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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
Published in International Journal of Engineering (IJE)
MORE INFORMATION
KEYWORDS
Artificial intelligence, deregulated system, Neural Network , Decision Tree, ANFIS
ABSTRACT
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.
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Mr. Ibrahim salem saeh
- Malaysia
ibrahimsaeh@yahoo.com
Mr. A. Khairuddin
- Malaysia
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