基本信息:
- 专利标题： 一种基于深度学习的变压器复杂工况识别方法
- 专利标题（英）：Transformer complex working condition identification method based on deep learning
- 申请号：CN201810763241.X 申请日：2018-07-06
- 公开(公告)号：CN108896857A 公开(公告)日：2018-11-27
- 发明人： 张利强 , 刘刚 , 焦邵华 , 白淑华 , 葛亮 , 张天侠 , 王立敏 , 许翠娟 , 杨常府 , 谢晓冬 , 赵纪元 , 詹庆才 , 徐延明
- 申请人： 北京四方继保自动化股份有限公司
- 申请人地址： 北京市海淀区上地信息产业基地四街9号
- 专利权人： 北京四方继保自动化股份有限公司
- 当前专利权人： 北京四方继保自动化股份有限公司
- 当前专利权人地址： 北京市海淀区上地信息产业基地四街9号
- 代理机构： 北京智绘未来专利代理事务所
- 代理人： 张红莲
- 主分类号： G01R31/02
- IPC分类号： G01R31/02 ; G06N3/04 ; G06N3/08
The invention discloses a transformer complex working condition identification method based on deep learning. The method comprises the following steps that 1, original sample data is acquired; 2, theoriginal sample data is built into a data set with type identification, a data set without type identification and a data set of test data; 3, according to a certain time window, window obtaining andgrouping are conducted on the built data sets; 4, voltage and current sequence signals in the window are processed to obtain frequency spectrum data; 5, the frequency spectrum data is subjected to recurrent neural network training; 6, a trained recurrent neural network is tested and optimized; 7, field data is input to the optimized recurrent neural network for conducting identification and precise positioning of transformer complex working conditions. According to the method, by adopting the recurrent network, complex mixed faults can be accurately judged and precisely positioned, and the robustness and a practical level of transformer complex working condition identification are improved.
公开/授权文献:
- CN108896857B 一种基于深度学习的变压器复杂工况识别方法 公开/授权日：2020-12-01
IPC结构图谱:
| G | 物理 |
| --G01 | 测量；测试 |
| ----G01R | 测量电变量；测量磁变量（通过转换成电变量对任何种类的物理变量进行测量参见G01类名下的 |
| ------G01R31/00 | 电性能的测试装置；电故障的探测装置；以所进行的测试在其他位置未提供为特征的电测试装置 |
| --------G01R31/02 | .对电设备、线路或元件进行短路、断路、泄漏或不正确连接的测试 |