Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor

doi:10.11930/j.issn.1004-9649.202208068

Abstract

Abstract: In recent years, the proportion of wind power connected to the grid has increased significantly, and the probability of occurrence of sub-synchronous/super-synchronous oscillations caused by this has also been greatly raised, which seriously threatens the safety and steadiness of the system. Accurate identification of sub-synchronous/super-synchronous oscillation parameters is the basis for oscillation suppression. Therefore, this paper proposes an identification method based on the attention mechanism of the residual convolutional neural network (CNN). The local correlation and weight sharing of the convolutional neural network determine its stronger feature learning and expression ability, and thus, the oscillation parameters can be more accurately identified when it is combined with the attention mechanism. Meanwhile, this method introduces residual connections to solve the problems of gradient vanishing and network degradation in the deep convolutional neural network. Simulations indicate that compared with the traditional method, this method can completely identify the parameters of sub-synchronous/super-synchronous oscillations on the data of a short time window. In addition, it can avoid the identification error caused by the subjective factors of the traditional method and reduce the complexity of oscillation parameter identification.

Key words: sub-synchronous/super-synchronous oscillation, synchrophasor, parameter identification, convolutional neural network, attention mechanism, residual network

LU Youwen, CUI Hao, CHEN Jianing, PENG Xiangjia, FENG Shuang, LIU Dong. Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor[J]. Electric Power, 2023, 56(4): 46-55,67.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202208068

https://www.electricpower.com.cn/EN/Y2023/V56/I4/46

References

[1] 蔡维正, 郭昆丽, 刘璐雨, 等. 基于一阶LADRC控制的直驱风机次同步振荡抑制策略[J]. 中国电力, 2022, 55(4): 175–184
CAI Weizheng, GUO Kunli, LIU Luyu, et al. Subsynchronous oscillation mitigation strategy based on first-order LADRC for direct-drive wind turbines[J]. Electric Power, 2022, 55(4): 175–184
[2] 张放, 刘军, 李佳欣, 谢小荣. 基于同步相量轨迹拟合的电力系统次同步/超同步振荡的实时参数辨识[J/OL]. 中国电机工程学报: 1–13[2023-01-12]. DOI:10.13334/j.0258-8013.pcsee.212806.
ZHANG Fang, LIU Jun, LI Jiaxin, et al. Real-time parameter identification with synchrophasor trajectory fitting technique for subsynchronous/supersynchronous oscillations in power systems[J/OL]. Proceeding of the CSEE: 1–13[2023-01-12]. DOI:10.13334/j.0258-8013.pcsee.212806.
[3] 李奇南, 夏勇军, 张晓林, 等. 渝鄂柔性直流输电系统中高频振荡影响因素及抑制策略[J]. 中国电力, 2022, 55(7): 11–21
LI Qinan, XIA Yongjun, ZHANG Xiaolin, et al. Key factors of medium-high frequency oscillation in chongqing-hubei HVDC system and suppression strategies[J]. Electric Power, 2022, 55(7): 11–21
[4] 陈良双, 吴思奇, 喻文倩, 等. 基于转子侧附加阻尼控制的双馈风机并网次/超同步振荡抑制方法[J]. 电力系统保护与控制, 2021, 49(15): 47–58
CHEN Liangshuang, WU Siqi, YU Wenqian, et al. A sub/super-synchronous oscillation suppression method for a DFIG-connected grid based on additional damping control on the rotor side converter[J]. Power System Protection and Control, 2021, 49(15): 47–58
[5] 刘韶峰, 徐泰山, 鲍颜红, 等. 基于K-means聚类和同步挤压小波变换的次同步振荡检测[J]. 电力科学与技术学报, 2021, 36(4): 132–140
LIU Shaofeng, XU Taishan, BAO Yanhong, et al. Sub-synchronous oscillation detection based on K-means clustering and frequency synchrosqueezing wavelet transforms[J]. Journal of Electric Power Science and Technology, 2021, 36(4): 132–140
[6] 王红星, 郭敬梅, 谢志文, 等. 海上风电次/超同步振荡的网侧附加阻尼抑制方法[J]. 南方电网技术, 2021, 15(11): 49–55
WANG Hongxing, GUO Jingmei, XIE Zhiwen, et al. Grid-side supplementary damping suppression method of sub-& super-synchronous oscillation in offshore wind farms[J]. Southern Power System Technology, 2021, 15(11): 49–55
[7] 马宁宁, 谢小荣, 贺静波, 等. 高比例新能源和电力电子设备电力系统的宽频振荡研究综述[J]. 中国电机工程学报, 2020, 40(15): 4720–4732
MA Ningning, XIE Xiaorong, HE Jingbo, et al. Review of wide-band oscillation in renewable and power electronics highly integrated power systems[J]. Proceedings of the CSEE, 2020, 40(15): 4720–4732
[8] XIE X R, ZHAN Y, SHAIR J, et al. Identifying the source of subsynchronous control interaction via wide-area monitoring of sub/super-synchronous power flows[J]. IEEE Transactions on Power Delivery, 2020, 35(5): 2177–2185.
[9] LIN H C. Power harmonics and interharmonics measurement using recursive group-harmonic power minimizing algorithm[J]. IEEE Transactions on Industrial Electronics, 2012, 59(2): 1184–1193.
[10] 李宁, 左培丽, 王新刚, 等. 基于改进DFT和时域准同步的间谐波检测算法[J]. 电力自动化设备, 2017, 37(4): 170–178
LI Ning, ZUO Peili, WANG Xingang, et al. Inter-harmonic detection based on improved DFT and time-domain quasi-synchronization[J]. Electric Power Automation Equipment, 2017, 37(4): 170–178
[11] 张超, 王维庆, 邱衍江, 等. 大规模风电并网地区次同步谐波检测方法[J]. 高电压技术, 2019, 45(7): 2194–2202
ZHANG Chao, WANG Weiqing, QIU Yanjiang, et al. Detection method of subsynchronous harmonic in regions with large scale wind power paralleled in grid[J]. High Voltage Engineering, 2019, 45(7): 2194–2202
[12] 杨京, 王彤, 唐俊刺. 基于滑窗FFT的次同步振荡时变幅频在线监测方法[J]. 中国电力, 2020, 53(11): 139–146
YANG Jing, WANG Tong, TANG Junci. Subsynchronous oscillation time-varying amplitude frequency on-line monitoring method based on sliding window FFT[J]. Electric Power, 2020, 53(11): 139–146
[13] 赵黎丽. 基于相关Hanning窗插值的间谐波分析算法[J]. 电工技术学报, 2008, 23(11): 153–158
ZHAO Lili. Inter-harmonics analysis based on correlation hanning window and interpolation algorithm[J]. Transactions of China Electrotechnical Society, 2008, 23(11): 153–158
[14] CHEN L, ZHAO W, WANG F P, et al. An interharmonic phasor and frequency estimator for subsynchronous oscillation identification and monitoring[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(6): 1714–1723.
[15] 肖怀硕, 贾梧桐, 肖冰莹, 等. 基于改进变分模态分解的低频振荡模式辨别[J]. 电力工程技术, 2020, 39(2): 95–102
XIAO Huaishuo, JIA Wutong, XIAO Bingying, et al. An identification method for power system low-frequency oscillation based on parameter optimized variational mode decomposition[J]. Electric Power Engineering Technology, 2020, 39(2): 95–102
[16] 吴熙, 陈曦, 吕万, 等. 电力系统次同步振荡检测与在线定位技术综述[J]. 电力自动化设备, 2020, 40(9): 129–141
WU Xi, CHEN Xi, LÜ Wan, et al. Review of detection and online localization technology of sub-synchronous oscillation in power system[J]. Electric Power Automation Equipment, 2020, 40(9): 129–141
[17] YANG X M, ZHANG J N, XIE X R, et al. Interpolated DFT-based identification of sub-synchronous oscillation parameters using synchrophasor data[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2662–2675.
[18] 刘灏, 李珏, 毕天姝, 等. 基于PMU相量的次/超同步间谐波识别方法[J]. 电网技术, 2017, 41(10): 3237–3245
LIU Hao, LI Jue, BI Tianshu, et al. Subsynchronous and supersynchronous inter-harmonic identification method based on phasor measurements[J]. Power System Technology, 2017, 41(10): 3237–3245
[19] LIU H, BI T S, LI J, et al. Inter-harmonics monitoring method based on PMUs[J]. IET Generation, Transmission & Distribution, 2017, 11(18): 4414–4421.
[20] 刘灏, 许苏迪, 毕天姝, 等. 基于同步相量数据的间谐波还原算法[J]. 电力自动化设备, 2019, 39(1): 153–160
LIU Hao, XU Sudi, BI Tianshu, et al. Inter-harmonic reduction algorithm based on synchrophasor measurements[J]. Electric Power Automation Equipment, 2019, 39(1): 153–160
[21] ZHANG F, CHENG L, GAO W Z, et al. Synchrophasors-based identification for subsynchronous oscillations in power systems[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 2224–2233.
[22] 马钺, 蔡东升, 黄琦. 基于Rife-Vincent窗和同步相量测量数据的风电次同步振荡参数辨识[J]. 中国电机工程学报, 2021, 41(3): 790–803
MA Yue, CAI Dongsheng, HUANG Qi. Parameter identification of wind power sub-synchronous oscillation based on Rife-Vincent window and synchrophasor data[J]. Proceedings of the CSEE, 2021, 41(3): 790–803
[23] LIU H K, XIE X R, HE J B, et al. Subsynchronous interaction between direct-drive PMSG based wind farms and weak AC networks[J]. IEEE Transactions on Power Systems, 2017, 32(6): 4708–4720.
[24] 马宁宁, 谢小荣, 亢朋朋, 等. 高比例风电并网系统次同步振荡的广域监测与分析[J]. 中国电机工程学报, 2021, 41(1): 65–74, 398
MA Ningning, XIE Xiaorong, KANG Pengpeng, et al. Wide-area monitoring and analysis of subsynchronous oscillation in power systems with high-penetration of wind power[J]. Proceedings of the CSEE, 2021, 41(1): 65–74, 398
[25] WANG L, XIE X R, JIANG Q R, et al. Investigation of SSR in practical DFIG-based wind farms connected to a series-compensated power system[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2772–2779.
[26] ZHANG Q S, WANG X, WU Y N, et al. Interpretable CNNs for object classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3416–3431.
[27] LU L Q, YI Y H, HUANG F L, et al. Integrating local CNN and global CNN for script identification in natural scene images[J]. IEEE Access, 2019, 7: 52669–52679.
[28] LI G F, TANG H, SUN Y, et al. Hand gesture recognition based on convolution neural network[J]. Cluster Computing, 2019, 22(2): 2719–2729.
[29] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. IEEE, 2018: 7132–7141.
[30] ZHANG M, SU H H, WEN J H. Classification of flower image based on attention mechanism and multi-loss attention network[J]. Computer Communications, 2021, 179: 307–317.
[31] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional Block Attention Module[C]//European Conference on Computer Vision. Cham: Springer, 2018: 3–19.