Different Techniques Of Fault Detection In Electrical Equipements

Abstract

Fault detection of electrical equipment is necessary for maintaining the reliability of electrical system. The degradation of electrical equipment can cause excessive overheating, which can lead to the eventual failure of the equipment. Additionally, failure of equipment requires a lot of maintenance cost, manpower and can also be catastrophic- causing injuries or even deaths. Therefore, the recognition process of equipment conditions as normal and defective is an essential step towards maintaining reliability and stability of the system. The study introduces infrared thermography based condition monitoring of electrical equipment. Manual analysis of thermal image for detecting defects and classifying the status of equipment take a lot of time, efforts and can also lead to incorrect diagnosis results. An intelligent system that can separate the equipment automatically could help to overcome these problems. This research work will be implementing an intelligent classification system for the conditions of equipment using machine learning algorithms. Three sets of features namely first order histogram based statistical, grey level co-occurrence matrix and component based intensity features are extracted by image analysis, which are used as input data for the machine learning algorithms.