Duan, J, Li, Y, Zhang, Z, Li, W, Jiang, L 
ORCID: 0000-0001-6531-2791 and Li, L
(2021)
Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity.
Dianli Xitong Zidonghua/Automation of Electric Power Systems, 45 (24).
pp. 65-72.
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考虑辨识结果连续性的Type-III型工商业负荷辨识方法.pdf - Author Accepted Manuscript Download (3MB) | Preview |
Abstract
Non-intrusive load monitoring technology can guide users to arrange power consumption time reasonably, thereby reducing power consumption. Among them, due to the continuous variability of the state, the identification of continuously varying (Type-Ⅲ) load has always been one of the difficult problems in non-intrusive load monitoring. Aiming at the problem of Type-Ⅲ load identification, a non-intrusive load identification algorithm based on deep convolutional neural network (CNN) and hidden Markov model (HMM) is proposed. Firstly, the load characteristics are selected according to the mutual information theory. Then, the residual neural network is used as the basic structure of deep CNN to extract multi-dimensional features of the load and realize the initial identification of Type-Ⅲ loads. Finally, in order to solve the problem of state breakpoint in CNN identification results, the HMM is used to complete the continuous optimization of load identification results. In the complex industrial and commercial operation environment, the algorithm is trained and verified on the representative Type-Ⅲ load data. The results show that the proposed algorithm can effectively identify the operation state of Type-Ⅲ industrial and commercial load.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 12 Jan 2022 14:57 |
| Last Modified: | 18 Jan 2023 21:16 |
| DOI: | 10.7500/AEPS20210416001 |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3146697 |
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