基于快速S转换和CPO优化的卷积神经网络的海上风电网连接的电力质量干扰的分类
Minan Tang1, Hongjie Wang1, Jiandong Qiu2
1College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, China.
PloS one
|December 5, 2024
概括
这项研究引入了一种使用快速S转换和猪优化器 (CPO) 的新算法,以改善海上风电网的电力质量干扰检测. 这种新方法提高了对电力系统干扰的分类准确性.
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
- 信号处理 信号处理
背景情况:
- 海上风力发电的大规模整合带来了重大的电力质量挑战.
- 精确检测和分类电力质量干扰对于电网稳定至关重要.
研究的目的:
- 开发和验证一种新的算法,用于检测和分类由海上风电集成引起的电力质量干扰.
- 提高电网电力质量干扰分析的准确性和效率.
主要方法:
- 分析与海上风电网连接的干扰机制和波形特征.
- 使用快速S转换的特征提取和时间频率图表生成.
- 使用Crested Porcupine Optimizer (CPO) 优化卷积神经网络 (CNN) 的超参数.
- 使用CPO优化的CNN模型对电源质量干扰的分类.
主要成果:
- 与标准CNN方法相比,拟议的CPO-CNN模型实现了更好的分类准确性.
- 模拟结果证明了算法在识别各种功率质量干扰信号方面的有效性.
- 该方法成功地执行了特征提取,选择和时间频率图的分类.
结论:
- 开发的快速S转换和CPO优化的CNN算法对电源质量干扰的检测和分类非常有效.
- 这种方法在分类准确度上有了显著的改进,有助于电源质量评估和控制.
- 该方法为管理海上风能集成产生的电力质量问题提供了强大的解决方案.
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