Simple-target three-level active power filter control method based on finite control set model prediction

Abstract

The invention discloses a simple-target three-level active power filter control method based on finite control set model prediction. The simple-target three-level active power filter control method based on finite control set model prediction is characterized by performing optimization and integration on the implementation method of three control targets: harmonic current compensation, direct current side voltage stabilization and direct current side midpoint potential balancing of a three-level active power filter; and realizing optimization control of the three control targets only by taking the output current as a constraint term in a constraint function. The simple-target three-level active power filter control method based on finite control set model prediction simplifies the constraint function, saves selection of the weighting factor in the constraint function, reduces the computational complexity brought by weighting factor setting and computation of multiple constraint terms, improves dynamic response of the system, and realizes balancing control of multiple targets.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps improve the performance of an Active Filter (AF) controller called 3LAPF(Three Level Power Factor Correction). It achieves this improvement by optimizing certain aspects such as AC line impedance or direct power supply voltages at both ends while also ensuring stable operation over time without requiring complicated calculations.

Problems solved by technology

This patented describes how MPC can be applied to optimize multi-dimensional constrained optimizations with various goals such as energy savings or improved efficiency over existing methods like pulse width modulated power supply systems. However, current models have limitations when dealing with complex real world scenarios where there are numerous variables involved which may lead to incorrect calculations leading to poor results. Additionally, some techniques require complicated mathematical expressions involving certain types of data points at particular locations within the process being controlled.

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