The demand for wind turbines has been ultimately increased over the last decades. Accordingly, the power converter controller plays the primary role in extracting energy out of the generator, using efficient and reliable techniques as Maximum Power Extraction (MPE) and delivering the power to the grid. This research pursues to present a Cascade-Forward Neural Network (CFNN) MPE that maintains the MPE's advantages besides providing the flexibility of limiting the output power at significantly lower complexity in the control loop. The proposed strategy uses the cascade-forward neural network to learn the wind turbine's aerodynamic nonlinear dynamics and achieves accurate power tracking. Additionally, it reformulates the machine d-q axes voltages equations to operate the wind energy conversion systems (WECS) in optimal condition by considering the wind speed, air temperature, power demand, and disturbances. Furthermore, it does not require any tuning procedure. The power tracking performance of the recommended CFNN MPE controller is evaluated through several experimental and simulation tests in different situations, and all the results are matched with the manufacturer's datasheets and another proven strategy to confirm its effectiveness.

中文翻译：

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使用级联前向神经网络的风能转换系统的最大功率跟踪

在过去的几十年中，对风力涡轮机的需求最终增加了。因此，功率转换器控制器在从发电机中提取能量方面发挥着主要作用，使用高效可靠的技术作为最大功率提取 (MPE) 并将功率输送到电网。本研究旨在提出一种级联前向神经网络 (CFNN) MPE，该 MPE 除了提供限制输出功率的灵活性之外，还保持了 MPE 的优势，同时控制回路的复杂性显着降低。所提出的策略使用级联前向神经网络来学习风力涡轮机的气动非线性动力学并实现准确的功率跟踪。此外，它通过考虑风速、空气温度、功率需求和干扰，重新制定机器 dq 轴电压方程，以在最佳条件下运行风能转换系统 (WECS)。此外，它不需要任何调整程序。推荐的 CFNN MPE 控制器的功率跟踪性能是通过在不同情况下的多次实验和模拟测试来评估的，所有结果都与制造商的数据表和另一个经过验证的策略相匹配，以确认其有效性。