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This paper presents a Rough Set Theory (RST) based approach for predicting the surface contamination level of porcelain type insulators at different environmental condition. The sample insulators have been contaminated by Solid layer Method (SLM) according to IEC60507. Leakage current of the insulators are recorded at different contamination level and at different humidity conditions. In the proposed method, autocorrelation technique is used for feature selection from leakage current because it is well suited for non-stationary leakage current signals and cancels out the effect of noise present in the signal. Rough Set Theory is used for classification of extracted features as it suits well with the correlation technique. Result shows that the proposed method is able to detect the contamination level with acceptable range of accuracy. [ABSTRACT FROM PUBLISHER]

The paper presents a method using deep learning framework based on convolution neural network (CNN), for identification and localization of faults of transformer winding under impulse test. The results show that the proposed method outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the analysis of fault current patterns. A part of the proposed network performs feature learning and the other part classifies the features in a supervised manner. The method is computation intensive but capable of achieving very high degree of accuracy; on an average a margin of more than 7% compared to other published literature till date. [ABSTRACT FROM AUTHOR]

Partial Discharge (PD) is one of the most critical electrical phenomena affecting the life of electrical equipment. Repetitive PD leading to arcing faults is a recurrent problem in many air insulated systems, such as high voltage air-insulated switchgears. Hence, detection and localization of PD inside such electrical equipment is necessary for early prevention of impending failure. Keeping this in mind, this paper proposes a methodology to localize single and multiple PD sources employing two recent developments in signal processing and machine learning techniques. Optical sensors have been used to record the PD data inside a cubical steel box. A cylindrical barrier has been inserted inside this box to emulate geometrical structures inside Switchgears. Mathematical Morphology aided feature extraction has been employed to extract important features from the PD signal. Sparse Representation classification has been employed to classify the extracted features and to identify the type and location of PD source. The results show that this methodology gives very high classification accuracy corresponding to both single and multiple PD sources. [ABSTRACT FROM AUTHOR]