Moussaoui Abderrahmane, Ben Attous Djilani, Benbouhenni Habib, Bekakra Youcef, Nedjadi Benharir, Elbarbary Z M S
Department of Electrical Engineering, LAAS Laboratory, National Polytechnic School of Oran- Maurice Audin, BP 1523 Oran El M'naouer, Algeria.
Department of Electrical Engineering, VTRS Laboratory, University of El Oued, 39000, El Oued, P.O.B. 789, Algeria.
Heliyon. 2024 Oct 25;10(21):e39738. doi: 10.1016/j.heliyon.2024.e39738. eCollection 2024 Nov 15.
This paper presents an adaptive neuro-fuzzy inference system (ANFIS) based on 24 sectors direct torque command (DTC) for a doubly-fed induction machine (DFIM) by using a 3-level neutral point clamped inverter. The DTC approach is used in this paper with 24 sectors based on the ANFIS regulator to minimize the torque fluctuations, flux fluctuations, and stator stream THD (Total Harmonic Distortion) of the DFIM drive. The composed technique is accomplished by replacing the hysteresis controllers of the flux and torque with the ANFIS controller. On the other hand, the results of the designed approach based on ANFIS controls were obtained compared to the traditional approach that uses usual controls, as MATLAB was used to realize these approaches in different working conditions. In all tests, the designed method shows improved performance in minimizing torque and flux undulations while reducing the THD of the stator stream. These results highlight the extent of efficiency, high competence, and effectiveness in improving machine features compared to the conventional approach, where the designed approach minimized the THD of current by ratios of approximately 77.50 %, 48.34 %, 75 %, and 81.43 % in all tests. Also, the torque undulation value was reduced by 30 %, 39.24 %, 31.94 %, and 59.31 % in all tests compared to the DTC. The designed approach minimized the speed overshoot compared to the DTC by percentages estimated at 98.83 %, 97.11 %, 96.75 %, and 95.56 % in all tests. These percentages demonstrate the high efficiency and effectiveness of the 24 sectors DTC-ANFIS compared to the conventional approach in improving the features of the control system, making it a promising solution in all electrical fields in the future.
本文提出了一种基于24扇区直接转矩指令（DTC）的自适应神经模糊推理系统（ANFIS），用于双馈感应电机（DFIM），采用三电平中性点钳位逆变器。本文采用基于ANFIS调节器的24扇区DTC方法，以最小化DFIM驱动器的转矩波动、磁链波动和定子电流总谐波失真（THD）。通过用ANFIS控制器取代磁链和转矩的滞环控制器来实现这种组合技术。另一方面，与使用常规控制的传统方法相比，获得了基于ANFIS控制的设计方法的结果，因为使用MATLAB在不同工作条件下实现了这些方法。在所有测试中，所设计的方法在最小化转矩和磁链波动以及降低定子电流THD方面表现出改进的性能。这些结果突出了与传统方法相比，在改善电机特性方面的效率、高能力和有效性程度，其中所设计的方法在所有测试中将电流THD分别降低了约77.50%、48.34%、75%和81.43%。此外，与DTC相比，在所有测试中，转矩波动值分别降低了30%、39.24%、31.94%和59.31%。与DTC相比，所设计的方法在所有测试中将速度超调量分别最小化了98.83%、97.11%、96.75%和95.56%。这些百分比表明，与传统方法相比，24扇区DTC-ANFIS在改善控制系统特性方面具有很高的效率和有效性，使其成为未来所有电气领域中一个有前途的解决方案。
