Power Quality Disturbance Classification Method Based on Unscented Kalman Filter and Kernel Extreme Learning Machine

The power quality index is an important index in the power industry. Power quality disturbances (PQDs) have a great impact on the power grid. It is important to identify the type of PQDs accurately. However, it is difficult to analyze a large number of PQDs, especially in more complex power systems. Considering the limitations of the traditional time–frequency domain method and the complexity of the optimization algorithm in extracting features, a novel algorithm is proposed to classify the PQDs in this paper, which is based on the unscented Kalman filter (UKF) and the kernel extreme learning machine (KELM). The UKF is used to detect and process the original disturbances, and anti-noise detection performance is analyzed by tracking the amplitude change in voltage swell under different signal–noise ratios (SNRs). The amplitudes of the fundamental wave, third harmonic, fifth harmonic, seventh harmonic, and oscillatory wave are tracked in real time, and their minimum and peak indexes are taken as the optimal feature vector set. The feature vector set is classified by the KELM. The performance of the proposed method has also been evaluated with simulated and experimental results.