Reactive power grid capacity configuration method for random inertia factor particle swarm optimization algorithm

Abstract

The invention discloses a reactive power grid capacity configuration method for a random inertia factor particle swarm optimization algorithm. The reactive power grid capacity configuration method includes steps that I, acquiring system parameters of a WAMS system in real time and setting particle boundary conditions; II, initializing a swarm and determining an adaptive value of the particle; III, dividing iterative stages; IV, updating the speed and position of the particle; V, judging whether the iteration times arrives at the maximum iteration times of a global search stage; VI, judging whether the iteration times arrives at the maximum iteration times of a primary solution stabilization stage; VII, judging whether the iteration times arrives at the upper iteration limit; VIII, iterating till arriving at the maximum times, and outputting an online reactive capacity configuration method. Compared with a standard algorithm and an adaptive mutation algorithm, the reactive power grid capacity configuration method for the random inertia factor particle swarm optimization algorithm enables the optimization precision to be improved and realizes to improve the early global search capability and the late local search precision based on guaranteeing a convergence rate through combining with actual situations of reactive optimization, and the global optimal solution is ultimately obtained.

Description

technical field [0001] The invention relates to a method in the field of power grid operation state evaluation and early warning of a power grid intelligent dispatch support system, in particular to a power grid reactive capacity configuration method based on a random inertia factor particle swarm optimization algorithm. Background technique [0002] With the rapid development of society, economy and power industry, the power grid has gradually developed into a large-scale, long-distance, UHV AC-DC interconnection and an increased proportion of new energy access, which increases the uncertainty of power grid operation. The receiving-end power grid is mainly centered on the load-concentrated area, and is connected to the long-distance generalized originating power supply through the surrounding tie-lines, so as to achieve the balance between supply and demand of electric energy. Due to the mismatch between the distribution of energy and the load center area, as well as the co...

AI Technical Summary

Problems solved by technology

However, when PSO is applied to high-dimensional complex problems, it is prone to premature convergence and local optimum problems, resulting in the algorithm not being able to guarantee convergence to the global optimum

The main reason for this situation is that the convergence speed is fast in the early stage, and no effective constraints are obtained in the later stage to make the algorithm break away from the minimum point

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