| Spatio-temporal correlation analysis of online monitoring data for anomaly detection and location in distribution networks X Shi, R Qiu, Z Ling, F Yang, H Yang, X He IEEE Transactions on Smart Grid 11 (2), 995-1006, 2019 | 60 | 2019 |
| Detection and classification of transmission line transient faults based on graph convolutional neural network H Tong, RC Qiu, D Zhang, H Yang, Q Ding, X Shi CSEE Journal of Power and Energy Systems 7 (3), 456-471, 2021 | 45 | 2021 |
| Improving power system state estimation based on matrix-level cleaning H Yang, RC Qiu, L Chu, T Mi, X Shi, CM Liu IEEE Transactions on Power Systems 35 (5), 3529-3540, 2020 | 27 | 2020 |
| Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder H Yang, RC Qiu, X Shi, X He Electric Power Systems Research 182, 106253, 2020 | 25 | 2020 |
| Power transformer fault classifying model based on deep belief network [J] S Xin, Z Yongli, S Churila Power System Protection and Control 44 (1), 71-76, 2016 | 25 | 2016 |
| Transformer fault diagnosis based on deep auto-encoder network X Shi, Y Zhu, X Ning, L Wang, G Sun, G Chen Electric Power Automation Equipment 36 (5), 122-126, 2016 | 24 | 2016 |
| Adversarial feature learning of online monitoring data for operational risk assessment in distribution networks X Shi, R Qiu, T Mi, X He, Y Zhu IEEE Transactions on Power Systems 35 (2), 975-985, 2019 | 17 | 2019 |
| Early anomaly detection and localisation in distribution network: A data‐driven approach X Shi, R Qiu, X He, Z Ling, H Yang, L Chu IET Generation, Transmission & Distribution 14 (18), 3814-3825, 2020 | 16 | 2020 |
| 深度学习神经网络在电力变压器故障诊断中的应用 石鑫， 朱永利 电力建设, 116-122, 2015 | 15 | 2015 |
| Monitoring data factorization of high renewable energy penetrated grids for probabilistic static voltage stability assessment H Yang, X Shi, RC Qiu, X He, Q Ai, Z Wang IEEE Transactions on Smart Grid 13 (2), 1273-1286, 2021 | 13 | 2021 |
| 人工智能在电力系统中应用的近期研究热点介绍 朱永利， 石鑫， 王刘旺 发电技术 39 (3), 204-212, 2018 | 13 | 2018 |
| 基于深度信念网络的电力变压器故障分类建模 石鑫， 朱永利， 王刘旺， 孙岗 电力系统保护与控制 44 (1), 71-76, 2016 | 13 | 2016 |
| Application of deep learning neural network in fault diagnosis of power transformer X Shi, Y Zhu Electric Power Construction 36 (12), 116-122, 2015 | 12 | 2015 |
| Effect of cap-assisted esophagogastroduodenoscopy on examination of the major duodenal papilla: a noninferior, randomized controlled trial X Shi, H Luo, B Ning, X Wang, Q Tao, S Liang, R Zhang, J Chen, B Luo, ... Endoscopy 51 (05), 427-435, 2019 | 8 | 2019 |
| Anomaly detection and location in distribution networks: A data-driven approach X Shi, R Qiu, X He, L Chu, Z Ling, H Yang arXiv preprint 1801, 2018 | 7 | 2018 |
| Co-optimization of building energy systems with renewable generations combining active and passive energy-saving Z Wu, X Shi, F Fang, G Wen, Y Mi Applied Energy 351, 121514, 2023 | 6 | 2023 |
| Dimensionality increment of PMU data for anomaly detection in low observability power systems X Shi, R Qiu arXiv preprint arXiv:1910.08696, 2019 | 6 | 2019 |
| 深度学习在电网图像数据及时空数据中的应用综述 张宇航， 邱才明， 杨帆， 徐舒玮， 石鑫， 贺兴 电网技术 43 (6), 1865-1873, 2019 | 6 | 2019 |
| 基于输电线路监测的无线传感器网络路由优化技术研究 李天， 石鑫， 李永倩 电测与仪表 52 (21), 6-10, 2015 | 6 | 2015 |
| Application of deep learning neural network in fault diagnosis of power transformer [J] S Xin, Z Yongli Electric Power Construction 36 (12), 116-122, 2015 | 5 | 2015 |
Robert C. QiuProfessor of Electrical Engineering, Tennessee Technological UniversityVahvistettu sähköpostiosoite verkkotunnuksessa ieee.org
