Open AccessCondition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP‐HF features
Author(s) -
Surya Prasad Potnuru,
Prabhakara Rao Bhima
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0836
Subject(s) - overhead (engineering) , electric power system , wavelet transform , support vector machine , overhead line , insulator (electricity) , engineering , histogram , wavelet , computer science , electric power transmission , electronic engineering , power (physics) , reliability engineering , artificial intelligence , electrical engineering , image (mathematics) , physics , quantum mechanics
With the increasing awareness on the reliable distribution of power with good quality, the research in power distribution automation surveillance system has gained prominence. The performance of distribution system is affected significantly by the damaged insulators in numerous ways. With enormous growth in the power distribution network, the traditional methods of examining the lines by manual patrolling and pole climbing to check that in close proximity are not feasible. The blooming field of on‐line condition monitoring of electrical equipment aims at predicting the possible failures before they actually occur. The improvement of a proficient, alternative method to assess the condition of insulators in a power distribution system using image processing and machine learning techniques is found to be a satisfactory method. This study presents a system to automatically monitor the insulator of overhead power distribution lines using extraction of features from wavelet transform as well as local binary pattern histogram Fourier (LBP‐HF) of the insulators and then subsequent condition analysis done by using support vector machine (SVM). The efficacy of the proposed techniques is validated by the results contained in this study and is found to be suitable for real‐time overhead power distribution system monitoring automation.