Ant Colony Technique for Transformer Tap Changer Setting Optimization

Optimization techniques are perquisite in solving multi-dimensional, higher order and multi-variable problem formulations. They can be used to solve problems in various fields such as mathematics, engineering, sciences and medicine. In electric power system engineering, optimization problems normally dealt with multi-variables, quadratic, non-convex and involved many constraints. Amongst the popular optimization techniques are such as; tabu search, genetic algorithm (GA), evolutionary programming (EP), ant colony optimization (ACO), artificial immune system (AIS) and particle swarm optimization (PSO). The trend these days is going towards EP, PSO, ACO and GA. However, any of these techniques are not accurate for all cases; in which generalization cannot be made with regards to a particular optimization technique. This paper presents ACO technique for optimizing the size for transformer tap changer setting (OTTCS) in a power transmission network which aims to improve voltage stability condition or loss minimization in the system. ACO is a new cooperative agent's approach, which was inspired by the observation of the behaviours; of real ant colonies on the topics of ant trial formation and foraging method. The set of cooperating agents called "ant" cooperate to find the optimal point of OTTCS. The essence of original ACO in solving graphical optimization problems have been modified to solve the power system optimization problems which are more continuous in nature. Implementation of ACO in identifying the optimal values for OTTCS, tested on a reliable test system has produced promising results. Comparative studies presented with respect to EP and AIS had indicated the merit of the proposed technique.