CN113496102A - 一种基于改进BiGRU的配网超短期功率态势预测方法 - Google Patents
一种基于改进BiGRU的配网超短期功率态势预测方法 Download PDFInfo
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113988395A (zh) * | 2021-10-21 | 2022-01-28 | 中国电建集团华东勘测设计研究院有限公司 | 基于SSD和双重注意力机制BiGRU的风电超短期功率预测方法 |
CN115293244A (zh) * | 2022-07-15 | 2022-11-04 | 北京航空航天大学 | 一种基于信号处理及数据约简的智能电网虚假数据注入攻击检测方法 |
CN115299962A (zh) * | 2022-08-12 | 2022-11-08 | 山东大学 | 一种基于双向门控循环单元和注意力机制的麻醉深度监测方法 |
CN116388865A (zh) * | 2023-06-05 | 2023-07-04 | 深圳市飞思卓科技有限公司 | 一种基于pon光模块光功率异常的自动筛选方法 |
CN113988395B (zh) * | 2021-10-21 | 2024-10-18 | 中国电建集团华东勘测设计研究院有限公司 | 基于SSD和双重注意力机制BiGRU的风电超短期功率预测方法 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110048438A (zh) * | 2019-05-09 | 2019-07-23 | 武汉大学 | 一种基于模型预测控制的配电网馈线级负荷功率控制方法 |
CN111461173A (zh) * | 2020-03-06 | 2020-07-28 | 华南理工大学 | 一种基于注意力机制的多说话人聚类系统及方法 |
CN113067344A (zh) * | 2021-03-08 | 2021-07-02 | 南京理工大学 | 一种基于模型预测控制的主动配电网无功优化方法 |
CN113964825A (zh) * | 2021-10-21 | 2022-01-21 | 中国电建集团华东勘测设计研究院有限公司 | 基于二次分解和BiGRU的超短期风电功率预测方法 |
CN117856204A (zh) * | 2023-11-23 | 2024-04-09 | 国网江苏省电力有限公司南京供电分公司 | 配电网超短期负荷功率区间的预测方法、系统及存储介质 |
-
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- 2021-07-05 CN CN202110756805.9A patent/CN113496102B/zh active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110048438A (zh) * | 2019-05-09 | 2019-07-23 | 武汉大学 | 一种基于模型预测控制的配电网馈线级负荷功率控制方法 |
CN111461173A (zh) * | 2020-03-06 | 2020-07-28 | 华南理工大学 | 一种基于注意力机制的多说话人聚类系统及方法 |
CN113067344A (zh) * | 2021-03-08 | 2021-07-02 | 南京理工大学 | 一种基于模型预测控制的主动配电网无功优化方法 |
CN113964825A (zh) * | 2021-10-21 | 2022-01-21 | 中国电建集团华东勘测设计研究院有限公司 | 基于二次分解和BiGRU的超短期风电功率预测方法 |
CN117856204A (zh) * | 2023-11-23 | 2024-04-09 | 国网江苏省电力有限公司南京供电分公司 | 配电网超短期负荷功率区间的预测方法、系统及存储介质 |
Non-Patent Citations (2)
Title |
---|
张娜: "间歇性能源输出功率预测与储能系统规划", 中国博士学位论文全文数据库工程科技II辑, no. 5, 15 May 2015 (2015-05-15) * |
赵振兵: "基于改进循环神经网络的配电网超短期功率预测方法", 电力科学与技术学报, vol. 37, no. 5, 24 August 2022 (2022-08-24) * |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113988395A (zh) * | 2021-10-21 | 2022-01-28 | 中国电建集团华东勘测设计研究院有限公司 | 基于SSD和双重注意力机制BiGRU的风电超短期功率预测方法 |
CN113988395B (zh) * | 2021-10-21 | 2024-10-18 | 中国电建集团华东勘测设计研究院有限公司 | 基于SSD和双重注意力机制BiGRU的风电超短期功率预测方法 |
CN115293244A (zh) * | 2022-07-15 | 2022-11-04 | 北京航空航天大学 | 一种基于信号处理及数据约简的智能电网虚假数据注入攻击检测方法 |
CN115293244B (zh) * | 2022-07-15 | 2023-08-15 | 北京航空航天大学 | 一种基于信号处理及数据约简的智能电网虚假数据注入攻击检测方法 |
CN115299962A (zh) * | 2022-08-12 | 2022-11-08 | 山东大学 | 一种基于双向门控循环单元和注意力机制的麻醉深度监测方法 |
CN116388865A (zh) * | 2023-06-05 | 2023-07-04 | 深圳市飞思卓科技有限公司 | 一种基于pon光模块光功率异常的自动筛选方法 |
CN116388865B (zh) * | 2023-06-05 | 2023-09-15 | 深圳市飞思卓科技有限公司 | 一种基于pon光模块光功率异常的自动筛选方法 |
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