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Classification of Voltage Sag Sources Based on PCA-CNN-LVQ Method

With the development of power electronic technology and information technology, power electronic equipment used for production has high voltage quality requirements in industrial parks. Even a very short voltage drop will cause waste on the production line. In order to reduce the impact of voltage sag, identifying the source of voltage sag is the first step to solve this problem. Deep learning as a branch of machine learning, can effectively perform feature extraction and pattern recognition. Apply deep learning methods in recognizing voltage sag sources has shown high accuracy.In this paper, we proposed a PCA-CNN-LVQ model. To apply this model based on simulated voltage sag data, we use the Principle Component Analysis(PCA) to reduce the dimensions of data, and a deep learning method, Convolutional Neural Network(CNN) to extract data feature, then classify the type of voltage sag with Learning Vector Quantization(LVQ). Through simulation experiments, it can be obtained that the model can effectively identify the type of voltage sag. Compared with other models that only use CNN (softmax), PCA-CNN (softmax), and CNN-LVQ, it has the best accuracy of identifying the source of voltage sags among them. PCACNN-LVQ has a very good performance in the small and medium-sized sample data set.