Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Optimization Principles: Practical Applications to the Operation and Markets of the Electric Power Industry
Modern Heuristic Optimization Techniques With Applications To Power Systems
Modern Heuristic Optimization Techniques With Applications To Power Systems
A gradient-based artificial immune system applied to optimal power flow problems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
On diversity and artificial immune systems: incorporating a diversity operator into ainet
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.