Intelligent Primary Control of Voltage Source Converters in AC Microgrids

This paper proposes an intelligent primary control strategy for voltage source converter (VSC)-based ac microgrid (MG). This is implemented by using a proportional resonant (PR) regulator adopted in the inner level of primary control of VSCs. An approach based on brain emotional learning (BEL) is proposed to provide an online and adaptive tuning of control coefficients of the PR regulator. The proposed BEL approach is fully model-free, indicating that the coefficients are regulated without previous knowledge of the system model and parameters. The outer level of primary control employs a droop control loop to regulate power-sharing among different distributed generators. Unlike the conventional control methods with constant coefficients, which are typically designed for a specified operating condition, the proposed approach avoids the dependency of the converter control system on the operating conditions and accommodates varying loading conditions. A sensitivity analysis is also performed to investigate the effects of PR coefficients on the system stability. Moreover, a Mesh analysis is carried out to examine the stability of dominant frequency modes of the whole AC-MG using the proposed control scheme. Simulations are provided to demonstrate the performance of the proposed control scheme.