Prediction models of voltage sag characteristics based on measured data

Abstract

An effective method of assisting utilities and users to avoid the risk of voltage sags is the accurate prediction of voltage sags. This study analyzes the predictability and proposes the prediction models for three characteristics of voltage sags based on measured data. First, this study defines the time series of voltage sag and analyzes the predictability of the residual voltage, duration, and time of occurrence. Second, this study proposes a fuzzy logic prediction model of the residual voltage to address the fuzziness of the residual voltage. Third, this study proposes an evidence theory prediction model of the duration according to the correlation between the residual voltage and duration. Further, this study proposes a chaotic signal prediction model of the occurrence time by reconstructing the phase space of the occurrence time. Finally, the measured voltage sag data from 10 sites are used to analyze and validate the proposed prediction models.