REAL TIME POWER QUALITY EVENT DETECTION

Abstract

The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. For improving the power quality continuous monitoring of power is needed which is being delivered at customer’s sites. Therefore, detection of Power Quality Disturbances (PQD), and proper classification of PQD is highly desirable. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQD. In this work S-Transform and Histogram of Oriented Gradients are used for detection of PQD. A number of power quality events are generated and S-Transform and Histogram of Oriented Gradients(HOG) are applied for accurate detection of disturbances. It is also observed that when the PQD are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Distinct features common to all PQD like Energy, Standard deviation (SD) are extracted and are fed as inputs to the classifier system for accurate detection and classification of various power quality disturbances.