provided (i.e. it approaches Bayes optimality), incremental training which is fast (i.e. additionally provided training examples can be incorporated without difficulties) and robustness to noisy examples. The results obtained here (99.3%, 84.3% and 85.5% for the corona, the floating in oil and the internal discharges respectively) are very encouraging for the use of PNN in PD classification.
1-4244-0750-8
609-615
Evagorou, D
f21ff092-4633-4728-aab0-521dd297a187
Kyprianou, A
3ce6d975-3c30-4a93-9d08-59fa5dc00c36
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Stavrou, A
88f5bae6-fb3e-4c97-8acc-6a19508e8807
Efthymiou, V
ed0d632d-ebe4-45d9-9a05-96516f8d56ff
Georghiou, G E
c3e9a8c7-a175-4d3c-aa20-e851d441c30d
Evagorou, D
f21ff092-4633-4728-aab0-521dd297a187
Kyprianou, A
3ce6d975-3c30-4a93-9d08-59fa5dc00c36
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Stavrou, A
88f5bae6-fb3e-4c97-8acc-6a19508e8807
Efthymiou, V
ed0d632d-ebe4-45d9-9a05-96516f8d56ff
Georghiou, G E
c3e9a8c7-a175-4d3c-aa20-e851d441c30d

Evagorou, D, Kyprianou, A, Lewin, P L, Stavrou, A, Efthymiou, V and Georghiou, G E (2007) Classification of Partial Discharge Signals using Probabilistic Neural Network. At 9th IEEE International Conference on Solid Dielectrics 9th IEEE International Conference on Solid Dielectrics, United Kingdom. 08 - 13 Jul 2007. pp. 609-615.

Record type: Conference or Workshop Item (Paper)

Abstract

Partial Discharge (PD) classification in power cables and high voltage equipment is essential in evaluating the severity of the damage in the insulation. In this paper, the Probabilistic Neural Network (PNN) method is used to classify the PDs. After the algorithm has been trained it uses the input vector, which contains the features that would be used for classification, to calculate the probability density function (pdf) of each class and together with the assignment of a cost for a misclassification the decision that minimizes the expected risk is taken. The maximum likelihood training is employed here. The success of this particular method for classification is asserted. This method has the advantage over Multilayer Neural Network that it gives rapid training speed, guaranteed convergence to a Bayes classifier if enough training examples are provided (i.e. it approaches Bayes optimality), incremental training which is fast (i.e. additionally provided training examples can be incorporated without difficulties) and robustness to noisy examples. The results obtained here (99.3%, 84.3% and 85.5% for the corona, the floating in oil and the internal discharges respectively) are very encouraging for the use of PNN in PD classification.

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More information

Published date: 2007
Additional Information: Event Dates: 8 - 13 July 2007
Venue - Dates: 9th IEEE International Conference on Solid Dielectrics, United Kingdom, 2007-07-08 - 2007-07-13
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 264258
URI: https://eprints.soton.ac.uk/id/eprint/264258
ISBN: 1-4244-0750-8
PURE UUID: 05df506a-6e0d-4301-9b68-fc6c15cb33e0

Catalogue record

Date deposited: 04 Jul 2007
Last modified: 18 Jul 2017 07:38

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