Research On Optimization Reconfiguration Algorithm Of Power Distribution Network With Distributed Generation

As the traditional energy depleted,power generation and energy crisis brought serious environmental pollution problems,making full use of existing power network of measures,with relatively less network loss and investment cost to provide larger capacity of the high quality of electric energy,should be taken.Reconfiguration of power distribution network can play to the potential of existing power grid equipment,reduce operating costs,improve the reliability and efficiency of the network operation and the power supply.Connected to distribution network of distributed power impact on the trend of the distribution network,the node voltage and network losses.The reconfiguration of power distribution network containing distributed generation is a typical nonlinear optimization problem with multiple constraints,multivariate,discrete and other characteristics.There are many problems in using the current optimization methods.for example,DG treatments affect the time and precision of computation;the traditional optimization algorithms are easy to fall into local optimum or difficult to find global optimal solution,optimization algorithms have a long time of computation,and optimize the accuracy of the results is low.Therefore,we need to study new methods or new improvable strategies.Represented by differential evolution algorithm is the evolution of intelligent algorithm,which is a powerful tool to solve the reconfiguration of distribution network for this type of complex nonlinear optimization problem.But differential evolution algorithm is a relatively new technique based on evolutionary optimization,which theoretical analysis and applied research are still in its infancy and have many issues worthy of studing:such as how to enhance the capacity of algorithm jump local optima,how to improve the accuracy and speed of high-dimensional complex multimodal problems.Firstly,the equivalent load model and the treatment methods of various kinds of DG is established.As to Newton's method with quadratic convergence properties of sensitivity initial value,the paper proposed N-EAPSO flow calculation method with dynamically adjust the distance between the particles based on parameters.Aiming at the problem that the weight and the learning factor is more dependent on the particle swarm optimization,the dynamic adjustment strategy of the weight and the learning factor is adopted.The effectiveness of the algorithm was verified.by IEEE33 bus system and join the DG of IEEE33 node system.Secondly,for solving problems that differential evolution algorithm into local optimum and slow convergence,the PIADE algorithm is used in this thesis.Adaptive mutation factor strategy based on individual advantage,holding elite thoughts and thoughts on chaos are used to improve performance of differential evolution algorithm.Dynamic adjustment in the process of optimization of population and the mutation factor,makes the algorithm can quickly effective convergence and jump out of local optimal solution,and the effectiveness of the algorithm was verified by an example.Finally,as to the problem that differential evolution algorithm of "greedy" selection mechanism easily lead to premature convergence of the algorithm,by using the determined by the form element concentration in randomness and directional balanced ideal search mechanism,this thesis combine plants growth simulation algorithm to the improved differential evolution algorithm to further expand the search scope and enhance the global convergence performance of the algorithm.