Use of hidden Markov models for partial discharge pattern classification

Satish, L.; Gururaj, Bharathi

doi:10.1109/14.212242

Cited by 70 publications

(31 citation statements)

References 11 publications

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“…Gulski and Krivda [10] applied different ANN algorithms for PD recognition and the results show that BP provides better recognition result. One of the improvements made to address these problems includes adding a momentum term for faster training but at the cost of extra memory space [22,44]. Despite all these issues, the BP has been widely applied for PD recognition because of its easier implementation and ability to provide better PD recognition result as compared to other ANN algorithms.…”

Section: The Back Propagation Algorithmmentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract: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 content 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.

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Section: The Back Propagation Algorithmmentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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“…The topic of developing systems for automatic interpretation of PD patterns has been an area of active research, since the past few years. Different alternatives, like the use of expert systems [4], neural networks [5], statistical approaches [6], fuzzy logic [7], fractal geometry [8], hid-den Markov models [9], and Fourier transforms [lo] have been examined with varying degrees of success. In many of these systems, the 4-q-n pattern was used as the input.…”

Section: Introductionmentioning

confidence: 99%

Fractal image compression for classification of PD sources

Lalitha

Satish²

1998

IEEE Trans. Dielect. Electr. Insul.

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The fractal image compression technique has an unique feature due to which physical position of blockslregions in the input image can be extracted directly from the compressed data. Applying this technique, +-q-n partial discharge (PD) patterns (treated as an image) are compressed and stored as affine transformations. These transfornations then are used directly to extract the embedded pattern features, which are classified by a neural network. The novel route to PD pattern classification described in this paper thus addresses both the tasks of compression and feature extraction in a single step. The task of compression is essential to store and handle large quantities of pattern data acquired, especially during on-line monitoring of PD in power apparatus. Results presented illustrate that this approach can address satisfactorily the tasks of compression and classification of PD patterns.

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“…It can be seen that these patterns contain sufficient information for discharge discrimination and recognition. On the basis of these types of patterns, e.g., by using the mean pulse height, the pulse count distributions, etc., or the 3-D patterns themselves, a number of approaches and classification methods have gradually appeared for the automation of discharge recognition: expert systems [3, 41, identification functions [5], the hidden Markov models [6], neural networks [7-101 statistical parameters [ll-131, etc. Recently, fractal features were employed for discharge recognition with encouraging results [14]. In this case only two parameters, the fractal dimension and lacunarity [15-181, calculated from 3-D PD patterns sufficiently discriminated among them.…”

Section: Introductionmentioning

confidence: 99%