Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Signal Processing, Artificial Intelligence
6
Scopus Publications
Scopus Publications
Combined fuzzy local binary pattern and wavelet transform features for defect detection of 11/33 kV overhead power line insulators International Journal of Innovative Technology and Exploring Engineering, 2019
Automatic vehicle license plate recognition using LabVIEW Journal of Advanced Research in Dynamical and Control Systems, 2018
Condition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP-HF features Potnuru Surya Prasad, Bhima Prabhakara Rao Iet Generation Transmission and Distribution, 2017 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.
Review on Machine Vision based Insulator Inspection Systems for Power Distribution System P. Surya Prasad, B. Prabhakara Rao, and Journal of Engineering Science and Technology Review, 2016 In the present world, there is a great necessity to have reliable and quality power distribution, and so there is great scope for research on automation of distribution system. The main objective of this paper is to analyze and comprehend different machine learning and image processing based algorithms to find a practical solution for automated inspection of overhead power line insulators. This method is a relatively new approach for. This paper also highlights the constraints and limitations that are present in the various existing methodologies to achieve the objective. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. The few techniques like artificial neural networks (ANN), Hidden Markov Model (HMM), k-means clustering, Wavelet transform features, S-transform features, and support vector machines (SVM) applied in the domain of condition monitoring of the insulators were presented.
Tracking of multiple objects using MPEG-7 visual standards V. Sreerama Murthy, P. Surya Prasad, Sumit Gupta, D.K. Mohanta Proceedings International Conference on Computational Intelligence and Multimedia Applications Iccima 2007, 2008
