LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines

With ever-increasing awareness on quality and reliable power distribution, the research in the area of automation of distribution system has great relevance from the practical point of view. Electric power utilities throughout the world are more and more adopting computer aided control, monitoring and management of electric power distribution system to offer improved services to the consumers of electricity. The purpose of on-line condition monitoring of cables or any electrical equipment is to predict possible failures before they actually occur. With phenomenal growth of distribution network even to remote areas, the traditional methods of inspecting the lines by foot-patrolling and pole-climbing to check them in close proximity do not seem to be viable. Since the damaged insulators of the distribution system affects the performance of distribution system significantly in terms of reduction in voltage, aerial patrolling has been adopted in developed countries for the purpose of insulator monitoring. The development of an efficient and alternative method for insulator condition monitoring uses image processing and machine learning techniques and is found to be a sustainable method. This work covers automatic defect detection and classification of insulator systems of electric power lines using vision-based techniques.