A Knowledge Learning Model for Intelligent Check of Wind Farm Dynamic Parameters Based on DDPG

doi:10.11930/j.issn.1004-9649.202102071

Abstract

Abstract: As the penetration rate of wind power increases and a large number of power electronic components are connected, the dynamic characteristics exhibited by wind farms become more and more complex. The traditional simulation verification methods based on a small number of cases and analysis are facing challenges. The successful application of a new generation of artificial intelligence represented by deep reinforcement learning in multiple fields provides a reference for the intelligent check of the dynamic parameters of wind farms. Based on the equivalent model of doubly-fed wind farms and the deep deterministic policy gradient ( DDPG) algorithm, a knowledge learning model for intelligent check of wind farm dynamic parameters is proposed. The proposed model gradually obtains the intelligent check knowledge of the wind farm dynamic parameters through a large number of simulations and learning, initially realizing the “knowledge”-based check of wind farm dynamic parameters. Finally, based on the measured disturbance data of wind turbines in a wind farm, the parameter check knowledge obtained through intelligent learning are used to correct the dominant parameters of wind farm dynamic characteristics, and the results are compared with traditional heuristic algorithms, which verifies the effectiveness of the proposed method.

Key words: wind farm, dominant parameters, intelligent parameter correction, deep reinforcement learning

ZHOU Qingfeng, WANG Sichun, LI Dexin, LIU Jiaqi, LI Tong. A Knowledge Learning Model for Intelligent Check of Wind Farm Dynamic Parameters Based on DDPG[J]. Electric Power, 2022, 55(5): 32-38.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202102071

https://www.electricpower.com.cn/EN/Y2022/V55/I5/32

References

[1] 潘雪冬, 张文朝, 顾雪平. 发电机模型及参数动态仿真准确度的评估[J]. 电力系统及其自动化学报, 2015, 27(2): 64–69
PAN Xuedong, ZHANG Wenchao, GU Xueping. Accuracy assessment of models and parameters in generator dynamic simulation[J]. Proceedings of the CSU-EPSA, 2015, 27(2): 64–69
[2] 陈刚, 何江, 吴小辰, 等. 用基于PMU数据的理想电压源法实现混合动态仿真验证策略[J]. 电力系统保护与控制, 2011, 39(22): 57–61,67
CHEN Gang, HE Jiang, WU Xiaochen, et al. A hybrid dynamic simulation validation strategy based on ideal voltage source with PMU data[J]. Power System Protection and Control, 2011, 39(22): 57–61,67
[3] 李大虎, 张志杰, 张伟晨, 等. 背靠背柔性直流接入电网后的影响评估[J]. 电力系统保护与控制, 2019, 47(3): 71–80
LI Dahu, ZHANG Zhijie, ZHANG Weichen, et al. Influence evaluation of provincial power grid integrated with back-to-back VSC-HVDC[J]. Power System Protection and Control, 2019, 47(3): 71–80
[4] 吴为, 汤涌, 孙华东, 等. 基于广域量测信息的电力系统暂态稳定研究综述[J]. 电网技术, 2012, 36(9): 81–87
WU Wei, TANG Yong, SUN Huadong, et al. A survey on research of power system transient stability based on wide-area measurement information[J]. Power System Technology, 2012, 36(9): 81–87
[5] 蒋振国, 周在彦, 刘征帆, 等. 计及多扰动的同步发电机模型参数有效性评估[J]. 智慧电力, 2020, 48(7): 100–105,111
JIANG Zhenguo, ZHOU Zaiyan, LIU Zhengfan, et al. Effectiveness evaluation of synchronous generator model parameters considering multi-disturbances[J]. Smart Power, 2020, 48(7): 100–105,111
[6] 安军, 王孜航, 穆钢, 等. 基于WAMS测量和戴维南等值的电力系统动态仿真误差溯源及可信度验证方法[J]. 电网技术, 2013, 37(5): 1389–1394
AN Jun, WANG Zihang, MU Gang, et al. Confidence level verification and tracking to error source for power grid dynamic simulation based on WAMS and thevenin equivalence[J]. Power System Technology, 2013, 37(5): 1389–1394
[7] 姜赫, 安军, 李德鑫, 等. 基于WAMS实测数据的电力系统仿真致差区域识别方法[J]. 电力系统保护与控制, 2021, 49(4): 96–103
JIANG He, AN Jun, LI Dexin, et al. Recognition method of power system simulation error area based on WAMS measured data[J]. Power System Protection and Control, 2021, 49(4): 96–103
[8] 龙云, 王建全. 基于粒子群游算法的同步发电机参数辨识[J]. 大电机技术, 2003(1): 8–11
LONG Yun, WANG Jianquan. Parameters identification of synchronous generator based on particle swarm optimization theory[J]. Large Electric Machine and Hydraulic Turbine, 2003(1): 8–11
[9] 刘兴杰, 闫亮. 基于轨迹灵敏度的励磁系统参数可辨识性分析[J]. 电力系统自动化, 2019, 43(1): 209–214,227
LIU Xingjie, YAN Liang. Parameter identifiability analysis of excitation system based on trajectory sensitivity[J]. Automation of Electric Power Systems, 2019, 43(1): 209–214,227
[10] 韩睿, 郑竞宏, 朱守真, 等. 基于灵敏度分析的同步发电机参数分步辨识策略[J]. 电力自动化设备, 2012, 32(5): 74–80
HAN Rui, ZHENG Jinghong, ZHU Shouzhen, et al. Step identification of synchronous generator parameters based on sensitivity analysis[J]. Electric Power Automation Equipment, 2012, 32(5): 74–80
[11] 吕泽芳, 马刚, 孙先文, 等. 人工智能安全的概念、分类及研究现状综述(一)[J]. 智慧电力, 2019, 47(8): 32–42
LV Zefang, MA Gang, SUN Xianwen, et al. Overview of concept, classification & study status of artificial intelligence security(Ⅰ)[J]. Smart Power, 2019, 47(8): 32–42
[12] SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484–489.
[13] SILVER D, SCHRITTWIESER J, SIMONYAN K, et al. Mastering the game of Go without human knowledge[J]. Nature, 2017, 550(7676): 354–359.
[14] 张瑞强. 人工智能技术在电力系统故障诊断中的运用分析[J]. 现代信息科技, 2019, 3(3): 29–31
ZHANG Ruiqiang. Analysis on the application of artificial intelligence technology in power system fault diagnosis[J]. Modern Information Technology, 2019, 3(3): 29–31
[15] 汤奕, 崔晗, 李峰, 等. 人工智能在电力系统暂态问题中的应用综述[J]. 中国电机工程学报, 2019, 39(1): 2–13,315
TANG Yi, CUI Han, LI Feng, et al. Review on artificial intelligence in power system transient stability analysis[J]. Proceedings of the CSEE, 2019, 39(1): 2–13,315
[16] 周聪. 人工智能技术在电力调度中的应用[J]. 数字技术与应用, 2018, 36(11): 63–64
ZHOU Cong. Application of artificial intelligence technology in electric power dispatching[J]. Digital Technology & Application, 2018, 36(11): 63–64
[17] 薛禹胜, 雷兴, 薛峰, 等. 关于风电不确定性对电力系统影响的评述[J]. 中国电机工程学报, 2014, 34(29): 5029–5040
XUE Yusheng, LEI Xing, XUE Feng, et al. A review on impacts of wind power uncertainties on power systems[J]. Proceedings of the CSEE, 2014, 34(29): 5029–5040
[18] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1–14
DING Ming, WANG Weisheng, WANG Xiuli, et al. A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE, 2014, 34(1): 1–14
[19] 徐超, 卢锦玲, 张洁, 等. 提高双馈风力发电机并网系统暂态稳定性的控制策略[J]. 电工电能新技术, 2015, 34(6): 45–51
XU Chao, LU Jinling, ZHANG Jie, et al. Control strategy for transient stability improvement of doubly-fed wind power generation system[J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34(6): 45–51
[20] 陈学松, 杨宜民. 强化学习研究综述[J]. 计算机应用研究, 2010, 27(8): 2834–2838,2844
CHEN Xuesong, YANG Yimin. Reinforcement learning: survey of recent work[J]. Application Research of Computers, 2010, 27(8): 2834–2838,2844
[21] 刘全, 翟建伟, 章宗长, 等. 深度强化学习综述[J]. 计算机学报, 2018, 41(1): 1–27
LIU Quan, ZHAI Jianwei, ZHANG Zongzhang, et al. A survey on deep reinforcement learning[J]. Chinese Journal of Computers, 2018, 41(1): 1–27
[22] 赵星宇, 丁世飞. 深度强化学习研究综述[J]. 计算机科学, 2018, 45(7): 1–6
ZHAO Xingyu, DING Shifei. Research on deep reinforcement learning[J]. Computer Science, 2018, 45(7): 1–6
[23] PETERS J, SCHAAL S. Natural actor-critic[J]. Neurocomputing, 2008, 71(7): 1180–1190.
[24] BHATNAGAR S, SUTTON R S, GHAVAMZADEH M, et al. Natural actor-critic algorithms[J]. Automatica, 2009, 45(11): 2471–2482.
[25] 刘建伟, 高峰, 罗雄麟. 基于值函数和策略梯度的深度强化学习综述[J]. 计算机学报, 2019, 42(6): 1406–1438
LIU Jianwei, GAO Feng, LUO Xionglin. Survey of deep reinforcement learning based on value function and policy gradient[J]. Chinese Journal of Computers, 2019, 42(6): 1406–1438