使用ANFIS-SRF控制的DSTATCOM优化弱电网集成风能系统
Peram Venkata Ramana1, K Mercy Rosalina2
1Department of EEE, Vignan's Foundation for Science, Technology and Research, Guntur, India. Peramvenkataramana23@gmail.com.
Scientific reports
|April 21, 2025
概括
本研究介绍了一种适应性神经模糊推理系统 (ANFIS) 控制器,用于改善连接到弱电网的风能系统的电力质量. 该ANFIS-SRF控制器增强稳定性和减少波,确保可靠的可再生能源整合.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 智能控制系统 智能控制系统
背景情况:
- 弱电网面临风能整合的挑战,包括电压波动和波.
- 传统控制器缺乏适应动态电网条件的实时适应性.
- 分配静态补偿器 (DSTATCOM) 对电网稳定性至关重要,但需要先进的控制.
研究的目的:
- 提出一个智能控制战略,以提高连接到弱电网的风能系统的电力质量.
- 为DSTATCOM.COM开发一个基于ANFIS的同步参考框架 (SRF) 控制器的自适应性神经模糊推理系统 (ANFIS).
- 为了证明ANFIS-SRF控制器在弱电网场景中优于传统方法的优势.
主要方法:
- 为DSTATCOM.实施基于ANFIS的SRF控制策略.
- 使用ANFIS来动态调整反应功率补偿,波减轻和电压稳定.
- 使用模拟来验证控制器在各种负载和风条件下的性能.
主要成果:
- 减少了电网电压总波扭曲,从11.26%降至9.83% (非线性负载) 和4.97%降至2.64% (混合负载).
- 改进了功率因子,达到0.98.8以上.
- 在不同的风条件下保持电网电压和电流稳定,符合IEEE 1547和IEEE 519-2014标准.
结论:
- 由ANFIS-SRF控制的DSTATCOM有效地提高了弱电网的电力质量和稳定性.
- 建议的智能控制策略与风能集成的传统方法相比,提供了更高的性能.
- 这项研究为增加电网稳定性和可再生能源透率提供了一种自学,实时适应性解决方案.
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