Active filter optimization method and system based on quantum-behaved particle swarm algorithm

Abstract

The invention discloses an active filter optimization method and a system based on a quantum-behaved particle swarm algorithm. The method comprises the following steps: initializing a model of an active filter according to an application background; initializing QPSO parameters and a particle swarm, and randomly assigning values to the particle swarm within a set position range; determining an active filter parameter with the best filtering effect under the application background by utilizing iterative optimization of a quantum-behaved particle swarm algorithm; and obtaining an optimal designparameter of the active filter. The filtering effect of the active filter is improved by using multiple advanced technologies, the parameters of the active filter are optimally configured, the optimalparameters are obtained under different working backgrounds, and harmonic components existing in a power grid are filtered to the greatest extent, so that the grid-connected current is close to standard sine current, and safe and stable operation of other electrical equipment is ensured. The method is high in iterative convergence speed, good in filtering effect and suitable for actual engineering application.

Description

technical field [0001] The invention relates to the technical field of active filter design of a power supply system, in particular to an active filter optimization method and system based on a quantum particle swarm algorithm. Background technique [0002] With the wide application of power electronic devices, their nonlinear characteristics are exposed, and high-frequency switching makes power electronic devices generate reactive components and a large number of harmonics during operation, which will affect the standard sinusoidal current on the power supply side. It further affects the safe operation and normal use of other electrical equipment, so how to optimize the power quality under the influence of harmonics has become a problem that needs to be solved. In order to reduce the harm caused by harmonic pollution to the power system, harmonic compensation devices, such as passive filters or active filters, are often placed on the harmonic source side. Passive filters a...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the overall structure of the active filter, its internal parameters are very cumbersome to design, and these parameters have a crucial impact on the filtering performance of the active filter. Therefore, when facing different application backgrounds, It is particularly important to determine the parameters of the active filter

[0003] Traditional active filter design often only optimizes a certain link, such as the optimization of the AC side inductance and capacitance or the optimization of the control link. However, the control link of the active filter often has a certain coupling relationship with the AC side inductance and capacitance, and independent optimization is difficult to satisfy. global optimum

Moreover, when the fundamental frequency, load, etc. change, the parameters of the active filter need to be redesigned and adjusted, which requires a lot of experiments. Therefore, an active filter optimization method and system based on quantum particle swarm algorithm is proposed, which can quickly Find the optimal parameters in the current application background and get the optimal filtering effect

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