STATCOM current control method of multi-model fuzzy neural network PI controllers

Abstract

The invention relates to a STATCOM current control method of multi-model fuzzy neural network PI controllers. The STATCOM current control method of the multi-model fuzzy neural network PI controllers comprises the steps that S1 a system is divided into a plurality of models Mi (i=1, 2, ..., n) with a load power factor serving as the basis of model dividing, S2 d-axis second-stage fuzzy neural network PI controllers PIdi (i=1, 2, ..., n) and q-axis fuzzy neural network PI controllers PIdi (i=1, 2, ..., n) are respectively designed for each model, and S3 when load current is connected in, corresponding models are selected, parameters kP and ki of the d-axis second-stage fuzzy neural network PI controllers and the q-axis fuzzy neural network PI controllers in each model are set through the fuzzy neural network to achieve the ideal control effect. The STATCOM current control method of the multi-model fuzzy neural network PI controllers can rapidly adapt to the changes of a load and achieve high accuracy.

Description

technical field [0001] The invention relates to a static synchronous compensation method in reactive power compensation of power system power quality, in particular to a static synchronous compensation current control method based on a multi-model fuzzy neural network PI controller. Background technique [0002] Using Static Synchronous Compensator (STATCOM, Static Synchronous Compensator) to improve power quality has two main purposes: to improve power factor and adjust system voltage. However, in some power consumption occasions, the change of the load does not lead to a significant drop in the system voltage, but the power factor of the system has undergone a large change, so the compensation of the power factor is particularly important. The main control objective of the static synchronous compensator is to improve the power factor of the system by compensating the reactive power of the load. [0003] The main control method of the static synchronous compensator is doub...

AI Technical Summary

Problems solved by technology

However, the parameter setting rules of the fuzzy PI controller need to be obtained through on-site debugging and expert experience, while the neuron adaptive PI control takes a long time to train the neuron

Although the PI control based on the genetic algorithm can obtain the PI parameters, the convergence is slow and the running time is long

The particle swarm optimization PI control can quickly get the optimal solution, but it is easy to fall into the local optimum

The above method can only overcome the nonlinear characteristics of the static synchronous compensator. When the impact load changes and the power factor changes, the above method cannot quickly adapt to the load change and obtain higher accuracy, thus affecting the device. compensation accuracy

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