Research On The DPAS-based Hybrid Algorithm For Var Optimization Of Active Distribution Network

With the deteriorating environment and dwindling fossil fuels in recent years, distributed generation (DG) which is mainly on clean energy consumption will account for a larger proportion in the power grid. Active distribution network (ADN) is becoming an effective method to realize the multilevel consumption of the renewable energy for the advantages of flexible power network and active control. Dynamic var optimization is an important part of the ADN planning, it will be inevitably influenced with the increasing penetration rate of DG.Firstly, the paper classifies var compensation types of DG on the basis of grid-connected formation and then Variable-speed Constant-frequency Double-Fed Induction Generator (DFIG) is chosen to analyze detailedly, furthermore establishes a mathematical model of DFIG in the rotating frame and achieves the decoupling control of active power and reactive power. Based on the mathematical model, the limit of reactive power output is calculated, the reactive power control strategy of wind farms is formed.Secondly, In allusion to the defects of artificial intelligent algorithms in var optimization, such as the prematurity and slow convergence speed, Glowworm Swarm Optimization (GSO) and Quantum-behaved Particle Swarm Optimization (QPSO) are combined to form a novel two-stage hybrid algorithm. In order to remain the advantages of the two kinds of algorithms and weaken their defects, the strategy of connecting GSO and QPSO in series is put forward in the hybrid algorithm. GSO is fully used in the former steps to search for all the global optimal solutions, thus the comprehensiveness of the optimization is ensured. Due to the advantages of the fast speed and high accuracy of the convergence, QPSO is applied in the Middle-Later Period of the iteration to ensure the convergence precision of the algorithm. During the search process of the QPSO, population substitution operator based on the golden point theory is applied to prevent the low efficiency and local optimum of the algorithm. Finally, simulation results of IEEE 33-bus system show effectiveness and rationality of the two-stage hybrid algorithm.Finally, this paper takes the load forecast module of DPAS as data support, the restriction of the revelant equiments action numbers as evidence, uses Fisher clustering algorithm to realize load segmentation, then through the flexible control of DG reactive power output, strategy of ADN dynamic var optimizaton is formed.