李超顺，男，1983年11月生，博士，华中科技大学教授、博导、水利水电科学研究院常务副院长。兼任长江技术经济学会智慧水电与装备专委会常务副主任委员、中国水力发电工程学院自动化专委会委员、中国振动工程学会转子动力学专委会委员和多家国内外期刊编委。
2005年本科毕业于武汉大学热能与动力工程专业（水动方向），2010年博士毕业于华中科技大学水利水电工程专业并留校任教，2012年破格聘副教授，2016年破格聘教授，2019年入选第四批国家级人才计划。
主持国家自然科学基金6项（含1项重点项目）、省部级项目5项（2项重点项目）和其他各类项目30余项（千万级项目1项）。撰写专著3部，发表论文SCI期刊论文122篇，其中一作/通讯作者论文90篇，Google引用6000余次，H指数44。成果获湖北省科技进步一等奖（排名1）、长江科学技术奖一等奖（排名1）、教育部自然科学奖一等奖（排名2）、水力发电科学技术奖一等奖（排名2）。自2021年起连续入选中国高被引学者（爱思唯尔）和全球2%顶尖科学家（斯坦福）等学术荣誉。
研究方向（不分先后）
（1）水电设备状态监测、故障诊断与状态检修；
（2）水电机组智能控制与网源协调控制；
（3）水力机械内部流动与多场耦合；
（4）水电站/泵站过渡过程；
（5）水电能源优化运行；
（6）水风光不确定性预报与联合优化运行；
（7）气流组织仿真与节能降耗。
主要科技奖励
1. 李超顺（1/15）特大型水电机组智能运维关键技术、成套装备及产业化，湖北省科学技术进步奖，一等奖，湖北省政府，2022
2. 李超顺（1/15）,长江中上游大型水电站机组智能控制与优化运行关键技术,长江科学技术奖，一等奖，长江技术经济学会（一级学会），2022
3. 李超顺(2/5)，大型水电机组动力学建模、故障诊断与优化控制，高等学校科学研究优秀成果奖（自然科学），一等奖，教育部，2017
4. 李超顺(2/15)，大型水电机组故障诊断与优化控制关键技术及应用，水力发电科学技术奖，一等奖，中国水力发电工程学会（一级学会），2015
主持的主要科研项目
【国家级项目】
1. 国家自然科学基金联合重点项目,基于云边端协同的水电厂机组智能状态监测及故障辨识关键技术,2023
2. 国家自然科学基金面上项目，变速抽蓄-风-光联合外送系统稳定性机理与多尺度协调控制研究，2022
3. 国家自然科学基金面上项目，融合深度学习的水电机组故障知识图谱构建与不确定推理诊断，2018
4. 国家自然科学基金面上项目，抽蓄储能风光互补智能微网多尺度控制研究，2016
5. 国家自然科学基金面上项目，抽水蓄能机组的集成故障诊断非线性预测控制研究，2014
6. 国家自然科学基金青年项目，基于模糊辨识与多模型描述的抽水蓄能机组控制系统故障诊断方法研究，2011
【省部级项目】
7. 湖北省自然科学基金联合基金重点项目，水风光储联合运行系统水电机组振荡机理与控制方法研究，2023
8. 湖北省重点研发计划项目，大型水电机组安全运行智能监测与健康管理系统研究，2021年,技术负责人。
9. 湖北省杰出青年基金，基于深度学习的大型水电机组故障诊断与性能预测，2019
10. 武汉市应用基础前沿专项，融合深度学习的智能电网设备故障知识图谱构建与推理诊断，2018
11. 教育部博士点基金新教师项目，基于模糊多模型的水电机组控制系统复杂特征辨识与故障诊断研究，2011
【横向项目】
12. 中国长江电力股份有限公司项目，宽负荷运行混流式水轮机转轮损伤机理、试验方法与监测技术研究，2023/11-2025/10
13. 国家电网公司总部科技项目，抽蓄电站机组轴系数字孪生及安全预警关键技术研究，2023/07-2025/12,课题负责
14. 国家电网公司总部科技项目，域适应推理技术及其在抽水蓄能机组预测性维护中的应用，2021/07-2023/12,课题负责
15. 国网新源公司项目，基于大数据挖掘的规模化抽水蓄能机组健康量化评价研究与应用，2023.5-2024.12
16. 中国电建集团中南勘测设计研究院项目，水、光、储联合优化调度策略研究，2023.6-2023.12
17. 中国长江三峡集团有限公司项目,基于状态检修目标的水电厂检修策略研究，2021
18. 广东能源集团有限公司项目,水电厂设备状态监测平台与监测支持系统研究，2021
19. 国家电网公司科技项目，水电机组多场耦合振动状态感知、故障诊断及劣化趋势预警技术研究与应用，2020
20. 湖南五凌电力科技有限公司，五强溪水电厂厂房温湿度环境优化研究,2020
21. 中国长江电力股份有限公司，白鹤滩电站调速器状态在线监测及故障诊断系统研发，2020
22. 华东勘测设计研究院，抽水蓄能电站设备运行趋势分析、健康评价与故障诊断模型研究与系统研发，2019
23. 乌江渡发电厂, 乌江渡发电厂水电站地下厂房智能化通风系统研究及应用,2019
24. 湖南电科院，水轮机组调节系统预测控制模型及优化控制策略研究，2019
25. 国网新源公司，江西洪屏抽水蓄能电站调速系统仿真及性能测试与诊断关键技术研究，2015，技术负责人
26. 国网新源公司，江西洪屏抽水蓄能电站非电气量控制逻辑典型设计研究,2015，已结题，技术负责人
27. 雅砻江水电公司，二滩水电站设备状态监测及故障诊断研究，2011，技术负责人
28. 湖北省电力公司，大规模吸纳外区电力情况下湖北电网调峰能力与调峰措施研究，2013
29. 湖北省电力公司，湖北电网水火电电源调峰响应能力分析研究，2013
专著
[1] 李超顺，周建中，许颜贺. 抽水蓄能机组控制优化问题及其启发式优化方法. 科学出版社, 2019.
[2] 周建中，李超顺，水电机组系统建模、参数辨识及调速和励磁控制方法，华中科技大学出版社，2016.
[3] 周建中，张勇传，李超顺. 水轮发电机组动力学问题与故障诊断原理及方法，华中科技大学出版社，2013.
主要论文
[1] Xiaoqiang Tan, Chaoshun Li*, Dong Liu, He Wang, Rongli Xu, Xueding Lu, Zhiwei Zhu. Multi-time scale model reduction strategy of variable-speed pumped storage unit grid-connected system for small-signal oscillation stability analysis. Renewable Energy, 2023，211: 985-1009.
[2] Xueding Lu, Chaoshun Li*, Dong Liu, Zhiwei Zhu, Xiaoqiang Tan, Rongli Xu. Comprehensive stability analysis of complex hydropower system under flexible operating conditions based on a fast stability domain solving method. Energy, 2023,274:127368.
[3] Xiaolong Cui#, Yifan Wu#, Xiaoyuan Zhang, Jie Huang, Pak Kin Wong, Chaoshun Li*. A Novel Fault Diagnosis Method for Rotor-Bearing System Based on Instantaneous Orbit Fusion Feature Image and Deep Convolutional Neural Network. IEEE/ASME Transactions on Mechatronics, 2023, 28(2): 1013 - 1024.
[4] Qiannan Zhu, Feng Jiang*, Chaoshun Li*. Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization. Energy, 271, 15: 127006.
[5] Yuying Xie, Chaoshun Li*, Mengying Li, Fangjie Liu, Meruyert Taukenova. An Overview of deterministic and probabilistic forecasting methods of wind energy. iScience, 2023, 26(1): 105804.
[6] Jie Huang, Chaoshun Li*, Xiangqu Xiao, Tian Yu, Xiaohui Yuan, Yongchuan Zhang. Adaptive Multivariate Chirp Mode Decomposition. Mechanical Systems and Signal Processing, 2023, 186, 109897.
[7] Xiangqu Xiao, Chaoshun Li*, Jie Huang, Tian Yu. Fault Diagnosis of Rolling Bearing Based on Knowledge Graph with Data Accumulation Strategy. IEEE Sensors Journal, 2022, 22(19): 18831-18840.
[8] Xiaolong Cui#, Jie Huang#, Chaoshun Li*, Yujie Zhao. Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm. Mechanical Systems and Signal Processing, 2022, 178: 109211.
[9] Xiaoyuan Zhang*, Yajun Jiang, Xianbo Wang, Chaoshun Li*, Jinhao Zhang. Health Condition Assessment for Pumped Storage Units using Multi-Head Self-Attentive Mechanism and Improved Radar Chart. IEEE Transactions on Industrial Informatics, 2022, 18 (11): 8087-8097.
[10] Hao Chen, Chaoshun Li*, Wenxian Yang, Jie Liu, Xueli An, Yujie Zhao. Deep balanced cascade forest: An novel fault diagnosis method for data imbalance. ISA Transactions, 2022, 126: 428-439.
[11] Xiaoyuan Zhang*, Yajun Jiang, Chaoshun Li*, Jinhao Zhang. Health status assessment and prediction for pumped storage units using a novel health degradation index. Mechanical Systems and Signal Processing, 2022, 171, 108910.
[12] Xueding Lu, Chaoshun Li*, Dong liu*, Zhiwei Zhu, Xiaoqiang Tan. Influence of water diversion system topologies and operation scenarios on the damping characteristics of hydropower units under ultra-low frequency oscillations. Energy, 2022, 239, 122679.
[13] Dong Liu, Chaoshun Li*, O.P. Malik. Operational characteristics and parameter sensitivity analysis of hydropower unit damping under ultra-low frequency oscillations. International Journal of Electrical Power & Energy Systems, 2022, 136:107689.
[14] Adnan Saeed, Chaoshun Li*, Zhenhao Gan, Yuying Xie, Jiefang Liu. A Simple approach for Short-term Wind Speed Interval Prediction based on Independently Recurrent Neural Networks and Error Probability Distribution. Energy, 2022, 238: 122012.
[15] Chen Feng, Yuan Zheng*, Chaoshun Li*, Zijun Mai, Wei Wu, Huixiang Chen. Cost advantage of adjustable-speed pumped storage unit for daily operation in distributed hybrid system. Renewable Energy, 2021, 176: 1-10.
[16] Dong Liu, Chaoshun Li*, O.P. Malik. Nonlinear modeling and multi-scale damping characteristics of hydro-turbine regulation systems under complex variable hydraulic and electrical network structures. Applied Energy, 2021, 293: 116949.
[17] Dong Liu, Chaoshun Li*, Xiaoqiang Tan, Xueding Lu, O.P. Malik. Damping characteristics analysis of hydropower units under full operating conditions and control parameters: accurate quantitative evaluation based on refined models. Applied Energy, 2021, 292:116881.
[18] Bo Fu, Wangjun Yuan, Xiaolong Cui, Tian Yu, XiLin Zhao, Chaoshun Li*. Correlation Analysis and Augmentation of Samples for a Bidirectional Gate Recurrent Unit for the Remaining Useful Life Prediction of Bearings, IEEE Sensors Journal, 2021, 21 （6）：7989-8001.
[19] Xiaosheng Peng, Hongyu Wang, Jianxun Lang, Wenze Li, Qiyou Xu, Zuowei Zhang, Tao Cai, Shanxu Duan1, Fangjie Liu, Chaoshun Li*. EALSTM-QR: a new interval wind power forecasting model using numerical weather prediction and deep learning technique. Energy, 2021, 220, 119692.
[20] Yuying Xie, Chaoshun Li*, Geng Tang, Zhenhao Gan. A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting. Energy, 2021, 216, 119179.
[21] Fangjie Liu, Chaoshun Li*, Yanhe Xu, Geng Tang, Yuying Xie. A new LUBE model using gradient descend training method for wind speed interval prediction. Wind Energy, 2021, 24(3): 290-304.
[22] Zhenhao Gan, Chaoshun Li*, Jianzhong Zhou, Geng Tang. Temporal convolutional networks interval prediction model for wind speed forecasting. Electric Power Systems Research, 2021, 191: 106865.
[23] Yizhuo Ma, ChaoshunLi*, JianzhongZhou, YongchuanZhang. Comprehensive stochastic optimal scheduling in residential micro energy grid considering pumped-storage unit and demand response. Journal of Energy Storage, 2020, 32: 101968.
[24] Ruoheng Wang, Chaoshun Li*, Wenlong Fu*, Geng Tang. A Deep Learning Method based on GRU and VMD for Short-term Wind Power Interval Prediction. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31 (10): 3814 - 3827.
[25] Xiaolong Cui, Chaoshun Li*, Bailin Li, Yi Li. Instantaneous Feature Extraction and Time-Frequency Representation of Rotor Purified Orbit Based on Vold-Kalman Filter. IEEE Transactions on Instrumentation & Measurement, 2020, 69(10): 7386-7397.
[26] Geng Tang, Yifan Wu, Chaoshun Li*, Pak Kin Wong, Zhihuai Xiao, Xueli An. A Novel Wind Speed Interval Prediction based on Error Prediction Method. IEEE Transactions on Industrial Informatics, 2020,16 (11): 6806-6815.
[27] Chaoshun Li*, Geng Tang, Xiaoming Xue, Adnan Saeed, Xin Hu. Short-term Wind Speed Interval Prediction based on Ensemble GRU model. IEEE Transactions on Sustainable Energy, 2020,11 (3):1370-1380. (ESI高引)
[28] Chunyang Wei, Chaoshun Li*, Chen Feng, Jianzhong Zhou, Yongchuan Zhang. A T-S fuzzy model identification approach based on evolving MIT2-FCRM and WOS-ELM algorithm. Engineering Applications of Artificial Intelligence, 2020, 92, 103653.
[29] Chaoshun Li*, Geng Tang, Xiaoming Xue*, Xinbiao Chen, Ruoheng Wang, Chu Zhang. The short-term interval prediction of wind power using the deep learning model with gradient descend optimization. Renewable Energy, 2020, 155: 197-211.
[30] Chen Feng, Chaoshun Li*, Li Chang, Tan Ding, Zijun Mai. Advantage analysis of variable-speed pumped storage units in renewable energy power grid: Mechanism of avoiding S-shaped region. International Journal of Electrical Power and Energy Systems, 2020, 120,105976.
[31] Xinjie Lai, Chaoshun Li*, Jianzhong Zhou, Yongchuan Zhang, Yonggang Li. A multi-objective optimization strategy for the optimal control scheme of pumped hydropower systems under successive load rejections. Applied Energy, 2020, 261,114474.
[32] Yujie Zhao, Chaoshun Li*, Wenlong Fu, Jie Liu, Tian Yu and Hao Chen. A modified variational mode decomposition method based on envelope nesting and multi-criteria evaluation. Journal of Sound and Vibration, 2020, 468:115099.
[33] Chaoshun Li*, Wenxiao Wang, Jinwen Wang, Deshu Chen. Network-constrained unit commitment with RE uncertainty and PHES by using a binary artificial sheep algorithm. Energy, 2019, 189 : 116203.
[34] Xinjie Lai, Chaoshun Li*, Jianzhong Zhou. A Multi-objective Artificial Sheep Algorithm. Neural Comput & Applic, 2019, 31(8): 4049–4083.（ESI高引）
[35] Xinjie Lai, Chaoshun Li*, Wencheng Guo*, Yanhe Xu, Yonggang Li. Stability and dynamic characteristics of the nonlinear coupling system of hydropower station and power grid. Communications in Nonlinear Science and Numerical Simulation, 2019, 79: 104919.
[36] Jinjiao Hou, Chaoshun Li*, Wencheng Guo*, et al. Optimal successive start-up strategy of two hydraulic coupling pumped storage units based on multi-objective control. International Journal of Electrical Power and Energy Systems, 2019, 111:398-410.
[37] Wen Zou, Chaoshun Li*, Pengfei Chen. An Inter Type-2 FCR Algorithm Based T-S Fuzzy Model for Short-term Wind Power Interval Prediction. IEEE Transactions on Industrial Informatics, 2019, 15(9): 4934 – 4943.
[38] Wenlong Fu*, Kai Wang, Chaoshun Li*, Jiawen Tan. Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Conversion and Management, 2019, 187:356-377. (一区) （ESI高引）
[39] Bo Fu, Chenxi Ouyang, Chaoshun Li*, Jinwen Wang, Eid Gul. An improved mixed integer linear programming approach based on symmetry diminishing for unit commitment of hybrid power system. Energies, 2019, 12(5), 833; doi: 10.3390/en12050833.（ESI高引）
[40] Chu Zhang, Tian Peng*, Chaoshun Li*, Wenlong Fu, Xin Xia, Xiaoming Xue. Multi-objective optimization of a fractional order PID controller for pumped turbine governing system using an improved NSGA-III algorithm under multi-working conditions. Complexity, 2019, 5826873.（ESI高引）
[41] Xinjie Lai, Chaoshun Li*, Jianzhong Zhou, Nan Zhang. Multi-objective optimization of the closure law of guide vanes for pumped storage units. Renewable Energy, 2019, 139:302-312.
[42] Chaoshun Li*, Wenxiao Wang, Deshu Chen. Multi-objective complementary scheduling of Hydro-Thermal-RE power system via a multi-objective hybrid grey wolf optimizer. Energy, 2019, 171: 241-255.（ESI高引）
[43] Wenlong Fu*, Kai Wang, Chaoshun Li*, Xiong Li, Yuehua Li, Hao Zhong. Vibration Trend measurement for Hydropower Generator Based on Optimal Variational Mode Decomposition and LSSVM Improved with Chaotic Sine Cosine Algorithm Optimization. Measurement Science and Technology, 2019, 30(1): 015012.（ESI高引）
[44] Yanhe Xu*, Yang Zheng*, Yi Du, Wen Yang, Xuyi Peng, Chaoshun Li*. Adaptive condition predictive-fuzzy PID optimal control of start-up process for pumped storage unit at low head area, Energy Conversion and Management, 2018, 177: 592-604.
[45] Chaoshun Li*, Zhengguang Xiao, Xin Xia*, Wen Zou, Chu Zhang. A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting. Applied Energy, 2018,215:131–144. （ESI高引）
[46] Zanbin Wang, Chaoshun Li*, Xinjie Lai, Nan Zhang, Yanhe Xu*, Jinjiao Hou. An integrated start-up method for pumped storage units based on a novel artificial sheep algorithm. Energies, 2018, 11(1), 151; doi:10.3390/en11010151. （ESI高引）
[47] Chaoshun Li*, Wen Zou, Nan Zhang, et al. An evolving T-S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2018, 69: 93-103.
[48] Wen Zou, Chaoshun Li*, Nan Zhang. A T-S Fuzzy Model Identification Approach based on a Modified Inter Type-2 FRCM Algorithm. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1104 – 1113.
[49] Chaoshun Li*, Zhou J, Chang L, et al. T-S fuzzy model identification based on a novel hyper-plane-shaped membership function. IEEE Transactions on Fuzzy Systems, 2017, 25 (5), 1364-1370.
[50] Chaoshun Li*, Yifeng Mao, Jiandong Yang, et al. A nonlinear generalized predictive control for pumped storage unit. Renewable Energy, 2017, 114:945-959.
[51] Chaoshun Li*, Nan Zhang, Xinjie Lai, Jianzhong Zhou, Yanhe Xu. Design of a fractional order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation. Information Sciences, 2017, 396: 162–181. (ESI高引)
[52] Wenxiao Wang, Chaoshun Li*, Xiang Liao, Hui Qin. Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm. Applied Energy, 2017, 187: 612–626.
[53] Chaoshun Li*, Yifeng Mao, Jianzhong Zhou, Nan Zhang, Xueli An. Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting. Applied Soft Computing, 2017, 52: 290-305.
[54] Meng Luo, Chaoshun Li*, Xiaoyuan Zhang, Ruhai Li, Xueli An. Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. ISA Transactions, 2016, 65: 556-566.
[55] Nan Zhang, Chaoshun Li*, Ruhai Li, Xinjie Lai, Yuanchuan Zhang. A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system. Knowledge-Based Systems, 2016, 109: 218-237.
[56] Chaoshun Li*, Li Chang, Zhengjun Huang, et al. Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 50: 177-191.
[57] Chaoshun Li*, Hongshun Li, Pangao Kou, Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system, Neurocomputing, 2014, 124: 139-148.
[58] Chaoshun Li*, Jianzhong Zhou. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis. ISA Transactions, 2014, 53(5): 1534-1543.
[59] Chaoshun Li*, Jianzhong Zhou, Jian Xiao, Han Xiao, Hydraulic turbine governing system identification using T–S fuzzy model optimized by chaotic gravitational search algorithm, Engineering Applications of Artificial Intelligence, 2013, 26 (9), 2073-2082.
[60] Chaoshun Li*, Jianzhong Zhou, Bo Fu, Pangao Kou, Jian Xiao, T-S fuzzy model identification with gravitational search based hyper-plane clustering algorithm, IEEE Transactions on Fuzzy Systems, 2012, 20 (2): 305-317.
[61] Chaoshun Li*, Jianzhong Zhou, Jian Xiao, Han Xiao, Parameters identification of chaotic system by chaotic gravitational search algorithm, Chaos, Solitons & Fractals 45 (4), 2012, 539-547.
[62] Chaoshun Li*, Jianzhong Zhou, Pangao Kou, Jian Xiao, A novel chaotic particle swarm optimization based fuzzy clustering algorithm, Neurocomputing, 2012, 83: 98-109.
[63] Chaoshun Li, Jianzhong Zhou*, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 2011, 52 (1), 374-381. (ESI高引)
[64] Chaoshun Li, Jianzhong Zhou*, Qingqing Li, et.al. A new T-S fuzzy-modeling approach to identify a boiler–turbine system. Expert Systems with Applications, 2010, 37(3): 2214-2221.
[65] Chaoshun Li, Jianzhong Zhou*, Xiuqiao Xiang, Qingqing Li, Xueli An. T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm. Engineering Applications of Artificial Intelligence, 2009, 22(4-5): 646-653.
发明专利
[1] 李超顺，赖昕杰.一种抽水蓄能机组相继甩负荷关机规律优化方法与系统,中国，2019101761784 （申请日：2019-03-08；授权日：2021-09-03）
[2] 李超顺,邹雯，刘颉，许颜贺，一种水电机组劣化趋势区间预测方法与系统，中国，201910220746.6 （申请日：2019-03-22；授权日：2020-12-29）
[3] 李超顺，侯进皎，抽水蓄能机组双机相继开机规律的多目标优选方法及系统，中国，201910204400.7（申请日：2019-03-18；授权日：2021-01-05）
[4] 李超顺，陈昊，赖昕杰，胡鑫，侯进皎，陈德树，基于融合特征的知识图谱的水电机组故障诊断方法和系统，201910173342.6（申请日：2019-03-07；授权日：2021-02-12）
[5] 李超顺，王若恒，涂文奇，陈昊，陈新彪，一种基于LSTM深度学习模型的水电机组故障诊断方法与系统，201711463863.2（申请日：2017-12-28；授权日：2020-06-05）
[6] 李超顺，陈新彪，邹雯，赖昕杰，陈昊，一种基于人工神经网络的风速区间预测方法与系统，201711463820.4（申请日：2017-12-28；授权日：2020-08-18）
[7] 李超顺，汪赞斌，甘振豪，侯进皎，王若恒，一种基于分阶段整体优化的短期风速预测方法与系统，201711459407.0 （申请日：2017-12-28；授权日：2021-02-09）
[8] 李超顺，侯进皎，汪赞斌，赖昕杰，张楠，一种抽水蓄能机组开机规律的双目标优选方法及系统，201711451826X（申请日：2017-12-28；授权日：2019-12-20）
[9] 李超顺,汪赞斌,赖昕杰,张楠,邹雯.一种抽水蓄能机组水轮机工况智能开机方法,中国，201610860982.0.（申请日：2016-09-28；授权日：2017-09-12）
[10] 李超顺, 汪赞斌, 杨兴昭, 王若恒, 涂文奇. 一种高层建筑抽蓄储能风光智能微网系统及控制方法, 中国, 201610860955.3. （申请日：2016-09-28；授权日：2018-07-06）
[11] 李超顺，王文潇，汪赞斌，李如海,黄润东,唐清波. 一种混合新能源电力系统机组组合优化方法，中国，201510885235.8.（申请日：2015-12-04;授权日: 2016-06-29）
[12] 李超顺，杨兴昭，李如海，一种水轮发电机组励磁系统PID控制参数的优选方法，中国，201510760890.0 （申请日：2015-11-10；授权日：2016-08-31）
[13] 李超顺,赵志高,汪赞斌,李如海，杨兴昭.一种水轮机调节系统控制参数的优选方法，201510760877.5.（申请日：2015-11-10；授权日：2018-02-23）
[14] 李超顺，张楠，王文潇，一种水轮发电机组励磁系统参数辨识方法，中国，201510760841.7.（申请日: 2015-11-10；授权日: 2018-11-30）
[15] 李超顺，董伟，毛翼丰，张楠, 罗萌, 王文潇. 一种水轮机调节系统的参数辨识方法，中国，201510759863.1。（申请日：2015-11-10;授权日: 2016-06-29）
[16] 李超顺，汪赞斌，董伟，王文潇，魏巍，毛翼丰.一种新能源混合系统控制参数的优选方法，中国，201510160557.X.（申请日：2015-11-10；授权日：2016-09-28）
[17] 李超顺，周建中，张楠，李如海，毛翼丰，罗萌，一种水轮机调速系统控制参数的自动整定方法，中国，201410811275.3。（申请日:2014-12-23;授权日:2017-03-08）
[18] 方仍存,李超顺，李如海，杜治. 基于引力搜索的水火电系统多目标调峰方法，中国，CN201410198218.2。（已授权）
[19] 周建中，李超顺，许颜贺，一种中小型水力发电机组的机群等值建模方法，2014. 12-2015.10，中国，201510046939.6。（已授权）
[20] 周建中，李超顺，寇攀高，卢有麟，水轮机调速系统仿真测试装置，2011.2-2013 .4，中国，201110044500.1。（已授权）
[21] 周建中，黄志伟，李超顺，罗志猛，水力发电机组效率监测装置、系统及方法，2011.2-2012.12，中国，201110044499.2。（已授权）
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