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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (5): 70-76.doi: 10.6040/j.issn.1672-3961.0.2022.174

• • 上一篇    

基于卷积神经网络和层次分析的新能源电源调频能力智能预测方法

王智伟1,徐海超1*,郭相阳1,马炯2,褚云龙1,陈前昌1,卢治3   

  1. 1.国家电网有限公司西北分部, 陕西 西安 710048;2.中国电力科学研究院有限公司南京分院, 江苏 南京 210003;3.电网智能化调度与控制教育部重点实验室(山东大学), 山东 济南 250061
  • 发布日期:2022-10-20
  • 作者简介:王智伟(1984— ),男,宁夏石嘴山人,硕士,高级工程师,主要研究方向为电力系统运行、控制、分析. E-mail:wangzw@nw.sgcc.com.cn. *通信作者简介:徐海超(1991— ),男,陕西西安人,硕士,工程师,主要研究方向为电力系统调度运行与控制技术工作. E-mail:xuhaichao_nwsgcc@163.com

Intelligent method based on convolutional neural network and analytic hierarchy process to predict frequency regulation ability of renewable energy power

WANG Zhiwei1, XU Haichao1*, GUO Xiangyang1, MA Jiong2, CHU Yunlong1, CHEN Qianchang1, LU Zhi3   

  1. 1. Northwest Branch, State Grid Corporation of China, Xi'an 710048, Shaanxi, China;
    2. Nanjing Branch, China Electric Power Research Institute Co., Ltd., Nanjing 210003, Jiangsu, China;
    3. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China
  • Published:2022-10-20

摘要: 针对新型电力系统实时运行期间,各个新能源机组的一次调频能力难以定量评估问题,源于数据驱动理论,提出一种基于卷积神经网络(convolutional neural networks, CNN)和层次分析法的新能源机组调频能力综合评估方法。以新能源机组有功出力、调频持续时间以及机组容量为指标,建立评估指标体系;采用卷积神经网络技术合理预测不同指标的数值,并通过层次分析法确定各指标间的相对权重。在PSCAD/EMTDC仿真软件中搭建包含风电场和光伏电站的IEEE 3机9节点模型进行仿真验证,算例结果表明所提出指标可以在数值上定量反映出各个新能源机组的调频能力,也验证了文中所提评估方法的有效性。

关键词: 新能源电源, 一次调频能力, 数据驱动理论, 评估指标体系, 卷积神经网络, 层次分析法

中图分类号: 

  • TM615
[1] 王彩霞,李琼慧,雷雪姣.储能对大比例可再生能源接入电网的调频价值分析[J]. 中国电力,2016,49(10):148-152. WANG Caixia, LI Qionghui, LEI Xuejiao. Methodology for analyzing the value of energy storage to power system frequency control in context of high shares of renewable energy[J]. Electric Power, 2016, 49(10):148-152.
[2] 边晓燕,姜莹,赵耀,等.高渗透率可再生能源微电网的风柴荷协调调频策略[J]. 电力系统自动化,2018,42(15):102-109. BIAN Xiaoyan, JIANG Ying, ZHAO Yao, et al. Coordinated frequency regulation strategy of wind, diesel and load for microgrid with high-penetration renewable energy[J]. Automation of Electric Power Systems, 2018, 42(15):102-109.
[3] 赵大伟,马进,钱敏慧,等.光伏电站参与大电网一次调频的控制增益研究[J]. 电网技术,2019,43(2):425-435. ZHAO Dawei, MA Jin, QIAN Minhui, et al. Research on control gain for photovoltaic power plants participating in primary frequency regulation of large power grid[J]. Power System Technology, 2019, 43(2):425-435.
[4] 兰飞,潘益丰,时萌,等.双馈风电机组变系数虚拟惯量优化控制[J]. 电力系统自动化,2019,43(12):51-59. LAN Fei, PAN Yifeng, SHI Meng, et al. Optimal variable-coefficient virtual inertia control for DFIG-based wind turbines[J]. Automation of Electric Power Systems, 2019, 43(12):51-59.
[5] 孙骁强,刘鑫, 程松,等.光伏逆变器参与西北送端大电网快速频率响应能力实测分析[J]. 电网技术,2017,41(9):2792-2798. SUN Xiaoqiang, LIU Xin, CHENG Song, et al. Actual measurement and analysis of fast frequency response capacity of PV-inverters in northwest power grid[J]. Power System Technology, 2017, 41(9):2792-2798.
[6] 王卿卿,张志文,邵霞,等.风水协同模式下系统调频能力评估方法研究[J]. 电工技术,2020(21):16-20. WANG Qingqing, ZHANG Zhiwen, SHAO Xia, et al. Research on evaluation method of system frequency modulation ability under wind power and hydropower cooperation mode[J]. Electric Engineering, 2020(21):16-20.
[7] 张道田,杨文思,张扬,等.影响风电一次调频能力的参数选取[J]. 江西电力,2019,43(12):56-60. ZHANG Daotian, YANG Wensi, ZHANG Yang, et al. Selection of parameters affecting primary frequency regulation capability of wind power[J]. Jiangxi Electric Power, 2019, 43(12):56-60.
[8] 周勇良,余光正,刘建锋,等.基于改进长期循环卷积神经网络的海上风电功率预测[J]. 电力系统自动化,2021,45(3):183-191. ZHOU Yongliang, YU Guangzheng, LIU Jianfeng, et al. Offshore wind power prediction based on improved long-term recurrent convolutional neural network[J]. Automation of Electric Power Systems, 2021, 45(3):183-191.
[9] WANG Huaizhi, LI Gangqiang, WANG Guibin, et al. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017, 188(15):56-70.
[10] 余光正,陆柳,汤波,等.基于云图特征提取的改进混合神经网络超短期光伏功率预测方法[J]. 中国电机工程学报,2021,41(20):6989-7003. YU Guangzheng, LU Liu, TANG Bo, et al. An improved hybrid neural network ultra-short-term photovoltaic power forecasting method based on cloud image feature extraction[J]. Proceedings of the CSEE, 2021, 41(20):6989-7003.
[11] 刘文杰,陈耀,宋晓宁,等.基于时域卷积网络精细化光伏发电功率预测[J]. 供用电,2020,37(10):76-82. LIU Wenjie, CHEN Yao, SONG Xiaoning, et al. A refined photovoltaic power prediction based on time domain convolutional network[J]. Distribution & Utilization, 2020, 37(10):76-82.
[12] 朱乔木,李弘毅,王子琪,等.基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术,2017,41(12):3797-3802. ZHU Qiaomu, LI Hongyi, WANG Ziqi, et al. Short-term wind power forecasting based on LSTM[J]. Power System Technology, 2017, 41(12):3797-3802.
[13] 林君豪, 张焰,赵腾,等.基于改进卷积神经网络拓扑特征挖掘的配电网结构坚强性评估方法[J]. 中国电机工程学报,2019,39(1):84-96. LIN Junhao, ZHANG Yan, ZHAO Teng, et al. Structure strength assessment method of distribution network based on improved convolution neural network and network topology feature mining[J]. Proceeding of the CSEE, 2019, 39(1):84-96.
[14] 余印振,韩哲哲,许传龙.基于深度卷积神经网络和支持向量机的NOx浓度预测[J]. 中国电机工程学报,2022,42(1):238-248. YU Yinzhen, HAN Zhezhe, XU Chuanlong. NOx concentration prediction based on deep convolution neural network and support vector machine[J]. Proceeding of the CSEE, 2022, 42(1):238-248.
[15] 张海峥,张兴,李明,等.一种有功备用式光伏虚拟同步控制策略[J]. 电网技术,2019,43(2):514-520. ZHANG Haizheng, ZHANG Xing, LI Ming, et al. A photovoltaic virtual synchronous generator control strategy based on active power reserve[J]. Power System Technology, 2019, 43(2):514-520.
[16] 王天翔,程雪坤,李伟超,等.基于变参数减载控制的风电场一次调频策略[J]. 中国电力,2021,54(12):94-101. WANG Tianxiang, CHEN Xuekun, LI Weichao, et al. Primary frequency control strategy for wind farms based on variable parameter de-loading control[J]. Electric Power, 2021, 54(12):94-101.
[17] LALOR Gillian, MULLANE Alan, O'MALLEY Mark. Frequency control and wind turbine technologies[J]. IEEE Transactions on Power Systems, 2005, 20(4):1905-1913.
[18] MORREN Johan, DE HAAN S W H, KLING W L, et al. Wind turbines emulating inertia and supporting primary frequency control[J]. IEEE Transactions on Power Systems, 2006, 21(1):433-434.
[19] XIN Huanhai, LIU Yun, WANG Zhen, et al. A new frequency regulation strategy for photovoltaic systems without energy storage[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4):985-993.
[20] NANOU Sotirios I, PAPAKONSTANTINOU Apostolos G, PAPATHANASSIOU Stavros A. A generic model of two-stage grid-connected PV systems with primary frequency response and inertia emulation[J]. Electric Power Systems Research, 2015, 127:186-196.
[21] 范高锋,王伟胜,刘纯,等.基于人工神经网络的风电功率预测[J]. 中国电机工程学报,2008,28(34):118-123. FAN Gaofeng, WANG Weisheng, LIU Chun, et al. Wind power prediction based on artificial neural network[J]. Proceedings of the CSEE, 2008, 28(34):118-123.
[22] 王昕,黄柯,郑益慧,等.基于PNN/PCA/SS-SVR的光伏发电功率短期预测方法[J]. 电力系统自动化,2016,40(17):156-162. WANG Xin, HUANG Ke, ZHENG Yihui, et al. Short-term forecasting method of photovoltaic output power based on PNN/PCA/PCA/SS-SVR[J]. Automation of Electric Power Systems, 2016, 40(17):156-162.
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