Research on non-intrusive unknown load identification technology based on deep learning

With the development of smart grid technology, optimization of energy structure, improvement of power efficiency and reduction of power consumption have become the major development trends. As a key process of power structure analysis, load identification is gaining increasing attention in smart grids. Although lots of load identification methods have been proposed, the introduction of unknown states or new load types is still a challenge for precise identification. In this paper, a similarity calculation method of the space convex hull overlap rate is proposed to deal with the identification of unknown loads based on the low-dimensional feature space model of Siamese neural networks. Moreover, transfer learning is introduced to realize the category-added learning of load pre-identification model for unknown load. The ultimate aim was to establish the identification and rapid modeling of unknown loads. The public datasets Plaid is used to verify the performance of the proposed method. As a result, the high accuracy of load identification for unknown load is proved.