Offset-free Data-Driven Predictive Control for Grid-Connected Power Converters in Weak Grid Faults

TL;DR

This paper introduces an offset-free data-driven predictive control method for grid-connected power converters, significantly improving stability and fault-ride-through performance during weak grid faults without relying on physics-based models.

Contribution

It replaces traditional PI controllers with a novel offset-free data-driven predictive controller that enhances stability and fault handling in weak grid conditions.

Findings

Doubles the critical grid impedance handling capacity.

Reduces RMS error during faults by a factor of 40.

Maintains real-time computational efficiency.

Abstract

Grid-connected power converters encounter significant stability challenges during weak grid faults, when conventional PI-based controllers exhibit an oscillatory response and poor fault-ride-through performance. This paper addresses this problem by replacing the conventional outer PI controllers that regulate DC-link and PCC voltages with an offset-free data-driven predictive controller. The developed algorithm leverages either pre-fault or fault-time data to construct input-output predictors, yielding offset-free control without the need for physics-based modelling. Simulation results show that pre-fault offset-free DPC doubles the critical equivalent grid impedance that can be handled and reduces the root mean squared error during faults by a factor of 40, while maintaining computation times comparable to conventional PI control. These findings demonstrate that the developed offset-free data predictive controller offers a simple, robust, and computationally efficient alternative to conventional control, significantly enhancing fault-ride-through capabilities of converters in weak grids.