Multi-objective Optimal Reconfiguration Research In Distribution Networks

With the rapid development of national economy, power load increases continuously, how to reduce network losses, in order to improve the economic benefit of power supply enterprise and improve the quality of power supply is increasingly outstanding. Distribution network reconfiguration is an important method of distribution system optimal operation, without the need for additional investment, it can greatly improve the quality of distribution network operation, so more and more attention. Distribution network reconfiguration is one of the important components of distribution automation. Under the normal operation condition, the distribution controller operates the switch in order to adjust network structure according to the running situation. It can balance load and improve the voltage quality.At the same time, it also can reduce the network loss and improve systematice eonomy. It must isolate the trouble while breaking down, narrow the range of losing electrical power, and resume supplying power rapidly after the trouble.According to the basic particle swarm optimization algorithm can not be directly applied to solve multi-objective optimization problem, the multi-objective particle swarm optimization algorithm based on Pareto control is applied. A multi-objective optimal reconfiguration model for distribution network system with minimization of power loss, minimum voltage value of the node and numbers of switches changing has been built, and the multi-objective particle swarm optimization(MOPSO) is applied to solve the model. The key of the multi- objective particle swarm optimization is how to select the personal best and the global best. This paper,conbining with Pareto concept, the personal best is selected by Pareto dominance criterion. Taking the Hamming distance of each particle of the external archive as its fitness value, then the global best is selected by roulettle proportional to the fitness,maintaining the diversity of the population. With a time limit global best phase-out strategy making particles jump out local optima, avoiding the algorithm into premature convergence and maintaining a good convergence. Simulated calculation results by using the IEEE 33 bus test system demonstrate that it is a feasible and efficient method.