Detection of power quality disturbances using an adaptive process noise covariance Kalman filter

Abstract

This paper proposes an adaptive process noise covariance Kalman filter (APNCKF) for detecting the power quality disturbances present in distorted power signals. Based on the Kalman filter (KF), the new algorithm updates the process noise covariance matrix of the KF by maximizing the evidence density function. The evolution of the estimated process noise exhibits remarkable singularities when the voltage sag occurs or the phase jumps, and the increase in the process noise allows the KF to jump from one stationary regime to another dynamic regime. Thus, the proposed method can determine the start time and the end time of voltage sag and phase jump by using the estimated process noise. Moreover, the APNCKF is used to detect power quality disturbances like voltage sag with phase jumps in presence of additive white Gaussian noise. To test the effectiveness of the algorithm, several time-varying power system signals are simulated with fundamental and harmonic components distorted with voltage sag, phase jump, transient impulse and harmonics. Also, two experiments with real voltage sag data from IEEE 1159.2 Working Group are conducted. Experimental results show that the proposed algorithm can exactly determine the start time and the end time of disturbances by use of the evolution of the estimated process noise covariance matrix.