Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine

In the process of traditional power load identification, the load information of V-I track is missing, the image similarity of V-I track of some power loads is high and the recognition effect is not good, and the training time of recognition model is too long. In view of the abovementioned situation, this study proposes a power load recognition method based on color image coding and the improved twin support vector machine (ITWSVM). First, based on the traditional voltage-current gray trajectory method, the bilinear interpolation technique is used to solve the pixel discontinuity problem effectively. Considering the complementarity of features, the numerical features are embedded into the gray V-I trajectory by constructing three channels, namely, current (R), voltage (G), and phase (B), so the color V-I image with rich electrical features is obtained. Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. Experimental results show that the proposed color V-I image coding method and the ITWSVM classification method, compared with the traditional V-I track image construction method and image classification algorithm, improve the accuracy by 6.12% and reduce the model training time by 1071.23 s.