‘‘IDENTIFICATION OF JUMPER BOLT PROBLEMS IN TRANSMISSION LINE USING THERMAL IMAGES USING NEURAL NETWORK AND FUZZY C MEANS CLUSTERING TECHNIQUE’’

Abstract: A method for detecting power transmission line bolts and their defects based on positional relationship. Using thermal image and their temperature to identify the problem in jumper bolts on transmission line. Thus existing method we are using which fuzzy c means clustering. In the proposed system first we are taken image deonising of transmission line jumper bolts. Then with help of image deonising we take more image segmentation of thermal image of healthy and faulty jumper bolts. After the image segmentation of transmission line bolts we also can feature extraction. Then the prominent feature selection is used to improve power quality in hybrid distributed power generating systems. The fault classification of the distributed power generating system already tested and it is named as thermos vision which is used to measure an object temperature. The heat energy is converted into thermal image.