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Partial discharges pattern recognition of transformer defect model by LBP & HOG features

Firuzi, K ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/TPWRD.2018.2872820
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. Partial discharge (PD) measurement and identification have great importance to condition monitoring of power transformers. In this paper a new method for recognition of single and multi-source of PD based on extraction of high level image features have been introduced. A database, involving 365 samples of phase-resolved PD (PRPD) data, is developed by measurement carried out on transformer artificial defect models (having different sizes of defect) under a specific applied voltage, to be used for proposed algorithm validation. In the first step, each set of PRPD data is converted into grayscale images to represent different PD defects. Two “image feature extraction” methods, the Local Binary Pattern (LBP) and the Histogram of Oriented Gradient (HOG), are employed to extract features from the obtained gray scale images. Different variants of Support Vector Machine (SVM) are adjusted for optimal classification of PD sources in this process. Impact of the employed parameters in the image processing such as image resolution, random noise and phase shift, on identification accuracy is investigated and addressed. It is shown that by using HOG-SVM method 99.3% accuracy can be achieved. This is hardly affected by various external factors. Two case studies have been conducted on multi-source PD for evaluating the performance of the proposed algorithm. A void defect is implemented into the transformer model and the resultant recorded signal is used for the study. The DBSCAN algorithm is used as the mean of PD source clustering and sub-PRPD pattern development. It is shown HOG-SVM method has superior performance in identifying active sources, under sub-PRPD pattern application. IEEE
  6. Keywords:
  7. Grayscale image ; HOG features ; LBP features ; PCA transform ; Sub-PRPD pattern ; SVM classifier and DBSCAN ; Clustering algorithms ; Condition monitoring ; Defects ; Extraction ; Image resolution ; Image retrieval ; Partial discharges ; Pattern recognition ; Power transformers ; Support vector machines ; Gray-scale images ; PRPD patterns ; SVM classifiers ; Image processing
  8. Source: IEEE Transactions on Power Delivery ; 2018 ; 08858977 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8477152