Elnaghi Basem E, Ismaiel Ahmed M, El Sayed Abdel-Kader Fathy, Abelwhab M N, Mohammed Reham H
Electrical Power and Machines Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt.
Electrical Power and Machine Department, Faculty of Engineering, Menoufia University, Menoufia, 32611, Egypt.
Sci Rep. 2025 Jan 3;15(1):711. doi: 10.1038/s41598-024-82382-y.
This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers. This comparative analysis is crucial in establishing the efficacy of the proposed method. To validate the proposed approach, experimental assessments are conducted using the DSpace DS1104 control board, allowing for real-time application of the control strategies. The results indicate that the EVOA-AFLCs outperform the C-BO-based AFLCs, GA-based AFLCs, and MPA-based optimal PIs in several key performance metrics. Notably, the EVOA-AFLCs exhibit rapid temporal response, a high rate of convergence, reduced peak overshoot, diminished undershoot, and significantly lower steady-state error. The EVOA-AFLC outperforms the C-BO-AFLC and GA-AFLC in terms of efficiency, transient responses, and oscillations. In comparison to the MPA-PI, it improves speed tracking by 86.3%, the GA-AFLC by 56.36%, and the C-BO by 39.3%. Moreover, integral absolute error (IAE) for each controller has been calculated to validate the system wind turbine performance. The EVOA-AFLC outperforms other approaches significantly, achieving a 71.2% reduction in average integral absolute errors compared to the GA-AFLC, 24.4% compared to the C-BO-AFLC, and an impressive 84% compared to the MPA-PI. These findings underscore the potential of the EVOA as a robust and effective optimization tool for enhancing the performance of adaptive fuzzy logic controllers in DFIG-based wind power systems.
本研究提出了一种名为能量谷优化器方法（EVOA）的新型优化算法，旨在有效开发六个最优自适应模糊逻辑控制器（AFLC），这些控制器包含用于风力发电厂（WPP）中并网双馈感应发电机（DFIG）的30个参数。基于EVOA的AFLC的主要目标是在风能应用中使DFIG的功率提取最大化，同时改善动态响应并在运行期间将误差最小化。对基于EVOA的AFLC的性能进行了全面研究，并与替代优化技术进行了基准测试，特别是混沌台球优化（C-BO）、遗传算法（GA）和基于海洋捕食者算法（MPA）的最优比例积分（PI）控制器。这种比较分析对于确定所提出方法的有效性至关重要。为了验证所提出的方法，使用DSpace DS1104控制板进行了实验评估，从而能够实时应用控制策略。结果表明，在几个关键性能指标方面，基于EVOA的AFLC优于基于C-BO的AFLC、基于GA的AFLC和基于MPA的最优PI。值得注意的是，基于EVOA的AFLC表现出快速的时间响应、高收敛速率、降低的峰值超调量、减小的下冲量以及显著更低的稳态误差。基于EVOA的AFLC在效率、瞬态响应和振荡方面优于基于C-BO的AFLC和基于GA的AFLC。与基于MPA的PI相比，它将速度跟踪提高了86.3%，与基于GA的AFLC相比提高了56.36%，与C-BO相比提高了39.3%。此外，还计算了每个控制器的积分绝对误差（IAE）以验证系统风力涡轮机的性能。基于EVOA的AFLC明显优于其他方法，与基于GA的AFLC相比，平均积分绝对误差降低了71.2%，与基于C-BO的AFLC相比降低了24.4%，与基于MPA的PI相比降低了84%，令人印象深刻。这些发现强调了EVOA作为一种强大而有效的优化工具在增强基于DFIG的风力发电系统中自适应模糊逻辑控制器性能方面的潜力。
