In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral

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... nt typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault. Based on the aforementioned pattern recognition techniques, distance classifiers, statistical classifiers and artificial intelligence classifiers have been applied to recognize PD. Some examples of the distance classifiers which have been applied are the minimum distance classifier [13] and nearest neighbour classifiers [16]. Statistical classifiers employed are the Bayes classifiers [17] and the recognition rate classifiers [18], while the intelligence classifiers include ANNs [7,10,19-21], fuzzy logic controllers [17], hidden Markov models [22,23], support vector machines [24,25], genetic algorithms [26] and data mining techniques [27]. Among them, the ANN is one of the most successful pattern recognition techniques because of its capability to learn input-output relationships from a few examples. Several ANN techniques applied for PD pattern recognition include the feed-forward neural network using the back-propagation (BP) [10,16,28], the Kohonen self-organizing map (KOH) and learning vector quantization (LVQ) [16,29], adaptive resonance theory [30], counter propagation neural network [31], probabilistic neural network (PNN) [18,32], cellular neural network [33], modular neural network (MNN) [34,35], extension neural network [36], fuzzy neural networks [37], and most recently the ensemble neural network (ENN) [7] . These techniques yield encouraging results, with recognition rates reaching as high as 90% in some instances, when testing was done with unknown PD fingerprints. When applied for PD pattern recognition, the template matching approach (e.g., minimum distance classifier) and the intelligent technique (e.g., ANN) recorded up to 100% recognition rate for some PD fault examples [10, 38] . However, the statistical approach (e.g., principal component analysis) is commonly applied as feature extraction technique of PD data in order to determine the most suitable parameters for classification. The Syntactic approach has never been applied to classify PD fingerprints.