Adaptive Rate Sampling and Machine Learning Based Power Quality Disturbances Interpretation

Abstract

The Power quality (PQ) disturbances causes rigorous issues in classical and smart grids, industries. The performance of power networks can be affected by these intermittent events. The identification of PQ disturbances and an effective prevention of such events are essential. In this framework, vital aspects are a precise understanding as a first step can be followed by a real-time treatment of the PQ disturbances in the future later on. The PQ signals are acquired by using the event-driven A/D converters (EDADCs). The acquired signal is segmented by using novel event-driven signal selection technique. Afterwards, the segmented signal pertinent features are extracted by using an effective adaptive rate time-domain analysis approach. These features are passed to the robust machine-learning based classifiers to realize an automated identification of the PQ disturbances. The system secures 13.26-fold compression gain compared to the conventional fix-rate counterparts. The highest classification precision of 99.44% is secured. It confirms that the suggested method can be integrated in contemporary automated PQ identifiers.