Static security assessment in deregulated power system using artificial inteligence techniques

• Main Author: Ali Ilsaeh, Ibrahem Salem
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/12708/1/IbrahemSalemAliMFKE2009.pdf; http://eprints.utm.my/id/eprint/12708/

The basic function of an electric power system is to supply customers with electric energy as economically as possible and with a reasonable degree of continuity and quality. Deregulation of power system in recent years has turned static security assessment (SSA) into a challenging task for which acceptably fast and accurate assessment methodology is essential. The objective of this research is to investigate the reliability of the SSA in determining the security level of power system from serious interference during operation. In this research, three types of Artificial Intelligence (AI) techniques namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System and Decision Trees are implemented to classify the security status in the test power system, comparison are made in terms of computation time and accuracy of the networks. Data obtained from Newton Raphson Load Flow (NRLF) analysis method are used for the training and testing purposes of the proposed AI techniques. The data are used also as a benchmark to validate the results from AI techniques to achieve high speed of execution and good classification accuracy. A new methodology of feature selection technique based on extracting variables has also been applied. The proposed techniques have been extended and tested on 5, 30, 57 and 118 IEEE test systems. The deregulated system is configured into base case, pool and bilateral contract modes in order to evaluate the effectiveness of the proposed techniques for SSA on deregulated power system. Generally, the proposed AI techniques have successfully been applied to evaluate SSA for various IEEE test system configured as deregulated systems. These techniques are as accurate as NRLF techniques but with shorter computation time. It is found that the ANN is well suited for online SSA of deregulated power systems amongst the three methods applied.