Adaptive Voltage Control of Single-Inductor 3x Multilevel Converters Interfaced DC Microgrids Using Multi-Agent Approximate Q-Learning

Abstract

Single-inductor 3x multilevel boost (SI-3xMLB) converters, recognized for their potential to supply multiple output voltage levels, are gaining much attention in microgrids due to their capability to diverse sub-systems with changing voltage requirements. However, some loads, such as constant power loads (CPLs), threaten the stability of SI-3xMLB converters due to their negative impedance properties. Thus, advanced regulation mechanisms are necessitated to reduce the destabilization effect of such loads in the microgrids. In response to the issue, in this paper, an adaptive data-driven controller with an observer is designed to mitigate the effect of non-ideal CPLs in dc/dc SI-3xMLB converters. The suggested data-driven controller can stabilize the system outputs using the input-output (I/O). In particular, the multi-agent fuzzy approximate Q-learning (MAFQ-learning) is employed to adjust the coefficients embedded in the data-driven controller dynamically. To do this, a reward function is defined according to the voltage control requirements of the dc/dc SI-3xMLB converter. By interacting the agent of MAFQ-learning with the environment, tunable control coefficients are adaptively adjusted. This process finds optimal policy, allowing the controller to dynamically respond to the effects of CPLs. The real-time responses based on the Arduino platform revealed the superior performance of the suggested controller to stabilize the dc/dc SI-3xMLB boost converter feeding CPL than the discrete time-averaged model predictive control (DTA-MPC), the optimal PI control based on state-action-reward-state-action (SARSA) technique.

Keywords

• Multiple DC microgrids
• multi-agent systems
• fuzzy approximate Q-learning
• real-time control
• discrete time-averaged model predictive control (DTA-MPC)