Video surveillance-based insulator condition monitoring analysis for substation monitoring system (SMS)

Video surveillance (VS) of electric power lines along with its accessories such as insulators has emerged as a potential alternative for the traditional practice of on-site physical detection. There has been a paradigm shift in electric substation automation using substation monitoring system (SMS). Since the damaged insulators severely affect the distribution system performance in terms of reduction in voltage as well as flow of leakage currents, therefore, the incorporation of insulator health as an augmented feature in SMS would improve the quality and reliability of power supply. By using information technology, the automation of insulator monitoring of power system is made faster to recover the fault system immediately. This paper presents a methodology for insulator condition analysis based on VS combined with wavelet coefficient differentiator (WCD) for SMS purposes. The case studies and results contained herein corroborate the efficacy of the proposed methodology to dispense with the conventional on-site physical methods, which are not only tedious, but also time-consuming.

[1]  D. I. Jones,et al.  Aerial video inspection of overhead power lines , 2001 .

[2]  S. C. Srivastava,et al.  Technology development and implementation for power distribution automation system , 2004 .

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Josef Kittler,et al.  Defect detection in random colour textures , 1996, Image Vis. Comput..

[8]  D. I. Jones.  Aerial inspection of overhead power lines using video: estimation of image blurring due to vehicle and camera motion , 2000 .

[9]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  D. I. Jones,et al.  A laboratory test-bed for an automated power line inspection system , 2005 .

[11]  Colin H. Hansen,et al.  Detection of broken rotor bars in induction motor using starting-current analysis and effects of loading , 2006 .

[12]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  A. T. Johns,et al.  An overview of the condition monitoring of overhead lines , 2000 .

[14]  C. Papageorgiou,et al.  Object and pattern detection in video sequences , 1997 .

[15]  Theodoros Evgeniou,et al.  A TRAINABLE PEDESTRIAN DETECTION SYSTEM , 1998 .

[16]  Xia Liu,et al.  Pedestrian detection and tracking with night vision , 2005, IEEE Transactions on Intelligent Transportation Systems.

[17]  M. Salama,et al.  SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.

[18]  Thorsten Joachims,et al.  Text categorization with support vector machines , 1999 .

[19]  D. I. Jones,et al.  Camera sightline pointing requirements for aerial inspection of overhead power lines , 2001 .