A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters

TL;DR

This paper introduces DeePConverters, a data-driven predictive control approach for grid-connected power converters that adaptively optimize performance amid complex and variable power grid conditions.

Contribution

It proposes a novel data-enabled predictive control architecture for power converters, enabling implicit grid perception and adaptive, robust control without relying on simplified models.

Findings

DeePConverters demonstrate improved stability and performance in simulations.

Hardware-in-the-loop tests validate the effectiveness of the control approach.

Abstract

Grid-connected power converters are ubiquitous in modern power systems, acting as grid interfaces of renewable energy sources, energy storage systems, electric vehicles, high-voltage DC systems, etc. Conventionally, power converters use multiple PID regulators to achieve different control objectives such as grid synchronization and voltage/power regulation, where the PID parameters are usually tuned based on a presumed (and often overly-simplified) power grid model. However, this may lead to inferior performance or even instabilities in practice, as the real power grid is highly complex, variable, and generally unknown. To tackle this problem, we employ a data-enabled predictive control (DeePC) to perform data-driven, optimal, robust, and adaptive control for power converters. We call the converters that are operated in this way DeePConverters. A DeePConverter can implicitly perceive the characteristics of the power grid from measured data and adjust its control strategy to achieve optimal, robust, and adaptive performance. We present the modular configurations, generalized structure, control behavior specification, inherent robustness, detailed implementation, computational aspects, and online adaptation of DeePConverters. High-fidelity simulations and hardware-in-the-loop (HIL) tests are provided to validate the effectiveness of DeePConverters.