With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board.

中文翻译：

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基于人工神经网络的直接功率控制可增强真实风速和参数不确定性下 PMSG-风能转换系统的性能：实验验证

随着人们越来越重视将先进技术嵌入到系统控制中，直接功率控制（DPC）方法因其简单且适应性强的算法而受到广泛关注。这种方法在众多应用中得到了越来越多的认可。然而，由磁滞控制器和开关表引起的可变频率、电流谐波失真和功率纹波降低了其有效性和鲁棒性，影响了系统的性能。为此，本文提出了一种基于人工神经网络（ANN）方法的新DPC。在这种方法中，磁滞比较器和开关表被人工神经网络控制器取代，然后应用于两侧：基于永磁同步发电机的风能转换系统的机侧变流器（MSC）和网侧变流器（GSC）（ PMSG-WECS）。此外，为了使系统在不同的风力条件下更加高效，本研究扩展了人工神经网络（ANN）的使用，以涵盖最大功率点跟踪（MPPT）控制策略。为了证明所提出的方法对系统行为的有效性，使用摩洛哥城市（索维拉）的真实风廓线在 Matlab/Simulink 环境中进行了模拟测试。与经典的DPC控制相比，仿真结果表明，所提出的ANN-DPC控制在参考跟踪、响应时间、超调、精度方面具有优越的性能，并且能够降低功率纹波率和总谐波失真（ THD）在注入电流中。此外，这项工作还包括鲁棒性测试，以检查所提出的控制对参数变化的鲁棒性。总之，通过使用 dSPACE DS1104 板的实验验证，证实了 ANN-DPC 控制方法的可行性和有效性。