To date, the potential of on-line Dynamic Security Assessment (DSA) to monitor, alert, and enhance system security is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations make it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems.
The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and
FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF.
An on-line DSA system requires the integration of several functional components. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method.
In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms. / February 2017
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31926 |
| Date | 10 November 2016 |
| Creators | Geeganage, Janath Chaminda |
| Contributors | Annakkage, Udaya (Electrical and Computer Engineering), Rajapakse, Athula (Electrical and Computer Engineering) Archer, Brian (Electrical and Computer Engineering) Bassuoni, Mohamed (Civil Engineering) Ajjarapu, Venkataramana (College of Engineering, Iowa State University) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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