This paper introduces the ant colony meta-heuristic technique to optimally locate STATCOM in 330kV Nigerian Network. The Ant Colony Optimization (ACO) algorithms used the STATCOM parameters and probabilistic model to generate solutions to the problem of siting STATCOM in Nigerian network. The optimal location of STATCOM in Nigerian network is evidenced in bus voltage profile enhancement and minimization of transmission losses. The probabilistic model is called pheromone model which consists of a set of model parameters, often referred to as pheromone values. At run-time, the ACO algorithms try to update the pheromone values from previously generated solutions in such a way that the probability to generate high quality solutions increases over time. Finally, the graph of pheromone trail and path treaded by the ants along the various nodes are captured whose codes are validated using the Matrix Laboratory Software (MATLAB) environment.
This paper introduces the ant colony meta-heuristic technique to optimally locate STATCOM in 330kV Nigerian Network. The Ant Colony Optimization (ACO) algorithms used the STATCOM parameters and probabilistic model to generate solutions to the problem of siting STATCOM in Nigerian network. The optimal location of STATCOM in Nigerian network is evidenced in bus voltage profile enhancement and minimization of transmission losses. The probabilistic model is called pheromone model which consists of a set of model parameters, often referred to as pheromone values. At runtime, the ACO algorithms try to update the pheromone values from previously generated solutions in such a way that the probability to generate high quality solutions increases over time. Finally, the graph of pheromone trail and path treaded by the ants along the various nodes are captured whose codes are validated using the Matrix Laboratory Software (MATLAB) environment.