Skip navigation
  • Home
  • Browse
    • Communities
      & Collections
    • Browse Items by:
    • Publication Date
    • Author
    • Title
    • Subject
    • Department
  • Sign on to:
    • My MacSphere
    • Receive email
      updates
    • Edit Profile


McMaster University Home Page
  1. MacSphere
  2. Open Access Dissertations and Theses Community
  3. Open Access Dissertations and Theses
Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/5854
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorEl-Kady, Mohamed A.en_US
dc.contributor.advisorSinhu, Naresh K.en_US
dc.contributor.authorEl-Sobki, Salah Mohameden_US
dc.date.accessioned2014-06-18T16:33:15Z-
dc.date.available2014-06-18T16:33:15Z-
dc.date.created2010-05-10en_US
dc.date.issued1985-04en_US
dc.identifier.otheropendissertations/1200en_US
dc.identifier.other2500en_US
dc.identifier.other1304432en_US
dc.identifier.urihttp://hdl.handle.net/11375/5854-
dc.description.abstract<p>The major effort in this thesis has been directed towards the performance modelling and reliability estimation of large-scale power systems subject to probable single or multiple contingencies. A scheme for performance data pooling is introduced, which overcomes the problems of the limited available performance history and manipulation of large numbers of data sets associated with various components of a power system. A simulation technique which adopts a Monte-Carlo scheme is developed-to estimate component reliability measures and to select the probable contingency states of the system. A combined optimization/reliability formulation is introduced which provides improved reliability figures of the power system. In this formulation the system control parameters are manipulated to simulate practical contingency situations in which suitable system controls are invoked to preserve as much as possible, the continuity of supply. An efficient network partitioning scheme based on Ward equivalence has been developed. Only those parts of the system which are mostly affected by a contingency are retained for detailed analysis while the rest of the system is modelled by network equivalents. It is demonstrated that the use of such a partitioning scheme yields significant reduction in computational time and storage for contingency analysis while maintaining sufficiently accurate solutions. The partitioning scheme is subsequently combined with the optimization/reliability formulation which adds to the efficiency of both approaches. A technique is developed to guide the partitioning scheme via predicting the change in a performance index due to a contingency. The predicted changes are efficiently calculated using either a perturbed matrix inversion scheme or a suitable nth order derivative formulation. Using these predicted changes, a generalized contingency ranking methodology is introduced, for generation and/or transmission contingencies. The contingency ranking scheme is based on an important class of line loading functions representing system security constraints. A novel and computationally efficient technique for fast linear contingency analysis and ranking is introduced. This technique utilizes a bilinear-based formulation to calculate efficiently, the changes of individual bus voltage angles after the occurrence of a transmission contingency. An alternative equivalent formulation of the technique is aIso presented. It adopts reduced gradients of system states and provides the post-contingency exact changes of these states with significantly reduced online computations.</p>en_US
dc.subjectElectrical and Computer Engineeringen_US
dc.subjectElectrical and Computer Engineeringen_US
dc.titleReliability Evaluation of Large-Scale Power Systemsen_US
dc.typethesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeDoctor of Philosophy (PhD)en_US
Appears in Collections:Open Access Dissertations and Theses

Files in This Item:
File SizeFormat 
fulltext.pdf
Open Access
5.06 MBAdobe PDFView/Open
Show simple item record Statistics


Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.

Sherman Centre for Digital Scholarship     McMaster University Libraries
©2022 McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8 | 905-525-9140 | Contact Us | Terms of Use & Privacy Policy | Feedback

Report Accessibility Issue