邱高-k8凯发官网

特聘副研究员/硕士生导师，四川大学引进人才

每年招收能源动力/电气工程方向硕士研究生2-3名！

教育经历

部分成果

论文：

[1]  gao qiu, et al., “analytic deep learning-based surrogate model for operational planning with dynamic ttc constraints,” in ieee transactions on power systems, doi: 10.1109/tpwrs.2020.3041866.

[2]  gao qiu, et al., "hybrid deep learning for dynamic total transfer capability control," in ieee transactions on power systems, vol. 36, no. 3, pp. 2733-2736, may 2021.

[3]  gao qiu, et al., "surrogate-assisted optimal re-dispatch control for risk-aware regulation of dynamic total transfer capability," in iet generation, transmission & distribution, doi: 10.1049/gtd2.12147.

[4]  gao qiu, et al., "ensemble learning for power systems ttc prediction with wind farms," in ieee access, vol. 7, pp. 16572-16583, 2019.

[5]  邱高, 刘友波, 许立雄, 等. 基于深度确定性策略梯度的电网断面极限传输能力动态趋优控制[j/ol]. 中国电机工程学报.

[6]  邱高, 刘俊勇, 刘友波, 等. 风电外送通道极限传输能力的自适应向量机估计[j]. 电工技术学报, 2018, 33(014): 3342-3352.

[7]  刘季昂，邱高*，等. 基于高斯过程的电力系统复杂方式多维指标快速置信评价[j/ol]. 电力系统自动化.  

[8]  胥威汀, 刘俊勇, 唐权, 邱高*, 等. 含风电系统断面ttc运行规则的极限学习机提取方法[j]. 电力系统保护与控制, 2018, 46(23): 135-142.

[9]  杨波, 崔红芬, 邱高*, 等. 基于储能系统控制的同步交直流系统稳定性改善方法[j]. 可再生能源, 2020, 38(09): 1252-1257.

[10] gao qiu, et al., "deep learning based ttc predictor for power systems with wind energy integration," 2020 ieee pes innovative smart grid technologies europe (isgt-europe), the hague, netherlands, 2020, pp. 439-443.

[11] liangzhong yao, fubao wu, gao qiu*, et al., "de-risking transient stability of ac/dc power systems based on ess integration," in the journal of engineering, 2019, vol. 2019, no. 16, pp. 1221-1226. (ei)

[12]     jing ren, chen xue, shuanbao niu, xiaowei ma, xiaodong zhang, gao qiu*, et al., "a learning-assisted dynamic security enabled operational planning with transferable load," 2021 6th asia conference on power and electrical engineering (acpee), 2021, pp. 1639-1643.

[13]  youbo liu, junbo zhao, lixiong xu, tingjian liu, gao qiu, et al., "online ttc estimation using nonparametric analytics considering wind power integration," in ieee transactions on power systems, vol. 34, no. 1, pp. 494-505, jan. 2019.

[14]  xi zhang, youbo liu, jiajun duan, gao qiu, et al, "ddpg-based multi-agent framework for svc tuning in urban power grid with renewable energy resources," in ieee transactions on power systems, in press.

[15]  tingjian liu, ; ; ; ; , et al., "a bayesian learning based scheme for online dynamic security assessment and preventive control," in ieee transactions on power systems, vol. 35, no. 5, pp. 4088-4099, sept. 2020.

[16]  jiang liu, youbo liu, gao qiu, et al., "deep-q-network-based intelligent reschedule for power system operational planning," 2020 12th ieee pes asia-pacific power and energy engineering conference (appeec), nanjing, china, 2020, pp. 1-6.

[17]  honghao wu, junyong liu, youbo liu, gao qiu, et al., "power system transmission line fault diagnosis based on combined data analytics," 2017 ieee power & energy society general meeting, chicago, il, 2017, pp. 1-5.

[18]  苏童, 刘友波, 沈晓东, 刘挺坚, 邱高, 等. 深度学习驱动的电力系统暂态稳定预防控制进化算法[j].中国电机工程学报,2020,40(12):3813-3824.

[19]      ji’ang liu, youbo liu, gao qiu* and xiao shao. learning-aided optimal power flow based fast total transfer capability calculation[j]. energies, 2022, 15(4):1320.

[20]      lidong yi, maosheng ding, jili wang, gao qiu*, fei xue, ji’ang liu, yuxiong huang, gengfeng li and junyong liu. a scenario-classification hybrids-based banding method for power transfer limits of critical inter-corridors[j]. journal of modern power systems and clean energy, early access.

[21]      youbo liu, shuyu gao, gao qiu*, tingjian liu*, lijie ding and junyong liu. a physics-informed action network for transient stability preventive control[j]. ieee transactions on power systems, 2023, 38(2): 1771-1774.

[22]      youbo liu, su tong, gao qiu*, hongjun gao, junyong liu, yue shui. analytic deep learning and stepwise integrated gradients-based power system transient stability preventive control[j]. ieee transactions on power systems, early access.

[23]      jingxian yang, junyong liu, gao qiu*, jichun liu, et. al. a spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-pv-phs integrated power system[j]. energy, 2023, 278: 127915.

[24]       x. shen, h. liu, g. qiu*, et. al, "interpretable interval prediction-based outlier-adaptive day-ahead electricity price forecasting involving cross-market features," in ieee transactions on industrial informatics, doi: 10.1109/tii.2024.3355105

[25]      yongdong chen, youbo liu, junbo zhao, gao qiu, et al. physical-assisted multi-agent graph reinforcement learning enabled fast voltage regulation for pv-rich active distribution network[j]. applied energy, 2023, 351: 121743.

[26]      zhenyu huang, youbo liu, kecun li, jichun liu, hongjun gao, gao qiu, et al. evaluating long-term profile of demand response under different market designs: a comparison of scarcity pricing and capacity auction[j]. energy, 2023: 128096.

[27]      荆渝,刘友波,邱高*,等.基于区间估计与深度强化学习的有源配电网多智能体电压滚动控制[j].电网技术,2023,47(05).

[28]      刘友波,王天翔,邱高*,魏巍,周波,刘挺坚,刘俊勇,梅生伟.嵌入输入凸神经网络的静态电压稳定控制替代建模方法及其解析算法[j].电力自动化设备,2023,43(02).

[29]       李康文,邱高*,刘挺坚，等.基于可迁移强化学习的断面输电极限计算方法[j/ol].电网技术:1-11[2023-09-13].

专利：

[1] 刘友波, 邱高, 等. 一种基于深度学习的极限传输容量的计算方法. 四川省: cn112001066a, 2020-07-30.

[2] 邱高, 等. 一种感知风险的深度学习驱动的极限传输容量调整方法. 已授权，四川省: 2020107395873, 2021-11-09.

科研项目

本人从事电力系统人工智能应用、电网运行调控与控制技术等方面研究，追求人工智能理论与电力系统知识无缝结合的学术理想，长期探索物理和知识引导的电力人工智能k8凯发官网入口的解决方案，经常有独树一帜、别具一格的idea，提供一对一的科研过程辅助与论文写作修改指导，怕发不出文章的以及有基础想发顶刊的同学都请放心！

本人乐观积极，热爱运动生活，秉承劳逸结合理念，喜欢带同学搞集体活动，甚至可以帮忙解决单身问题（只要你不i且有需求）！

本人坚持“多出去走走才能不out”的理念，提倡并支持同学参与国内外学术会议（前提是你真要去学习交流，而不是单纯旅游）！

本人所处团队分工明确、气氛融洽，有工程师团队转化算法技术为应用工具，同学们可专注科研。此外，团队经费充足，能提供就业支持和深造规划！

最后，欢迎各位同学加入智能电网优化运行与电力市场运营研究团队！