墨西哥axolotl优化算法与回忆增强的循环神经网络用于模块化多层逆变器供电的光伏系统
R Madavan1, B Karthikeyan2, R Palanisamy3
1Department of EEE, K.Ramakrishnan College of Technology, Trichy, 621112, India.
Scientific reports
|April 24, 2025
概括
使用墨西哥Axolotl优化 (MAO) 和回忆增强循环神经网络 (RERNN) 的新混合控制方法优化了光伏 (PV) 连接到电网的系统. 这种先进的技术提高了功率转换效率和电网稳定性,同时减少了波扭曲.
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
- 控制系统 控制系统
背景情况:
- 连接到电网的光伏 (PV) 系统对于可再生能源的整合至关重要.
- 传统的多层逆变器 (MLI) 在效率,成本和复杂性方面面临挑战.
- 需要先进的控制策略来优化电力转换和电网合规性.
研究的目的:
- 为光伏系统中模块化多层逆变器 (MMI) 引入一种新的混合控制方法,MAO-RERNN.
- 为了提高功率转换效率,系统稳定性,并减少总波扭曲 (THD).
- 展示MMI与传统MLI拓学的优势.
主要方法:
- 为光伏系统实施模块化多层逆变器 (MMI) 拓.
- 开发一种混合控制策略,将墨西哥Axolotl优化 (MAO) 集成为离线参数调整和回忆增强循环神经网络 (RERNN) 进行实时控制.
- 使用梯形波形方法来实现平滑的正弦输出和干扰减轻.
主要成果:
- 与现有方法相比,MAO-RERNN控制策略显示出优越的功率调节.
- 实现了总波扭曲 (THD) 的显著降低.
- 观察到增强的系统稳定性和稳定性,在干扰的情况下保持电网合规性.
结论:
- 拟议的MAO-RERNN控制方法为连接到电网的光伏系统提供了高效和稳定的解决方案.
- 与先进的混合控制相结合的MMI拓学减少了组件数量和复杂性.
- 这种方法代表了下一代光伏逆变器系统的有希望的进步.
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