Adaptive Non-Intrusive Load Monitoring Based on Feature Fusion

Intelligent power management system is an important part of the smart grid, in which the non-intrusive load monitoring technology is one of the key technologies. However, most of the load monitoring methods regard this task as a multi-classification problem, thus it is not effective to identify the unknown loads that did not participate in training. In this paper, an adaptive non-intrusive load monitoring method based on feature fusion has been proposed which utilizes both information of harmonic current feature and voltage-current (V-I) trajectory feature. The harmonic current feature is obtained from the high-frequency load sampling data through Fast Fourier Transform (FFT). At the same time, the V-I trajectory feature is obtained by using the pre-trained convolutional autoencoder neural network that was trained for feature extraction on a public dataset. After the feature extraction, the similarity between these two feature vectors and the available feature vectors in the database is calculated by TOPSIS algorithm. Then the load monitoring can be carried out according to the similarity. When the maximum similarity is greater or equal than the set threshold, the load is considered to be one of the existing loads in the database, otherwise, it will be considered as a new type of load, and the load feature database will be updated. The autoencoder model is trained by using the V-I trajectory from the BLUED dataset and the PLAID dataset is used to verify the identification accuracy of the proposed algorithm by comparison with different algorithms. On the PLAID dataset, the identification accuracy can reach above 97%. Finally, on the embedded Linux system with STM32MP1 as the core, some household electrical appliances are used for validation in real house environment. The results show that the proposed method has improved the capability of load identification by using the complementarity of different features. It can be carried out in real time on embedded system and is able to identify the unknown load at the same time.