The voltage source inverters in microgrids often rely on the droop control method integrated with voltage and inner current control loops in order to provide a reliable electric power supply. This research aims to present a Cascade-Forward Neural Network (CFNN) droop control method that manages inverter-based microgrids under grid-connected/islanded operating modes. The proposed method operates the inverter in a bi-directional technique for a wide range of battery energy storage systems or any other distributed generation systems. The proposed strategy uses the CFNN to learn the inverter’s nonlinear model to achieve accurate demand and reference power tracking under different operating conditions for smart grid applications. Additionally, it reformulates the grid control concept to drive the inverter based on the optimal conditions by considering the power demand, reference power, equipment size, and disturbances. Also, it does not require any tuning procedure. The power tracking and operating performance of the proposed CFNN controller are evaluated through several experimental tests using the power hardware-in-the-loop (PHIL) methodology in different scenarios. All results are matched with the proven conventional strategy to confirm its effectiveness.

中文翻译：

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使用级联前向神经网络的下垂控制双向逆变器微电网

微电网中的电压源逆变器通常依靠电压和内部电流控制回路集成的下垂控制方法来提供可靠的电力供应。本研究旨在提出一种级联前向神经网络 (CFNN) 下垂控制方法，用于在并网/孤岛运行模式下管理基于逆变器的微电网。所提出的方法以双向技术操作逆变器，适用于范围广泛的电池储能系统或任何其他分布式发电系统。所提出的策略使用 CFNN 来学习逆变器的非线性模型，以在智能电网应用的不同运行条件下实现准确的需求和参考功率跟踪。此外，它重新制定了电网控制概念，通过考虑电力需求、参考功率、设备尺寸和干扰，在最佳条件下驱动逆变器。此外，它不需要任何调整程序。所提出的 CFNN 控制器的功率跟踪和运行性能通过在不同场景中使用功率硬件在环 (PHIL) 方法的多次实验测试进行评估。所有结果都与经过验证的常规策略相匹配，以确认其有效性。