基于VMD-LSTM和模糊模型的稳定状态电源质量的风险预警方法
Yu Shen1, Wei Hu1, Mingqi Dong2
1Electric Power Science Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan, 430077, China.
Heliyon
|May 13, 2024
概括
本研究提出了一种用于预测电网电力质量趋势和风险的新方法. 该方法将变化模式分解 (VMD) 与长期短期记忆 (LSTM) 网络相结合,用于准确的预测.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 预测建模预测建模
背景情况:
- 对稳定状态电源质量进行有效的风险预警对于电网管理至关重要.
- 现有的方法在预测电力质量趋势和量化相关风险方面缺乏准确性.
- 解决电力质量风险评估中的不确定性是一个重大挑战.
研究的目的:
- 开发一种用于稳定状态电源质量风险预警的新方法.
- 为了提高电力质量指数的预测准确度.
- 系统地解决电力质量风险评估中的不确定性.
主要方法:
- 利用变化模式分解 (VMD) 和长短期内存 (LSTM) 进行电源质量指数预测.
- 集成的内核密度估计 (KDE) 使用模糊模型进行风险预警.
- 使用来自住宅负载的现场监测数据验证了该方法.
主要成果:
- 拟议的VMD-LSTM模型显著提高了电力质量指数的预测准确性.
- KDE-fuzzy模型有效地解决了电源质量风险评估中的不确定性.
- 实验验证证证实了该方法的可靠性和实用性,预测误差中位数为5.03%,准确度超过90%.
结论:
- 新的稳定状态电源质量风险预警方法显示出高可靠性和适用性.
- VMD-LSTM和模糊逻辑的综合方法为电网质量管理提供了强大的解决方案.
- 这项研究为预防和管理现实世界的电力质量问题提供了有价值的工具.
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