article · Energy Reports
Direct Power Control offers a simple and adaptable algorithm for managing power systems, but conventional versions suffer from power ripples, harmonic distortion, and variable switching frequencies. To solve these issues in wind energy conversion systems using permanent magnet synchronous generators, a new control system introduces artificial neural networks. The neural network controllers replace conventional hysteresis comparators and switching tables across both the machine-side and grid-side converters. The approach also applies artificial neural networks to maximum power point tracking under variable wind conditions. Tested in simulations using real wind profile data from Essaouira, Morocco, the controller outperformed traditional methods by improving tracking precision, response times, and overshoot, while lowering harmonic distortion and power ripples. Robustness against parameter changes and overall operational feasibility were confirmed through experimental hardware testing using a dedicated control board.
Wind turbines must efficiently generate clean power despite unpredictable wind conditions. Conventional power control systems often suffer from electrical fluctuations and distortions that can stress equipment and reduce power quality. Upgrading these controls with artificial neural networks creates smoother power output, improves grid compatibility, and protects electrical infrastructure from adverse power ripples.
This technology is relevant to wind turbine manufacturers, power converter developers, and grid operators looking to improve power quality and system stability. The work sits at an applied and tested stage, having progressed from simulations with real-world wind profiles to physical validation on a dSPACE laboratory board, though further operational trials in commercial-scale turbines would be needed before deployment.
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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.
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DOI: 10.1016/j.egyr.2024.03.039
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