Loss Minimization by Optimal Power Flow Using Search Intelligences

This paper describes Loss minimization by optimal power flow based on Search intelligences in which the power transmission loss function is used as the problem objective. Although most of optimal power flow problems involve the total of the entire Optimal power system, in some cases some different objective may be chosen. In this paper, to minimize the overall power transmission losses, four decision variables are participated. They are i) power generated from generating plants, ii) specified voltage magnitude at control substations, iii) tap position of tap changing transformers and iv) reactive power injection from reactive power compensators. Search intelligences are well known and widely accepted as potential intelligent methods for solving such a problem. In this paper, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Dingo Optimization Algorithms (DOA) are employed to solve optimal power flow problems. 14-bus and 30-bus IEEE power systems are used for test. As a result, all search intelligences algorithms can solve optimal power flow problems efficiently. IEEE 14-bus test system use optimal power flow based on Search intelligences loss before is 14.72MW. After apply Optimization Algorithms power loss solutions were 12.73 MW, 12.77 MW, 12.72 and 12.71 MW for the GA, PSO, ABA and DOA. IEEE 30-bus test system use optimal power flow based on Search intelligences loss before is 6.50MW. After apply Optimization Algorithms power loss solutions were 1.71 MW, 1.82 MW, 1.68and 1.66 MW for the GA, PSO, ABA and DOA. The Dingo Optimization Algorithms and the differential evolution provide results better than other search intelligent techniques.