基于DFIG的风电场电网连接系统的双阶段次同步振荡评估方法
Ge Liu1,2, Jun Liu3, Andong Liu1
1College of Automation, Xi'an University of Technology, Xi'an, 710048, China.
Scientific reports
|September 27, 2024
概括
这项研究引入了一种新的方法,用于准确评估具有双源感应发电机 (DFIG) 的电力系统中的次同步振荡 (SSO). 该技术通过精确识别和减轻SSO来提高电网稳定性,提高电力系统可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 整合可再生能源的整合
背景情况:
- 风力发电场中的双源感应发电机 (DFIG) 可以诱导次同步振荡 (SSO),威胁电网稳定性.
- 准确评估SSO对于保持电网可靠性和防止潜在的中断至关重要.
研究的目的:
- 提出和验证一种新的,准确的方法来评估SSO在电力系统与DFIGs.
- 通过有效识别和减轻SSO事件来提高电网的稳定性.
主要方法:
- 使用了结合上置信界限 (UCB) 和双深Q网络 (DDQN) 的分类模型来识别相位测量单元 (PMU) 数据中的干扰水平.
- 开发了一个局部特征融合变压器 (LFF-Transformer) 网络用于SSO参数估计,以适应不同干扰级别的数据.
主要成果:
- 实现了较低的错误率:eRMSE-f (0.001),EMAPE-F (0.003),eRMSE-δ (0.009) 和EMAPE-δ (0.015). 在此过程中,我们获得了较低的错误率.
- 与多SVR和多CNN方法相比,显著提高了训练 (90年代) 和测试 (18年代) 时间.
- 应用后的改进包括降低频率偏差 (0.05至0.02赫兹),电压偏差 (3.5%至1.5%),功率波动 (10至5兆瓦),SSO频率 (<0.5赫兹) 和增加SSO减压比率 (0.08至0.15).
结论:
- 拟议的评估方法通过准确评估和管理SSO,有效提高电网稳定性.
- 基于LFF转换器的方法为基于DFIG的风力发电系统中SSO分析提供了计算效率高,准确的解决方案.
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