In this paper, an artificial neural network-based control strategy is proposed for low voltage DC microgrid (LVDC microgrid) with a hybrid energy storage system (HESS) to improve power-sharing between battery and supercapacitor (SC) to suit the demand-generation imbalance, maintain state-of-charge (SOC) within boundaries and thereby to regulate the dc bus voltage. The conventional controller cannot track the SCs current rapidly with the high-frequency component that will place dynamic stress on the battery, further resulting in shorter battery life. The significant advantage is that in the proposed control strategy, redirections of unwaged battery currents to SCs for fast compensations enhance battery life span. The proposed control strategy effectiveness was investigated by simulations, including a comparison of overshoot/undershoot and settling time in dc bus voltage with a conventional control strategy. The results have been experimentally verified by hardware-in-loop (HIL) on a field-programmable gate array (FPGA)-based real-time simulator.

本文针对混合动力储能系统（HESS）提出了一种基于人工神经网络的低压直流微电网（LVDC微电网）控制策略，以改善电池和超级电容器（SC）之间的功率共享以适应需求的产生在不平衡的情况下，将充电状态（SOC）维持在边界内，从而调节直流母线电压。传统的控制器无法利用高频分量快速跟踪SC电流，而高频分量会在电池上施加动态应力，从而进一步缩短了电池寿命。显着的优点是，在建议的控制策略中，将未消耗的电池电流重定向到SC以获得快速补偿，从而延长了电池寿命。通过仿真研究了所提出的控制策略的有效性，包括使用传统控制策略比较直流母线电压中的过冲/下冲和建立时间。结果已在基于现场可编程门阵列（FPGA）的实时仿真器上通过硬件在环（HIL）进行了实验验证。