汕头大学主页|汕头大学工学院|English Vision
  联系信息

邮件:zfan@stu.edu.cn

地址:广东省汕头市大学路243号汕头大学科学楼

邮编:515063

 
学术团队
张孝顺
硕士生导师
张孝顺,博士,副教授,硕士生导师, 邮箱:xiaoshunzhang@stu.edu.cn; xszhang1990@sina.cn 办公地点:行政中心院办220
 

研究方向:电力系统优化运行与控制;强化学习;迁移学习;博弈论

 

学习经历

2012.09至2017.06,华南理工大学 电力学院,电力系统及其自动化专业,工学博士,导师:余涛教授

2008.09至2012.06,华南理工大学 电力学院,电气工程及其自动化专业,工学学士

 

工作经历

2017.09至2018.09,香港理工大学 电机工程系,Research Associate,合作导师:许昭

 

张孝顺,博士,硕士生导师,于2017年6月获华南理工大学工学博士学位,2017.09至2018.09到香港理工大学电机工程系从事了一年的博士后研究工作。在本研究领域已发表专著一本,SCI/ EI期刊论文69篇,其中:① SCI期刊论文37篇(20篇Top, 2篇ESI高被引),第一作者或通讯作者22篇(12篇top);② EI期刊论文32篇,第一作者或通讯作者16篇。担任了国内外十余个SCI/EI期刊的审稿专家,并被多次授予“优秀审稿专家”荣誉称号。

 

论文著作:

     [1] 余涛,张孝顺,殷林飞,等. 智能发电控制. 北京:科学出版社,2019. (导师为第一作者)

[2] Xiaoshun Zhang, Tao Yu, Zhenning Pan, et al. Lifelong learning for complementary generation control of interconnected power grids with high-penetration renewables and EVs. IEEE Transactions on Power Systems, 2018, 33(4): 4097-4110. 

[3] Xiaoshun Zhang, Qing Li, Tao Yu, et al. Consensus transfer Q-learning for decentralized generation command dispatch based on virtual generation tribe. IEEE Transactions on Smart Grid, 2018, 9(3): 2152-2165. 

[4] Xiaoshun Zhang, Yixuan Chen, Tao Yu, et al. Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems. Applied Energy, 2017, 189: 157-176.

[5] Xiaoshun Zhang, Tao Yu, Bo Yang, et al. Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. Energy Conversion and Management, 2015, 106: 543-556.

[6] Xiaoshun Zhang, Tao Yu, Bo Yang, et al. Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid. Energy, 2016, 101: 34-51.

[7] Xiaoshun Zhang, Tao Bao, Tao Yu, et al. Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid. Energy, 2017, 133: 348-365.

[8] Xiaoshun Zhang, Tao Yu, Zhao Xu, et al. A cyber-physical-social system with parallel learning for distributed energy management of a microgrid. Energy, 2018, 165: 205-221.

[9] Xiaoshun Zhang, Shengnan Li, Tingyi He, et al. Memetic reinforcement learning based maximum power point tracking design of PV systems under partial shading condition. Energy, 2019, In Press.

[10] Xiaoshun Zhang, Tao Yu. Fast Stackelberg equilibrium learning for real-time coordinated energy control of a multi-area integrated energy system. Applied Thermal Engineering, 2019, 153: 225-241.

[11] Xiaoshun Zhang, Dezhi Wang, Tao Yu, et al. Ensemble learning for optimal active power control of distributed energy resources and thermostatically controlled loads in an islanded microgrid. International Journal of Hydrogen Energy, 2018, 43(49):22474-22486.

[12] Xiaoshun Zhang, Tao Yu, Bo Yang, et al. Accelerating bio-inspired optimizer with transfer reinforcement- learning for reactive power optimization. Knowledge-Based Systems, 2017, 116: 26-38.

[13] Xiaoshun Zhang, Hao Xu, Tao Yu, et al. Robust collaborative consensus algorithm for decentralized economic dispatch with a practical communication network. Electric Power Systems Research, 2016, 140: 597-610.

[14] Bo Yang, Tao Yu, Xiaoshun Zhang*, et al. Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition. Energy Conversion and Management, 2019, 179: 286-303.

[15] Bo Yang, Tao Yu, Xiaoshun Zhang*, et al. Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. Journal of Cleaner Production, 2019, 215: 1203-1222.

[16] Dezhi Wang, Xiaoshun Zhang*, Kaiping Qu, et al. Pareto tribe evolution with equilibrium-based decision for multi-objective optimization of multiple home energy management systems. Energy and Buildings, 2018, 159: 11-23.

[17] Tao Yu, Xiaoshun Zhang, Bin Zhou, et al. Hierarchical correlated Q-learning for multi-layer optimal generation command dispatch. International Journal of Electrical Power & Energy Systems, 2016, 78: 1-12. (导师为第一作者)

[18] Xiaoshun Zhang, Tao Yu, Lexin Guo, et al. Culture evolution learning for optimal carbon-energy combined-flow. IEEE Access, 2018, 6: 15521-15531.

[19] Xiaoshun Zhang, Tao Yu, Zhiyi Zhang, et al. Multi-agent bargaining learning for distributed energy hub economic dispatch. IEEE Access, 2018, 6: 39564-39573.

[20] Zhukui Tan, Xiaoshun Zhang*, Baiming Xie, et al. Fast learning optimizer for real-time optimal energy management of a grid-connected microgrid. IET Generation Transmission & Distribution, 2018, 12(12): 2977-2987.

[21] Xiaofeng Dong, Xiaoshun Zhang*, Tong Jiang. Adaptive consensus algorithm for distributed heat-electricity energy management for an islanded microgrid. Energies, 2018, 11(9), 2236.

[22] Min Tan, Chuanjia Han, Xiaoshun Zhang*, et al. Hierarchically correlated equilibrium Q-learning for multi-area decentralized collaborative reactive power optimization. CSEE Journal of Power and Energy Systems, 2016, 2(3): 65-72. 

[23] Jianlin Tang, Tao Yu, Xiaoshun Zhang*, et al. Multi-searcher optimization for the optimal energy dispatch of combined heat and power-thermal-wind-photovoltaic systems. Applied Sciences, 2019, 9(3), 537.

[24] Bo Yang, Xiaoshun Zhang, Tao Yu, et al. Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Conversion and Management, 2017, 133:427-443. (高被引)

[25] Kaiping, Tao Yu, Xiaoshun Zhang, et al. Homogenized adjacent points method for multi-objective optimal energy flow of integrated electricity and gas system. Applied Energy, 2019, 233-234: 338-351.

[26] Linfei Yin, Tao Yu, Xiaoshun Zhang, et al. Relaxed deep learning for real-time economic generation dispatch and control with unified time scale. Energy, 2018, 149: 11-23.

[27] Yixuan Chen, Tao Yu, Bo Yang, Xiaoshun Zhang, et al. Many-objective optimal power dispatch strategy incorporating temporal and spatial distribution control of multiple air pollutants. IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2896968.

[28] Lei Xi, Tao Yu, Bo Yang, Xiaoshun Zhang. A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid. Applied Energy, 2016, 178: 198-211.

[29] Bo Yang, Tao Yu, Hongchun Shu, Xiaoshun Zhang, et al. Democratic joint operations algorithm for optimal power extraction of PMSG based wind energy conversion system. Energy Conversion and Management, 2018, 159:312-326. (高被引)

[30] Lei Xi, Tao Yu, Bo Yang, Xiaoshun Zhang. A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids. Energy Conversion and Management, 2015, 103: 82-93.

[31] Kaiping Qu, Tao Yu, Linni Huang, Bo Yang, Xiaoshun Zhang. Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market. Energy, 2018, 149: 779-791.

[32] Lefeng Chen, Tao Yu, Xiaoshun Zhang, et al. Parallel Cyber-Physical-Social Systems Based Smart Energy Robotic Dispatcher and Knowledge Automation: Concepts, Architectures and Challenges. IEEE Intelligent Systems, 2018, DOI: 10.1109/MIS.2018.2882360.

[33] Linfei Yin, Tao Yu, Bo Yang, Xiaoshun Zhang. Adaptive deep dynamic programming for integrated frequency control of multi-area multi-microgrid systems. Neurocomputing, 2018, In Press.

[34] Chuanjia Han, Bo Yang, Tao Bao, Tao Yu, Xiaoshun Zhang. Bacteria foraging reinforcement learning for risk-based economic dispatch via knowledge transfer. Energies, 2017, 10(5), 638.

[35] Bo Yang, Tao Yu, Xiaoshun Zhang, et al. Interactive teaching–learning optimiser for parameter tuning of VSC-HVDC systems with offshore wind farm integration. IET Generation Transmission & Distribution, 2018, 12(3) :678-687.

[36] Linfei Yin, Tao Yu, Lv Zhou, Linni Huang, Xiaoshun Zhang, et al. Artificial emotional reinforcement learning for automatic generation control of large-scale interconnected power grids. IET Generation Transmission & Distribution, 2017, 11(9): 2305-2313.

[37] Linfei Yin, Lulin Zhao, Tao Yu, Xiaoshun Zhang. Deep forest reinforcement learning for preventive strategy considering automatic generation control in large-scale interconnected power systems. Applied Sciences, 2018, 8(11), 2185.

[38] Linni Huang, Bo Yang, Xiaoshun Zhang, et al. Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth-flame optimizer. Transactions of the Institute of Measurement and Control, 2017: 0142331217712091.

[39] 余涛,张孝顺*. 一种具有记忆自学习能力的快速动态寻优算法及其无功优化求解[J]. 中国科学:技术科学,2016, 46(3): 256-267.

[40] 张孝顺*,余涛. 互联电网AGC 功率动态分配的虚拟发电部落协同一致性算法[J]. 中国电机工程学报,2015,35(15): 3750-3759.

[41] 张孝顺*,李清,余涛,陈柏喜. 基于协同一致性迁移Q学习算法的虚拟发电部落AGC功率动态分配[J]. 中国电机工程学报,2017, 37(5): 1455-1466.

[42] 包涛, 张孝顺*,余涛,刘希喆,王德志. 反映实时供需互动的Stackelberg博弈模型及其强化学习求解[J]. 中国电机工程学报,2018, 38(10): 2947-2955.

[43] 张孝顺*,郑理民,余涛. 基于多步回溯Q(λ)学习的电网多目标最优碳流算法[J]. 电力系统自动化,2014,38(17): 118-123.

[44] 张孝顺*,余涛, 唐捷. 基于分层相关均衡强化学习的CPS指令优化分配算法[J]. 电力系统自动化,2015, 39(8): 80-86.

[45] 张孝顺*, 余涛, 唐捷. 基于CEQ(λ)多智能体协同学习的互联电网性能标准控制指令动态分配优化算法[J]. 电工技术学报, 2016, 31(8): 125-133.

[46] 张孝顺*,余涛. 互联电网自动发电控制功率分配的改进逼近于理想解的排序-Q多目标优化算法[J]. 控制理论与应用,2015, 32(4): 497-503.

[47] 徐茂鑫, 张孝顺*,余涛. 迁移蜂群优化算法及其在无功优化中的应用[J]. 自动化学报,2017, 43(1): 83-93.

[48] 徐豪,张孝顺*,余涛.非理想通信网络条件下的经济调度鲁棒协同一致性算法[J]. 电力系统自动化,2016, 40(14): 15-24.

[49] 韩传家,张孝顺*,余涛,瞿凯平. 风险调度中引入知识迁移的细菌觅食强化学习优化算法[J]. 电力系统自动化,2017,41(8): 69-77.

[50] 潘振宁,张孝顺*,余涛,王德志. 大规模电动汽车集群分层实时优化调度[J]. 电力系统自动化,2017,41(16):96-104.

[51] 张泽宇,张孝顺*,余涛. 孤岛智能配电网下的AGC机组一致性协同控制算法[J]. 控制理论与应用, 2016, 33(5): 599-607.

[52] 王德志,张孝顺*,余涛,等. 基于帕累托纳什均衡博弈的电网/多元家庭用户互动多目标优化算法[J].电力自动化设备,2017, 37(5): 114-121.

[53] 陈艺璇,张孝顺*,余涛. 基于纳什均衡迁移学习的碳-能复合流自律优化[J]. 控制理论与应用,2018, 35(5): 668-681.

[54] 杨博,钟林恩,朱德娜,束洪春,张孝顺*,余涛. 部分遮蔽下改进樽海鞘群算法的光伏系统最大功率跟踪[J]. 控制理论与应用,2019,已录用.

[55] 瞿凯平,张孝顺,余涛,韩传家. 基于知识迁移Q学习算法的多能源系统联合优化调度[J]. 电力系统自动化,2017, 41(15): 18-25.

[56] 王德志,张孝顺,刘前进,等. 基于集成学习的孤岛微电网源-荷协同频率控制[J]. 电力系统自动化,2018, 42(10): 46-52.

[57] 李清,张孝顺,余涛,等.电动汽车充换电站参与电网AGC功率分配的成本一致性算法[J]. 电力自动化设备,2018, 38(3):80-87.

[58] 陈艺璇,张孝顺,郭乐欣,余涛. 基于多智能体强化学习算法的电力系统最优碳-能复合流求解[J]. 高电压技术,2018,已录用.

[59] 陈艺璇,余涛,张孝顺,等. 考虑多种污染物时空分布的电力系统高维多目标优化调度策略[J]. 中国科学:技术科学,2018, 48(7):755-772.

[60] 程乐峰,余涛,张孝顺,等. 信息-物理-社会融合的智慧能源调度机器人及其知识自动化:框架、技术与挑战[J]. 中国电机工程学报. 2018, 38(1):25-40.

[61] 程乐峰,余涛,张孝顺,等. 机器学习在能源与电力系统领域的应用于展望[J]. 电力系统自动化,2019, 43(1):15-31.

[62] 席磊,余涛,张孝顺,等. 基于狼爬山快速多智能体学习策略的电力系统智能发电控制方法[J]. 电工技术学报,2015, 30(23): 93-101.

[63] 杨博,黄琳妮,张孝顺,余涛. 多端高压直流输电系统自适应无源控制器设计[J]. 控制理论与应用, 2017, 34(5):637-647.

[64] 瞿凯平,黄琳妮,余涛,张孝顺. 碳交易机制下多区域综合能源系统的分散调度[J]. 中国电机工程学报,2018, 38(3):697-707.

[65] 殷林飞, 余涛, 张泽宇, 张孝顺. 基于深度自适应动态规划的孤岛主动配电网发电控制与优化一体化算法[J]. 控制理论与应用, 2018, 35(2):169-183.

[66] 殷林飞, 余涛, 陈吕鹏, 张孝顺. 基于CPSS平行系统懒惰强化学习算法的实时发电调控[J]. 自动化学报. DOI:10.16383/j.aas.c180215

[67] 杨博, 束洪春, 张瑞颖, 黄琳妮, 张孝顺, 余涛. 针对柔性高压直流输电系统的交互式教-学优化算法[J]. 控制与决策,2019, 34(2):325-334.

[68] 潘振宁,王克英,瞿凯平,余涛,王德志,张孝顺. 考虑大量电动汽车接入下的电-气-热多能耦合系统协同优化调度[J]. 电力系统自动化,2018, 42(4):104-112.

[69] 郑宝敏,余涛,瞿凯平,张孝顺,殷林飞. 多区域并行协同下的分布式帕累托多目标最优潮流求解[J]. 电力系统自动化,2018, 42(20): 93-101.

[70] 张志义,余涛,王德志,潘振宁,张孝顺. 基于集成学习的含电气热商业楼宇群的分时电价求解[J]. 中国电机工程学报,2019, 39(1):112-125.

 

期刊审稿专家:

IEEE Transactions on Power Systems

IEEE Transactions on Smart Grid

IEEE Access

Applied Energy

Energy

Energy Conversion and Management

International Journal of Energy Research

International Journal of Electrical Power & Energy Systems

IET Generation Transmission & Distribution

Journal of Energy Engineering

Journal of Modern Power Systems and Clean Energy

AEU-International Journal of Electronics and Communications

Transactions of the Institute of Measurement and Control

Engineering Science and Technology, an International Journal

《电力系统自动化》

《控制理论与应用》

 

科研项目

1)项目名称:基于深度强化学习的综合能源系统最优调度策略自动生成研究;起止时间为2019.01-2021.12;项目来源:汕头大学;主持。

2) 项目名称:源-网-荷协同的智能电网能量管理和运行控制基础研究(973项目课题5);起止时间:2013.01-2017.12;项目来源:国家科技部;参与。

3) 项目名称:含大规模新能源的交直流互联大电网智能运行与柔性控制关键技术(863项目任务5);起止时间:2012.05-2015.04;项目来源:国家科技部;参与。

4) 项目名称:智能发电控制的混合均衡态及其多智能体随机均衡对策理论;起止时间:2012.01-2015.12;项目来源:国家自然科学基金委员会;参与。

5) 项目名称:电力系统频率自治与虚拟发电部落的智能协同控制理论;起止时间:2015.01-2018.12;项目来源:国家自然科学基金委员会;参与。

6) 项目名称:能源互联网的多尺度精细建模及其运行规划研究;起止时间:2015.11-2017.10;项目来源:中国南方电网有限责任公司电网技术研究中心;参与。

7) 项目名称:基于主动配电网的智能用电模式动态优化关键技术研究;起止时间:2014.09-2017.09;项目来源:云南电网公司玉溪供电局;参与。

8) 项目名称:面向能源互联网的配网侧/需求侧综合能源管理技术研究与示范;起止时间:2016.12-2018.12;项目来源:贵州电网有限责任公司;参与。

9) 项目名称:考虑多种影响因素的配电网供电可靠性分析及算法研究项目;起止时间:2015.10-2017.05;项目来源:中国南方电网有限责任公司电网技术研究中心;参与。

10) 项目名称:深圳供电局有限公司“十二五”节能减排规划;起止时间:2014.01-2014.12;项目来源:深圳供电局有限公司;参与。

11) 项目名称:基于设备可靠性分析的一次设备管理优化方法及系统研制;起止时间:2013.11-2014.12;项目来源:广东电网公司江门供电局;参与。

12) 项目名称:分布式新能源接入配电网技术研究;起止时间:2013.08-2014.07;项目来源:云南电网公司规划研究中心;经费:参与。

13) 项目名称:多种低碳新能源结构下的最优机网协调配合策略研究;起止时间:2012.10-2013.10;项目来源:广东电网公司韶关供电局;参与。

14) 项目名称:专线用户节电潜力在线自动检测和快速能源审计职能系统的研制;起止时间:2012.09-2014.12;项目来源:广东电网公司韶关供电局;参与。

15) 项目名称:贵州省地区电网“十二五”节能减排工作方案研究;起止时间:2011.12-2012.07;项目来源:贵州电网有限责任公司;参与。

16) 项目名称:南方电网公司“十二五”节能减排规划;起止时间:2011.07-2012.03;项目来源:南方电网综合能源有限公司;参与。

主要荣誉

1) 2018,荣获“2018年度电力创新奖”技术类一等奖,颁奖单位:中国电力企业联合会。

2) 2017,广东省优秀学生(研究生阶段)荣誉称号。

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