A Novel Recognition Method for Complex Power Quality Disturbances Based on Visualization Trajectory Circle and Machine Vision

Accurate and fast recognition of power quality disturbances (PQDs) is very significant for power pollution control. A novel method based on visualization trajectory circle (TC) and machine vision is proposed to ameliorate the recognition accuracy of complex PQDs. To obtain the anti-interference stationary analytic signal sequence, an improved Hilbert transform (IHT) is performed on single and complex PQD signals. The instantaneous amplitude and phase are taken as polar radius and angle to obtain the TC image in polar coordinates. The images are input in ResNet50 for training to achieve the optimal network model, to realize the type recognition. Finally, the proposed method is tested by the synthetic database, which is built from mathematical models and compared with other advanced methods. In addition, time interval detection can be realized by the Hilbert spectrum based on IHT. Simulation results demonstrate that the method has strong robustness and high accuracy. Furthermore, a 13-node microgrid test system with distributed generations is built on the RT-Lab platform, to generate PQDs for further validating the method. The single or complex PQDs caused by test events are successfully recognized.