Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering
Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5748546 |
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| author | Shuhui Yi Yinglong Diao Junjie Liu Tian Fang Xiaodong Yin |
| author_facet | Shuhui Yi Yinglong Diao Junjie Liu Tian Fang Xiaodong Yin |
| author_sort | Shuhui Yi |
| collection | DOAJ |
| description | Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model. |
| format | Article |
| id | doaj-art-124a9e4e1dea48df9c77c1168ebfd040 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-124a9e4e1dea48df9c77c1168ebfd0402025-02-03T00:59:36ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/5748546Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader ClusteringShuhui Yi0Yinglong Diao1Junjie Liu2Tian Fang3Xiaodong Yin4China Electric Power Research InstituteChina Electric Power Research InstituteChina Electric Power Research InstituteChina Electric Power Research InstituteChina Electric Power Research InstituteNonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.http://dx.doi.org/10.1155/2022/5748546 |
| spellingShingle | Shuhui Yi Yinglong Diao Junjie Liu Tian Fang Xiaodong Yin Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering Journal of Control Science and Engineering |
| title | Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering |
| title_full | Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering |
| title_fullStr | Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering |
| title_full_unstemmed | Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering |
| title_short | Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering |
| title_sort | nonintrusive load identification for industrial users integrated with lsqr and sequential leader clustering |
| url | http://dx.doi.org/10.1155/2022/5748546 |
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