Classification of voltage sags causes in industrial power networks using multivariate time-series

Voltage sags are the most frequent and impactful disturbances in industrial power grids, leading to high financial losses for industrial clients. The identification of the cause and its relative location is crucial for the contractual relation between the energy provider and the industrial customers. This paper proposes a methodology to identify the origins of voltage sags based on instantaneous symmetrical components and dynamic time warping. Short-Time Fourier and Fortescue transform are implemented in the pre-processing step using the voltage and current waveforms. Then, a distance-based classification strategy to identify the sources of voltage sags is used. It relies on a four-dimension time-series signature used as features. Moreover, a confidence index associated with the classification output is provided. The proposal offers an easy implementation in industrial applications with no previous recorded data. It has the benefit of using a reduced-size reference database entirely composed of synthetic data. The main advantages of the proposed method are its generalization capabilities and the possibility of raising an alert based on the confidence index. The obtained classification accuracy on synthetic data with seven causes is 100%. The method reaches a classification F1-score higher than 99% with field measurements representing five classes obtained from three different industrial sites.