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使用混合ANFIS-PI控制策略,提高连接到电网的基于双源感应发电机的风力发电厂的MPPT性能
Likenesh Walle Biyazne1, Milkias Berhanu Tuka2,3, Yoseph Mekonnen Abebe1
1Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Scientific reports
|January 19, 2026
概括
这项研究使用一种新型混合适应性神经模糊推理系统 (ANFIS) 增强风能发电,并配备双源感应发电机 (DFIG) 的比例整合 (PI) 控制器. 这种新方法在可变风条件下显著提高了最大功率点跟踪 (MPPT) 的效率.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 在电力系统中的人工智能
背景情况:
- 连接到电网的风力发电厂,特别是使用双源感应发电机 (DFIG) 的风力发电厂,由于风速波动,参数不确定性和电网干扰,在保持最大功率点跟踪 (MPPT) 效率方面面临挑战.
- 优化从可变风力资源获取能源对于风力发电的经济可行性和电网整合至关重要.
研究的目的:
- 开发和评估一种有效的控制技术,以提高连接到电网的基于DFIG的风力发电厂的MPPT性能.
- 解决现有控制方法在处理快速风速变化和电网干扰方面的局限性.
主要方法:
- 模拟了一个基于DFIG的修改动态风力轮机系统.
- 为背向背转换器开发了一种混合适应性神经模糊推理系统 (ANFIS),与比例整合 (PI) 控制器集成.
- 来自埃塞俄比亚阿达马II风力发电厂的真实世界风速和发电数据被用于培训和验证.
主要成果:
- 拟议的ANFIS-PI控制器实现了2.22MW的最大功率点跟踪 (MPPT),超过了达到2.2MW的模糊逻辑控制器 (FLC) -PI.
- 使用ANFIS-PI,最大功率系数提高到0.5504,而使用FLC-PI的最大功率系数是0.5473,而基线为0.4109.
- 使用MATLAB-Simulink R2024a.进行了模拟.
结论:
- 混合ANFIS-PI控制器提供了一种优越的控制策略,用于在动态风条件下增强基于DFIG的风力发电厂的MPPT性能.
- 开发的技术在电力采集和效率方面取得了显著的改进,有助于更可靠,更有效地整合风能.
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