Tracking of voltage variations by means of an adaptive filter and fuzzy logic

Monitoring of voltage variations is a demanding issue for academic and industrial fields due mainly to their negative impact on equipment. In this work, a methodology based on adaptive filter using the least mean squares algorithm for tracking of voltage variations and a fuzzy logic system for automatic classification are proposed. The proposal consists of three stages: 1) denoising through a lowpass filter to remove non-fundamental frequency components, 2) envelope and type of voltage tracking, and 3) final classification according to the IEEE Std. 1159 using a rule-based decision process. In order to validate and test the proposal, a set of synthetic and real signals is used. The obtained results demonstrate the proposal effectiveness to detect and classify voltage variations, even when they are embedded in high level noise. Unlike other reported works, the proposed fuzzy logic system allows the tracking of the voltage variation such as sag, swell, or interruption over time, it means sample to sample.