Home // International Journal On Advances in Intelligent Systems, volume 6, numbers 3 and 4, 2013 // View article
Authors:
Leandro H. S. Silva
Sérgio C. Oliveira
Eduardo Fontana
Keywords: Partial discharges; rain detection; pattern recognition; leakage current; insulators; sensor network
Abstract:
Partial discharges (PD) on the surface of high voltage insulators are directly related with the accumulation of pollution. A complete partial discharge sensor network was previously developed and has been in operation for approximately three years. This system records the PD activity, classifying it into four levels. As the PD activity is influenced by the weather conditions, each sensor system in the network also measures the one-hour average temperature and relative humidity. Also a fuzzy inference system was developed to extract the flashover occurrence risk level based on the partial discharge activity recorded. However, a strong rain event can wash insulators almost instantaneously, in turn decreasing the risk level. For a correct interpretation of the results it is important to properly analyze the weather data to detect the rain occurrence. This paper presents a comparison among three machine learning techniques for rain detection from humidity and temperature data, namely, Naïve Bayes Classifier, Support Vector Machines and Multilayer Perceptron Neural Network. These are trained on data gathered by meteorological stations located nearby the PD sensors and used in conjunction with the data obtained by the Sensor Network. Studies on the generalization training power and long term data analysis on sensor data are performed and presented.
Pages: 356 to 366
Copyright: Copyright (c) to authors, 2013. Used with permission.
Publication date: December 31, 2013
Published in: journal
ISSN: 1942-2679