Recognition Method of Voltage Sag Sources Based on RMT-CNN Model

The precise recognition of the sources of voltage sag is the basis and key to formulating a voltage sag governance plan and clarifying responsibility for an accident. Due to the complexity of grid devices and the identification of the mode of power consumption, the conventional approach to identifying voltage sags faces a new challenge. Due to the dependence on accurate voltage sag models, the traditional methods are inadequate for complex problems with multiple uncertainty factors. The random matrix theory based on a data-driven method analyzes data correlation using single characteristic statistics, which is not suitable for the dimensional change of the matrix. It is obvious that this method is not applicable to the position of the source of the voltage sag due to random sags. Therefore, this paper proposes a voltage sag source recognition method based on the combination of random matrix theory (RMT) and a convolutional neural network (CNN). In this method, the CNN optimizes the characteristic statistics of RMT and constructs new characteristic statistics such as the correlation analysis index of the voltage sag source recognition model to avoid the error caused by a single characteristic statistic. First, random matrix theory is used to extract characteristics from historical data, and characteristic statistics under different faults and different data dimension matrices are obtained and then, are used as the input characteristic sets of the CNN. Then, through training extraction, the optimized characteristic statistics that can be applied to a variety of conditions are obtained. Furthermore, a voltage sag source recognition model based on random matrix theory is constructed by using optimized characteristic statistics. The correlation analysis index is obtained to recognize voltage sag sources under different data conditions. Finally, examples are given to verify the feasibility and effectiveness of the proposed method.