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无刷双馈风力发电机中用于电压稳定性的智能最大功率点跟踪及协调控制

Intelligent MPPT and coordinated control for voltage stability in brushless DFIG wind turbines.

作者信息

Rajanala Prashanth, Kumar Malligunta Kiran, Giriprasad Ambati, Choi Joon-Ho, Rao K V Govardhan, Sravan V Sri, Reddy Ch Rami

机构信息

Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India.

Department of Electrical and Electronics Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, 500090, India.

出版信息

Sci Rep. 2025 Jul 2;15(1):22669. doi: 10.1038/s41598-025-08676-x.

DOI:10.1038/s41598-025-08676-x
PMID:40596470
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12217611/
Abstract

This research develops a novel control approach for improving voltage stability and maximizing power extraction in Brushless Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECS). The developed approach incorporates a Chaotic Salp Swarm Optimization (CSSO) tuned Adaptive Neuro Fuzzy Inference System (ANFIS) for Maximum Power Point Tracking (MPPT) allowing rotor speed's dynamic adjustments and torque to attain better wind power extraction. The control framework has coordinated control among the Rotor Side Converter (RSC) and Grid Side Converter (GSC), where the GSC provides the delivery of power to the grid and offers grid support features while the RSC manages torque of rotor side and DC link voltage. To support grid stability under changing conditions, reactive power balancing and voltage regulation are incorporated into the system. By utilizing a d-q reference frame based current control strategy, the harmonic distortion in the grid current is alleviated. Furthermore, the efficacy of developed controller is validated in MATLAB/Simulink tool demonstrating tracking efficiency of [Formula: see text] with improved tracking speed (0.08s), reduced total harmonic distortion (THD < 2.85%), enhanced voltage stability revealing significant improvements in voltage stability, harmonic suppression and wind energy harvesting efficiency under both steady-state and dynamic operating conditions.

摘要

本研究为基于无刷双馈感应发电机（DFIG）的风能转换系统（WECS）开发了一种新颖的控制方法，用于提高电压稳定性并最大化功率提取。所开发的方法采用了一种经混沌鹈鹕群优化（CSSO）调整的自适应神经模糊推理系统（ANFIS）进行最大功率点跟踪（MPPT），允许对转子速度进行动态调整并控制转矩，以实现更好的风能提取。该控制框架在转子侧变流器（RSC）和电网侧变流器（GSC）之间进行协调控制，其中GSC负责向电网输送功率并提供电网支持功能，而RSC则管理转子侧的转矩和直流母线电压。为了在变化的条件下支持电网稳定性，系统中纳入了无功功率平衡和电压调节。通过采用基于d-q参考坐标系的电流控制策略，减轻了电网电流中的谐波失真。此外，在MATLAB/Simulink工具中验证了所开发控制器的有效性，结果表明其跟踪效率为[公式：见原文]，跟踪速度提高（0.08秒），总谐波失真降低（THD＜2.85%），电压稳定性增强，在稳态和动态运行条件下，电压稳定性、谐波抑制和风能采集效率均有显著改善。

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本文引用的文献

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A Dual-Mode Triboelectric Nanogenerator for Wind Energy Harvesting and Self-Powered Wind Speed Monitoring.一种用于风能收集和自供电风速监测的双模摩擦纳米发电机
ACS Nano. 2022 Apr 26;16(4):6244-6254. doi: 10.1021/acsnano.1c11658. Epub 2022 Mar 21.