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- Publisher Website: 10.1109/PESGM.2018.8586038
- Scopus: eid_2-s2.0-85060814655
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Conference Paper: Clustering Of Uncertain Load Model Parameters With K-mediods Algorithm
Title | Clustering Of Uncertain Load Model Parameters With K-mediods Algorithm |
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Authors | |
Keywords | Clustering K-mediods algorithm Load modelling Power system uncertainty |
Issue Date | 2018 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000581 |
Citation | IEEE Power & Energy Society (PES) General Meeting, Portland, Oregon, USA, 5-9 August 2018, p. 1-5 How to Cite? |
Abstract | Load model is difficult to build due to the uncertain property of power load. Using ambient signal based load model parameter identification method, load model parameter identification can be performed very frequently and then many different identification results at different time points can be obtained. To deal with these uncertain load model parameters, a load model parameter clustering method is proposed to pick up the representative load model parameters from the identification results. The distances of models used for clustering are based on the post-fault response curves to get better clustering results. K-medios clustering algorithm is applied and the cluster number is decided by the radius of the clusters. The simulation results have shown the effectiveness of the proposed load model parameter clustering method. |
Persistent Identifier | http://hdl.handle.net/10722/259705 |
ISSN | 2020 SCImago Journal Rankings: 0.345 |
DC Field | Value | Language |
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dc.contributor.author | Zhang, X | - |
dc.contributor.author | Hill, DJ | - |
dc.date.accessioned | 2018-09-03T04:12:30Z | - |
dc.date.available | 2018-09-03T04:12:30Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Power & Energy Society (PES) General Meeting, Portland, Oregon, USA, 5-9 August 2018, p. 1-5 | - |
dc.identifier.issn | 1944-9933 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259705 | - |
dc.description.abstract | Load model is difficult to build due to the uncertain property of power load. Using ambient signal based load model parameter identification method, load model parameter identification can be performed very frequently and then many different identification results at different time points can be obtained. To deal with these uncertain load model parameters, a load model parameter clustering method is proposed to pick up the representative load model parameters from the identification results. The distances of models used for clustering are based on the post-fault response curves to get better clustering results. K-medios clustering algorithm is applied and the cluster number is decided by the radius of the clusters. The simulation results have shown the effectiveness of the proposed load model parameter clustering method. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000581 | - |
dc.relation.ispartof | IEEE Power & Energy Society General Meeting (PESGM) | - |
dc.rights | IEEE Power & Energy Society General Meeting (PESGM). Copyright © IEEE. | - |
dc.subject | Clustering | - |
dc.subject | K-mediods algorithm | - |
dc.subject | Load modelling | - |
dc.subject | Power system uncertainty | - |
dc.title | Clustering Of Uncertain Load Model Parameters With K-mediods Algorithm | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhang, X: zhangxr7@hku.hk | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/PESGM.2018.8586038 | - |
dc.identifier.scopus | eid_2-s2.0-85060814655 | - |
dc.identifier.hkuros | 288828 | - |
dc.identifier.hkuros | 307233 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 5 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1944-9925 | - |