Effective Power Quality Disturbances Identification Based on Event-Driven Processing and Machine Learning

Abstract

Power quality (PQ) disturbances cause rigorous issues in smart grids and industries. The identification of PQ disturbances and effective prevention of such events are essential. In this framework, vital aspects are a precise understanding and a real-time treatment of the PQ disturbances. A novel tactic is described in this chapter. It is founded on the basis of event-driven processing, analysis and machine learning for successful and efficient detection of PQ disturbances‎. The definition is based on an sophisticated combination of event-driven signal amplification and segmentation and local extraction of features and categorization to achieve an effective and appropriate precision method.Two rigorous classifiers namely k-Nearest Neighbor (KNN) and Naïve Bias are used for the automatic identification. For a case study, the framework functionality is checked, and findings are reported. Compared to conventional equivalents, the first order of magnitude reduction in the cumulative sample count is accomplished by the invented method. It leads to substantial benefit in the reduction of computational complexity and effectiveness in terms of power consumption and delays in processing. The designed framework also attains an appropriate precision of categorization for the case of four-class PQ disturbances.