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Implementation of Artificial Intelligence Techniques for Steady State Security Assessment in Pool Market

Author(s): I. S. Saeh | A. Khairuddin

Journal: International Journal of Engineering
ISSN 1985-2312

Volume: 3;
Issue: 1;
Start page: 1;
Date: 2009;
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Keywords: Artificial intelligence | deregulated system

Various techniques have been implemented to include steady state securityassessment in the analysis of trading in deregulated power system, howevermost of these techniques lack requirements of fast computational time withacceptable accuracy. The problem is compounded further by the requirements toconsider bus voltages and thermal line limits. This work addresses the problemby presenting the analysis and management of power transaction between powerproducers and customers in the deregulated system using the application ofArtificial Intelligence (AI) techniques such as Neural Network (ANN), DecisionTree (DT) techniques and Adaptive Network based Fuzzy Inference System(ANFIS). Data obtained from Newton Raphson load flow analysis method areused for the training and testing purposes of the proposed techniques and alsoas comparison in term of accuracy against the proposed techniques. The inputvariables to the AI systems are loadings of the lines and the voltage magnitudesof the load buses. The algorithms are initially tested on the 5 bus system andfurther verified on the IEEE 30 57 and 118 bus test system configured as pooltrading models. By comparing the results, it can be concluded that ANNtechnique is more accurate and better in term of computational time takencompared to the other two techniques. However, ANFIS and DT’s can be moreeasily implemented for practical applications. The newly developed techniquescan further improve security aspects related to the planning and operation ofpool-type deregulated system.
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