Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation
In traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stabili...
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| Format: | Article |
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State Grid Energy Research Institute
2020-01-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201912113 |
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| _version_ | 1850053184683245568 |
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| author | Yanzhen ZHOU Xianyu ZHA Jian LAN Qinglai GUO Hongbin SUN Feng XUE Shengming WANG |
| author_facet | Yanzhen ZHOU Xianyu ZHA Jian LAN Qinglai GUO Hongbin SUN Feng XUE Shengming WANG |
| author_sort | Yanzhen ZHOU |
| collection | DOAJ |
| description | In traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stability prediction based on data augmentation and deep residual network (ResNet). Firstly, the original training data is extended with consideration of the noise data and data-missing conditions. Then, the real-time data of the disturbed generator is used as input features. Considering the similarity between high-dimensional time series data and images, the deep residual network, which is an improved algorithm based on convolutional neural networks, is used to build transient stability assessment model. The case studies show that the proposed method can improve the generalization ability of the model, and has better robustness in dealing with noise data or data missing problems. |
| format | Article |
| id | doaj-art-1b72d165913c4b1ebbb3a23f3afaeac1 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-1b72d165913c4b1ebbb3a23f3afaeac12025-08-20T02:52:37ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492020-01-01531223110.11930/j.issn.1004-9649.201912113zgdl-53-1-zhouyanzhenTransient Stability Prediction of Power Systems Based on Deep Residual Network and Data AugmentationYanzhen ZHOU0Xianyu ZHA1Jian LAN2Qinglai GUO3Hongbin SUN4Feng XUE5Shengming WANG6Department of Electrical Engineering, Tsinghua University, Beijing 100084, ChinaNARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100084, ChinaNARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, ChinaNARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, ChinaIn traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stability prediction based on data augmentation and deep residual network (ResNet). Firstly, the original training data is extended with consideration of the noise data and data-missing conditions. Then, the real-time data of the disturbed generator is used as input features. Considering the similarity between high-dimensional time series data and images, the deep residual network, which is an improved algorithm based on convolutional neural networks, is used to build transient stability assessment model. The case studies show that the proposed method can improve the generalization ability of the model, and has better robustness in dealing with noise data or data missing problems.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201912113transient stabilitydeep learningdeep residual networkdata augmentationpower systemnoiseinformation missing |
| spellingShingle | Yanzhen ZHOU Xianyu ZHA Jian LAN Qinglai GUO Hongbin SUN Feng XUE Shengming WANG Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation Zhongguo dianli transient stability deep learning deep residual network data augmentation power system noise information missing |
| title | Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation |
| title_full | Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation |
| title_fullStr | Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation |
| title_full_unstemmed | Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation |
| title_short | Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation |
| title_sort | transient stability prediction of power systems based on deep residual network and data augmentation |
| topic | transient stability deep learning deep residual network data augmentation power system noise information missing |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201912113 |
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