2010 | OriginalPaper | Chapter
Constraint Handling in Transmission Network Expansion Planning
Authors : R. Mallipeddi, Ashu Verma, P. N. Suganthan, B. K. Panigrahi, P. R. Bijwe
Published in: Swarm, Evolutionary, and Memetic Computing
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Transmission network expansion planning (TNEP) is a very important and complex problem in power system. Recently, the use of metaheuristic techniques to solve TNEP is gaining more importance due to their effectiveness in handling the inequality constraints and discrete values over the conventional gradient based methods. Evolutionary algorithms (EAs) generally perform unconstrained search and require some additional mechanism to handle constraints. In EA literature, various constraint handling techniques have been proposed. However, to solve TNEP the penalty function approach is commonly used while the other constraint handling methods are untested. In this paper, we evaluate the performance of different constraint handling methods like Superiority of Feasible Solutions (SF), Self adaptive Penalty (SP),
$\mathcal E$
-Constraint (EC), Stochastic Ranking (SR) and the ensemble of constraint handling techniques (ECHT) on TNEP. The potential of different constraint handling methods and their ensemble is evaluated using an IEEE 24 bus system with and without security constraints.