Reconfiguration Algorithm Of Distribution Network Based On Parallel Intelligent Algorithm In Cloud Environment

Network reconfiguration of distribution network is a very important means to reduce the network loss by using the existing equipment in the network,which can also improve the security of the power supply system.With the rapid development of smart grid and the sharp expansion of grid scale,computational complexity of the network reconstruction algorithm increases significantly,the traditional serial algorithms have gradually failed to meet people’s requirements for fast computing.Ant colony algorithm has been widely used in solving the distribution network reconfiguration problem because of its robustness,parallelism and positive feedback mechanism.With the rise and maturity of "cloud" technology,cloud computing provides favorable support for the realization of parallel computing.However,the existing parallel ant colony algorithm in the cloud environment to solve distribution network reconfiguration suffered from the repeated calculations.For example,as the number of iterations increases,more and more ants under the guidance of pheromones will find a fixed or several network structure,in this case,the search for different ants will be repeated.As the same network structure will produce the same network loss,the repeated flow calculation will increase,which greatly wastes the cloud computing resources.In addition,as the number of iterations increases,these algorithms still tend to fall into the local optimum.In order to solve the deficiency of parallel ant colony algorithm that calculate network reconfiguration in cloud environment,in this paper we first propose a reduction-accumulation dual strategy based ant colony algorithm.On one hand,by defining a reduction factor,number of the ants can decrease continuously given the convergence stability in the iterative process.It implements a dynam ically adaptive mechanism in selecting ant number and speeds up the computation;On the other hand,the algorithm defines an accumulation factor and increases a stage of pheromone accumulation.This can guide the algorithm to jump out of the local optimal and improve the probability of finding the optimal topology.Secondly,in order to reduce the repetitions calculation of power flow,we propose a parallel ant colony memory search algorithm.It records part of the optimal network structure and corresponding network loss by setting up a lookup table,afterwards,if the new structure can be found in the table,the algorithm can skip the power flow calculation and get the net loss directly.In addition,according to the mutual independence principle between different processes in parallel computing,by Stepwise handling pheromones of different processes can expanding the ants search range and reducing the probability of falling into the local optimumWith the same numbers of updated pheromone and initial ants,the experimental results show that the proposed algorithm can improve the computation speed by about 33% and reduce the minimum net loss by about 9%,compared with the existing work.