Power quality disturbances classification using complex wavelet phasor space reconstruction and fully connected feed forward neural network

Power quality disturbances (PQD) degrades the quality of power. Detection of these PQDs in real time using smart systems connected to the power grid is a challenge due to the integration of energy generation units and electronic devices. Deep learning methods have shown advantages for PQD classification accurately. PQD events are non-stationary and occur at discrete events. Pre-processing of power signal using dual tree complex wavelet transform in localizing the disturbances according to time-frequency-phase information improves classification accuracy.Phase space reconstruction of complex wavelet sub bands to 2D data and use of fully connected feed forward neural network improves classification accuracy. In this work, a combination of DTCWT-PSR and FC-FFNN is used to classify different complex PSDs accurately.The proposed algorithm is evaluated for its performance considering different network configurations and the most optimum structure is developed. The classification accuracy is demonstrated to be 99.71% for complex PQDs and is suitable for real time activity with reduced complexity.

Classification and Identification of Power Quality Disturbances Based on Stochastic Forest Model

With the emergence and use of a large number of new power electronic equipment, the power supply department has begun to pay extensive attention to the problem of power quality, and the majority of users have put forward higher requirements for the quality of power supply. This paper studies the classification and identification method of power quality disturbances based on random forest model. In this paper, according to IEEE power quality standard, the normal waveform and 16 common power quality disturbance waveforms are mathematically modeled, and the power quality disturbance signal is analyzed by S-transform. In this paper, the power quality disturbance identification algorithm based on random forest is optimized. Experimental data show that the optimized method has higher disturbance recognition accuracy and better anti noise ability. Therefore, using the power quality disturbance identification method proposed in this paper to monitor the power quality of power grid is of great significance to ensure the safe and stable operation of power grid and improve economic benefits.