Automated test-driven design for robust optimized current control of GTIs operating under normal and abnormal grid conditions

Abstract

Many current controllers are designed based on linear models, which may lead to poor performance when limiting its variables to a safe operation region, or even lead to instability when subject to abnormal operation conditions. Taking that as motivation, the contribution of this work is a new automated test-driven design procedure for robust and optimized current controllers applied to LCL-filtered grid-tied inverters. The design of the control gains towards an optimal solution is oriented by high-fidelity simulations of the converter, covering tests under normal and abnormal operating conditions, such as: reference tracking, grid impedance variations, voltage sags, harmonic compliance, and current and voltage limitations. A particle swarm optimization algorithm is used to evolve the control gains in a computationally efficient way, and linear matrix inequalities are employed to accelerate the optimization process and to provide a theoretical certificate of robust stability. Results are obtained in both controller hardware-in-the-loop testbed and an experimental 5.4 kW prototype, illustrating cases where the proposed design ensures superior performance under normal and abnormal grid conditions when compared with three other current control designs from the literature.