基于CNN和趋势特征分析的高比例的清洁能源电网连接振荡风险预测技术的研究
Wang Pu1, Xie Yingnan1, Zhao Chongjuan2
1State Grid Huzhou Electric Power Supply Company, Huzhou, China.
Scientific reports
|December 13, 2023
概括
这项研究引入了一种用于动力系统的新振荡预测技术. 它可以提前检测振荡能量,通过提前8分钟提供警告,防止发电机触发和减负.
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:
- 电力系统的振荡带来了重大风险,包括发电机触发和负载流失.
- 传统方法侧重于故障后的管理,这是反应性和效率较低的.
- 积极的振荡预测对于提高电网稳定性和保护至关重要.
研究的目的:
- 为电力系统开发早期振荡预测技术.
- 积极干预并防止广泛的电网损坏和不稳定.
- 提高现代电网的可靠性和弹性.
主要方法:
- 介绍了干扰功率密度的概念,以将干扰能量与时间域电量相关联.
- 通过分析电压-电流相角和阻抗细微差别,提高了故障检测速度.
- 利用一维卷积神经网络 (CNN) 与集成的注意力机制进行振荡预测.
主要成果:
- 拟议的技术有效地预测了电力系统的振荡.
- 在振荡发生前最多八分钟发出早期警告.
- CNN模型准确地捕捉了电压,活性功率和反应功率之间的复杂关系.
结论:
- 开发的振荡预测技术为电力系统稳定提供了积极的方法.
- 这种方法显著减少了因电网干扰和断电造成的损害.
- 在电网管理中,CNN与注意力机制的整合显示出实际应用的前景.
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