WEIGHTS AGGREGATED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZER FOR OPTIMAL POWER FLOW CONSIDERING THE GENERATION COST , EMISSION , TRANSMISSION LOSS AND BUS-VOLTAGE PROFILE

This study proposes weights aggregated multi-objective particle swarm optimizer with static penalty constraint handling technique, linearly altered upper and lower velocity bounds and objectives handled via weights aggregation approach, to solve for the multi-objective optimal power flow problem in power systems for simultaneous minimization of active transmission loss, load buses volt deviation, cost and thermal emissions of active power generation units while acknowledging different operational and security constraints forced by the system and due to the network limited abilities. These objective functions conflict with one another, so a fuzzy based mechanism is represented to extract the best trade-off point among non-dominated solutions, obtained via multiple runs of the proposed algorithm with different weight settings. The multi-objective problem is converted to single objective optimization problem and standard particle swarm optimization with linearly modified upper and lower velocity limits is applied to come up with a final narrower range during iterative procedure for better local exploration of search space. The proposed method of solution is implemented to IEEE 30-bus benchmark system using MATLAB and compared with other nature inspired methods. The obtained results show better performance of the proposed algorithm over the other presented methods while strictly following all the system model constraints.