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The increasing demand for the uninterrupted supply of power is equally increasing the necessity to monitor the health of the distribution network. The failure of the network implicates loss of supply and hence incurs huge loss to power distribution utilities. This paper envisages an algorithm to monitor the condition of overhead distribution line insulators with complex background, keeping in view pragmatic considerations. Complex background necessitates an arduous effort to monitor the condition of insulators and requires a powerful technique to monitor and identify the condition of the insulator. Support vector machines (SVM) and Adaptive neuro-fuzzy inference system (ANFIS) are used to estimate the condition of the insulator with the aid of features extracted from Discrete Orthogonal STransform (DOST). The algorithm is developed in MATLAB environment using the image processing toolbox. Superiority of the SVM over ANFIS has been shown from the results for complex background to validate the efficacy of proposed technique. [ABSTRACT FROM PUBLISHER]

Condition analysis of overhead power distribution system insulators using combined support vector machine (SVM) and wavelet multi-resolution analysis (MRA) seems to be promising for distribution system monitoring (DSM) automation to cope with the increasing system complexity. Though system well-being analysis for engineering applications has been used mostly for electric power system reliability studies, the same principle has been extended for assessing the condition of insulators in a distribution system based on the extent of their damage. Video surveillance with fixed cameras provide the required images of power lines along with insulators at regular intervals and same is sent to a control room using remote terminal units (RTUs) for analysis. Not only the health of the insulators, but also the sagging of the lines, breakage of both insulators and lines can be captured with such cameras. This paper mainly focuses on application of wavelet-transform based feature extraction for digital image processing and SVM for subsequent condition analysis of insulators. The most significant contribution of the paper is to compute the condition indices for overhead power distribution line insulators to overcome difficulties related to vehicular applications in video surveillance. The results contained in this paper validate the efficacy of the proposed methodology for wide-scale applications in overhead power distribution system monitoring (DSM) automation. [ABSTRACT FROM AUTHOR]

The diminishing trend of reliability owing to possible power system failures has become a serious concern for the power system as a whole. It has necessitated the development of advanced protection methodologies employing advanced information technology and substation monitoring system (SMS) constitutes an integral part of such methodologies. The proposed scheme for condition monitoring of insulators, which serves as an augmented feature of SMS, aims at alleviating overall system reliability as well as power quality because cracked insulators cause disruption of power, thereby incurring heavy loss to the power system utilities. The image processing based video surveillance (VS) is incorporated to dispense with the cumbersome and time consuming conventional manual on-site detection using discrete orthogonal S-transform (DOST) in conjunction with some intelligent classification algorithms to ascertain the condition of the insulators. [ABSTRACT FROM AUTHOR]