Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical Engineering | en_US |
| dc.creator | Yeung, Chi On | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12502 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Risk assessment of power system security for operational planning | en_US |
| dcterms.abstract | A new Holomorphic Embedding Load Flow Method HELM with different embedded equations is proposed to solve the probabilistic power flow problem and original power flow problem with respect to change in operating conditions in one particular bus specified in the equations. The numerical implementation of proposed non-iterative HELM is simple requiring linear algebra and Padé Approximant only. It guarantees to converge to an operable solution if one exists hence having a better performance comparing to traditional iterative methods when near voltage collapse point. When dealing with a large amount of power flow simulations, proposed HELM has a smaller computation burden comparing to other numerical methods because the system parameters can be explicitly expressed by rational fractions as functions of complex power injection at another pre-determined bus. The original power flow solution can be obtained online by simple substitution to the rational fraction without doing the whole computational process again which can save tremendous amount of time. Moreover, proposed HELM helps system operator to perform offline risk assessment during operational planning at different operating conditions as the likelihood of different system risks can be computed using probability distribution function generated from proposed HELM model. Hence, system operator can take proactive action when dealing with fluctuation of renewable energy power output. In this dissertation, wind power probabilistic model is used to demonstrate this idea. The explicit form of the distribution function is also derived in this dissertation. | en_US |
| dcterms.extent | xix, 116 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Wind power -- Risk management | en_US |
| dcterms.LCSH | Electric power system stability | en_US |
| dcterms.LCSH | Renewable energy sources | en_US |
| dcterms.LCSH | Renewable energy sources | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6951.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.21 MB | Adobe PDF | View/Open |
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