Rolling optimization method and system for model predictive control of modular multilevel converters

Abstract

The present invention provides a rolling optimization method and system for model predictive control of modularized multilevel converters, which respectively calculate the predicted value of the total voltage of the bridge arm and the output phase voltage according to the phase current and the reference value of the internal current of the bridge arm; The current and the sampling value of the internal current of the bridge arm calculate the prediction error of the total voltage of the bridge arm and the output phase voltage; perform rolling optimization on the total voltage of the bridge arm and the output phase voltage according to the prediction error of the total voltage of the bridge arm and the output phase voltage, and obtain the final The total voltage of the bridge arm and the predicted value of the output phase voltage; according to the predicted value of the total voltage of the bridge arm and the output phase voltage, calculate the number of sub-modules that need to be opened for each bridge arm; according to the current direction of the bridge arm and the voltage of the sub-module Sorting, selecting the corresponding sub-module as the input sub-module, and issuing a trigger command to control the switching of the input sub-module and other sub-modules. The invention can eliminate the static error caused by the low accuracy of system parameters, and improve the steady-state accuracy of the system.

Description

technical field [0001] The invention relates to the field of converter control, in particular to a rolling optimization method and system for model predictive control of modularized multilevel converters. Background technique [0002] Modular Multilevel Converter (MMC) HVDC transmission system (High Voltage Direct Current Transmission, HVDC) has the characteristics of good power transmission quality, flexible power adjustment capability, simple structure, and easy expansion. , It has been widely used in offshore wind power and other renewable new energy power generation grid connection and long-distance flexible DC transmission occasions. When MMC adopts model predictive control technology, due to problems such as insufficient system parameter detection accuracy and sampling lag, there will be static errors in the phase current, and error compensation and correction are required. [0003] No error feedback rolling optimization method suitable for model predictive control te...

AI Technical Summary

Problems solved by technology

When MMC adopts model predictive control technology, due to problems such as insufficient system parameter detection accuracy and sampling lag, there will be static errors in the phase current, and error compensation and correction are required

[0003] No error feedback rolling optimization method suitable for model predictive control technology of modular level converters has been found so far

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