File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/PESGM.2018.8586673
- Scopus: eid_2-s2.0-85060779907
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method
Title | Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method |
---|---|
Authors | |
Keywords | Load modeling Analytical models Data models Parameter estimation Measurement errors |
Issue Date | 2018 |
Publisher | IEEE. The Proceedings' 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 parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated. |
Persistent Identifier | http://hdl.handle.net/10722/259706 |
ISSN | 2020 SCImago Journal Rankings: 0.345 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, X | - |
dc.contributor.author | Lu, C | - |
dc.contributor.author | Wang, Y | - |
dc.date.accessioned | 2018-09-03T04:12:31Z | - |
dc.date.available | 2018-09-03T04:12:31Z | - |
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 | 1944-9925 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259706 | - |
dc.description.abstract | Load model parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Proceedings' 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.rights | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Load modeling | - |
dc.subject | Analytical models | - |
dc.subject | Data models | - |
dc.subject | Parameter estimation | - |
dc.subject | Measurement errors | - |
dc.title | Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhang, X: zhangxr7@hku.hk | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/PESGM.2018.8586673 | - |
dc.identifier.scopus | eid_2-s2.0-85060779907 | - |
dc.identifier.hkuros | 288830 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 5 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1944-9925 | - |