在电力系统中识别电力质量事件的混合方法:弹性网回归分解和优化的概率神经网络
Indu Sekhar Samanta1, Pravat Kumar Rout2, Kunjabihari Swain3
1Department of Computer Science and Engineering, Siksha' O' Anusandhan University, India.
Heliyon
|September 27, 2024
概括
一个新的混合算法增强了智能电网电力质量 (PQ) 分析,使用基于弹性网回归的变化模式分解和Salp Swarm算法优化了概率神经网络,用于可靠的事件检测和分类.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 人工智能在能源中的作用
背景情况:
- 现代智能电网和微电网面临着关键的电力质量挑战.
- 有效可靠的电力质量 (PQ) 识别方法对于不断发展的发电系统至关重要.
- 现有的方法需要进一步的研究,以提高性能和准确性.
研究的目的:
- 提出一种混合算法,用于精确检测和分类智能电网中的PQ事件.
- 通过弹性网回归 (ER) 提高变化模式分解 (VMD) 的性能.
- 使用Salp Swarm算法 (SSA) 优化一个概率神经网络 (PNN),以提高分类准确性.
主要方法:
- 实现了基于弹性网回归的变化模式分解 (ER-VMD) 用于特征提取.
- 使用Salp Swarm算法 (SSA) 来优化概率神经网络 (PNN) 的参数.
- 开发了一个混合ER-VMD和SSA-PNN模型用于PQ事件识别.
主要成果:
- 拟议的ER-VMD方法增强了内在模式功能重建.
- 在实时数据中,SSA-PNN实现了高精度 (98.58%),灵敏度 (100%) 和特异性 (98.46%).
- 混合算法展示了强大的性能对抗噪音,快速学习,并减少了计算复杂性.
结论:
- 拟议的混合算法为智能电网中的PQ事件检测和分类提供了有效和强大的解决方案.
- 集成ER-VMD和SSA-PNN显著提高了电力质量监测的准确性和可靠性.
- 这种方法解决了智能电网供电质量的关键问题,为更稳定,更高效的电力系统铺平了道路.
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