Abstract:
In the task of non-intrusive load identification, only using a single load feature to classify devices may appear overlapping characteristics, which does not meet the requirements for the fine-grained classification of the devices. Therefore, a non-intrusive fine-grained load identification based on color encoding is proposed in this paper. The Fryze power theory is firstly used to separate the high-frequency sampling current into an active and a reactive component, and the high-frequency sampling voltage and the reactive current are standardized to construct the two-dimensional voltage- current (U-I) trajectory images. Then the trajectory images are processed by the color encoding technology, and fused with the active current, the trajectory change information and the instantaneous power respectively in the R, G, and B channels to acquire the color U-I trajectory images. Finally, the convolutional neural network is constructed to extract the features of the color U-I trajectory images, realizing the classification of the devices. On this basis, a self-learning method is proposed to realize the autonomous updating of the load identification model. The PLAID and WHITED datasets are used to test the recognition effect of this algorithm and the self-learning method. The experimental results show that the proposed method increases the amount of information carried by the U-I trajectory, enhances the distinguishability of load features, and realizes the fine-grained identification of devices. The self-learning method can learn new electrical appliances and update the model, which improves the scene adaptability of the load identification model.