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|Title: ||Online Voltage Stability Prediction and Control Using Computational Intelligence Technique|
|Authors: ||Zhou, Qun Debbie|
|Supervisor: ||Annakkage, Uadya (Electrical and Computer Engineering)|
|Examining Committee: ||Rajapakse, Athula (Electrical and Computer Engineering)
Rattanawangcharoen, Nipon (Civil Engineering)
Xu, Wilsun (Electrical and Computer Engineering, University of Alberta)|
|Graduation Date: ||October 2010|
|Keywords: ||Power System|
Voltage Stability and Control
Computational Intelligence Application
|Issue Date: ||21-Sep-2010|
|Citation: ||Debbie Q. Zhou, U. D. Annakkage, and A. D. Rajapakse, "Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network", IEEE Transactions on Power Systems, Vol 25, No:4, Page(s):1566-1574, 2010|
Debbie Q. Zhou, Udaya D. Annakkage, and Athula Rajapakse, "An Online Load Shedding Approach for Voltage Stability Enhancement", CIGRE Canada Conference on Power Systems, Toronto, October, 2009.
Debbie Q. Zhou, Udaya D. Annakkage, and Athula Rajapakse, "Optimal Placement of PMUs in a Wide Area Monitoring System Using an Artificial Neural Network Based Technique", CIGRE Canada Conference on Power Systems,Winnipeg, October, 2008.
Debbie Q. Zhou and Udaya D. Annakkage, "Investigation of a Criterion for Load Shedding Based on voltage stability indices", CIGRE Canada Conference on Power Systems, Calgary, August, 2007.
Voltage instability has become a major concern in power systems. Many blackouts have been reported where the main cause is voltage instability. This thesis deals with two specific areas of voltage stability in on-line power system security assessments: small-disturbance (long-term) and large-disturbance (short-term) voltage stability assessment. For each category of voltage stability, both voltage stability analysis and controls are studied. The overall objective is to use the learning capabilities of computational intelligence technology to build up the comprehensive on-line power system security assessment and control strategy as well as to enhance the speed and efficiency of the process with minimal human intervention.
The voltage stability problems are quantified by voltage stability indices which measure the system for the closeness of current operating point to voltage instability. The indices are different for small-disturbance and large-disturbance voltage stability assessment. Conventional approaches, such as continuation power flow or time-domain simulation, can be used to obtain voltage stability indices. However, these conventional approaches are limited by computation time that is significant for on-line computation. The Artificial Neural Network (ANN) approach is proposed to compute voltage stability indices as an alternative to the conventional approaches. The proposed ANN algorithm is used to estimate voltage stability indices under both normal and contingency operating conditions.
The input variables of ANN are obtained in real-time by an on-line measurement system, i.e. Phasor Measurement Units (PMU). This thesis will propose a suboptimal approach for seeking the best locations for PMUs from a voltage stability viewpoint. The ANN-based method is not limited to compute voltage stability indices but can also be extended to determine suitable control actions.
Load shedding is one of the most effective approaches against short-term voltage instability under large disturbances. The basic requirement of load shedding for recovering voltage stability is to seek an optimal solution for when, where, and how much load should be shed. Two simulation based approaches, particle swarm optimization (PSO) algorithm and sensitivity based algorithm, are proposed for load shedding to prevent voltage instability or collapse. Both approaches are based on time-domain simulation.|
|Appears in Collections:||FGS - Electronic Theses & Dissertations (Public)|
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