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Implementation of Artificial Intelligence Techniques for Steady State Security Assessment in Pool Market
Ibrahim salem saeh, A. Khairuddin
Pages - 1 - 11     |    Revised - 20-02-2009     |    Published - 15-03-2009
Volume - 3   Issue - 1    |    Publication Date - February 2009  Table of Contents
Artificial intelligence, deregulated system, Neural Network , Decision Tree, ANFIS
Various techniques have been implemented to include steady state security assessment in the analysis of trading in deregulated power system, however most of these techniques lack requirements of fast computational time with acceptable accuracy. The problem is compounded further by the requirements to consider bus voltages and thermal line limits. This work addresses the problem by presenting the analysis and management of power transaction between power producers and customers in the deregulated system using the application of Artificial Intelligence (AI) techniques such as Neural Network (ANN), Decision Tree (DT) techniques and Adaptive Network based Fuzzy Inference System (ANFIS). Data obtained from Newton Raphson load flow analysis method are used for the training and testing purposes of the proposed techniques and also as comparison in term of accuracy against the proposed techniques. The input variables to the AI systems are loadings of the lines and the voltage magnitudes of the load buses. The algorithms are initially tested on the 5 bus system and further verified on the IEEE 30 57 and 118 bus test system configured as pool trading models. By comparing the results, it can be concluded that ANN technique is more accurate and better in term of computational time taken compared to the other two techniques. However, ANFIS and DT’s can be more easily implemented for practical applications. The newly developed techniques can further improve security aspects related to the planning and operation of pool-type deregulated system.
CITED BY (3)  
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Mr. Ibrahim salem saeh
- Malaysia
Mr. A. Khairuddin
- Malaysia