基于测量阻抗变化轨迹智能识别的水轮发电机失磁保护

doi:10.19595/j.cnki.1000-6753.tces.211901

Abstract
Figure/Table
References
Related Citation (8)

Download: PDF (3000 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The complete or partial loss of excitation of large hydro generators is a common and serious fault, which requires the loss of excitation protection to act more quickly. Traditional loss of excitation protection based on static boundary of terminal impedance can only judge whether or not it is loss of excitation by the final static local information after the fault, it cannot reflect the changes of measurement impedance of various disturbances in complex power grid environment, and it is difficult to satisfy the selectivity and rapidity at the same time. Recently, either some mechanism methods that reflect the change of electrical quantity or some machine learning methods are difficult to adapt to unknown scenarios. In order to improve the generalization ability of machine learning loss of excitation protection, a new data-driven loss of excitation protection based on measurement impedance dynamic trajectory recognition was proposed in this paper.
Firstly, the dynamic time-series motion characteristics of the impedance trajectory measured at the terminal in the fixed time window was analyzed, and statistics was introduced to describe the distribution of the time-series characteristics; Secondly, the features were sorted by using mutual information based the minimal-redundancy-maximal-relevance criterion (mRMR); Thirdly, the weighted convex combination of global and local kernel functions was used as the multi-kernel function to construct the multiple kernel learning support vector machine (MKLSVM) model, and the Wrapper strategy was used to determine the final input features; Finally, considering the influence of the severity of generator loss of excitation fault on the measured impedance trajectory, a double time window discrimination principle based on the classification function distance was proposed to improve the reliability of loss of excitation protection. This loss of excitation discrimination model considers both global and local features, and further improves the generalization ability of SVM classification model.
Simulation results of simplified equivalent hydraulic generator transmission system show that the average accuracy of verification set before and after feature selection is 99.52% and 99.43% respectively under 7 single time windows within 0.3~3 s, which shows that mRMR can retain key features well. Further, the average accuracy of identifying the loss of excitation with the double time window discriminant strategy can reach 100% in 1.5 s time, which indicates that the reliability of the loss of excitation protection has been significantly improved. The generalization ability of the previously trained discriminant model was tested using IEEE-39 Bus System with new energy access considering more disturbance conditions, and the average accuracy of the test set in 1.5 s time is above 96.95%. In addition, the generalization ability of MKLSVM composed of multi-kernel function is stronger than single-kernel SVM. Finally, the influence of time window on the loss of excitation protection scheme are investigated. The results show that the severity of loss of excitation is inversely proportional to the time window length required for identification, which just meets the rapidity nature of loss of excitation protection.
The following conclusions can be drawn from the simulation analysis: (1) The proposed scheme of loss of excitation protection for hydro generators guides the design of artificial intelligence frame of loss of excitation protection by utilizing the characteristics of measured impedance change trajectory with explicit physical meaning in the mechanism-based traditional loss of excitation protection, and achieves their complementary advantages. (2) The feature selection method based on mRMR and the MKLSVM model which considers both local and global information enhance the generalization ability of the model, while the two-time window discriminant based on the distance of classification function enhances the reliability of the model. (3) The proposed principle for identifying the loss of excitation fault is independent of the strength of the external power network and the topology of the power network, and has a strong applicability.

Key words： Hydro generator loss of excitation protection impedance trajectory multiple kernel learning support vector machine intelligent identification generalization ability

Received: 20 November 2021

PACS:

TM772

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Chao
	Xiao Shiwu

Cite this article:

Liu Chao,Xiao Shiwu. Loss of Excitation Protection of Hydro Generator Based on Intelligent Identification of Measured Impedance Change Trajectory[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1808-1825.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211901 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I7/1808

[1] 姚晴林. 同步发电机失磁及其保护[M]. 北京: 机械工业出版社, 1981.
[2] 常忠蛟, 刘云. 巴西电网“3.21”大停电中控制保护系统动作分析及启示[J]. 电网技术, 2020, 44(11): 4415-4426.
Chang Zhongjiao, Liu Yun.Analysis on and inspiration of the control and protection actions during “0321/March 21” blackout in Brazilian power grid[J]. Power System Technology, 2020, 44(11): 4415-4426.
[3] 郑涛, 余青蔚, 詹荣荣, 等. 调相机接入对发电机失磁保护的影响[J]. 电力系统保护与控制, 2018, 46(4): 50-56.
Zheng Tao, Yu Qingwei, Zhan Rongrong, et al.Impact of synchronous condenser access on generator loss of excitation protection[J]. Power System Protection and Control, 2018, 46(4): 50-56.
[4] 沈全荣, 陈佳胜, 陈俊, 等. 基于导纳特性的水轮发电机失磁保护新判据[J]. 电力自动化设备, 2017, 37(7): 220-223.
Shen Quanrong, Chen Jiasheng, Chen Jun, et al.LOE protection criterion based on admittance characteristic for hydraulic generator[J]. Electric Power Automation Equipment, 2017, 37(7): 220-223.
[5] 李晖, 鲁功强, 王育学, 等. 大型水轮发电机失磁保护与低励限制配合问题的探讨[J]. 电力系统保护与控制, 2014, 42(5): 68-72.
Li Hui, Lu Gongqiang, Wang Yuxue, et al.Discussion on coordination between loss of excitation protection and under excitation limit control for large hydro-generator[J]. Power System Protection and Control, 2014, 42(5): 68-72.
[6] Hasani A, Haghjoo F.Fast and secure detection technique for loss of field occurrence in synchronous generators[J]. IET Electric Power Applications, 2017, 11(4): 567-577.
[7] Hasani A, Haghjoo F, da Silva F M F, et al. A current-based differential technique to detect loss of field in synchronous generators[J]. IEEE Transactions on Power Delivery, 2020, 35(2): 514-522.
[8] Noroozi N, Alinejad-Beromi Y, Yaghobi H.Fast approach to detect generator loss of excitation based on reactive power variation[J]. IET Generation, Transmission & Distribution, 2019, 13(4): 453-460.
[9] 贾德峰, 王明东, 傅润炜, 等. 抽水蓄能机组RTDS仿真与失磁保护改进研究[J]. 电力系统保护与控制, 2021, 49(3): 158-164.
Jia Defeng, Wang Mingdong, Fu Runwei, et al.RTDS simulation and improvement of excitation-loss protection for pumped storage units[J]. Power System Protection and Control, 2021, 49(3): 158-164.
[10] 李峰, 王琦, 胡健雄, 等. 数据与知识联合驱动方法研究进展及其在电力系统中应用展望[J]. 中国电机工程学报, 2021, 41(13): 4377-4389.
Li Feng, Wang Qi, Hu Jianxiong, et al.Combined data-driven and knowledge-driven methodology research advances and its applied prospect in power systems[J]. Proceedings of the CSEE, 2021, 41(13): 4377-4389.
[11] de Morais A P, Cardoso G, Mariotto L. An innovative loss-of-excitation protection based on the fuzzy inference mechanism[J]. IEEE Transactions on Power Delivery, 2010, 25(4): 2197-2204.
[12] Amraee T.Loss-of-field detection in synchronous generators using decision tree technique[J]. IET Generation, Transmission & Distribution, 2013, 7(9): 943-954.
[13] Ostovic V.Memory motors-a new class of controllable flux PM machines for a true wide speed operation[C]//Conference Record of the 2001 IEEE Industry Applications Conference, 36th IAS Annual Meeting (Cat. No.01CH37248), Chicago, IL, USA, 2001: 2577-2584.
[14] Rasoulpour M, Amraee T, Sedigh A K.Loss of field protection in synchronous generators based on data mining technique[C]//2019 Smart Grid Conference (SGC), Tehran, Iran, 2019: 1-6.
[15] Rasoulpour M, Amraee T, Sedigh A K.A relay logic for total and partial loss of excitation protection in synchronous generators[J]. IEEE Transactions on Power Delivery, 2020, 35(3): 1432-1442.
[16] 吴雪莲, 刘福锁, 李兆伟, 等. 基于联络线功率轨迹特征的暂态功角稳定性分析[J]. 中国电机工程学报, 2019, 39(11): 3194-3201.
Wu Xuelian, Liu Fusuo, Li Zhaowei, et al.Analysis of transient power angle stability based on the characteristics of power trajectory[J]. Proceedings of the CSEE, 2019, 39(11): 3194-3201.
[17] 王长江, 姜涛, 刘福锁, 等. 基于轨迹灵敏度的暂态过电压两阶段优化控制[J]. 电工技术学报, 2021, 36(9): 1888-1900, 1913.
Wang Changjiang, Jiang Tao, Liu Fusuo, et al.Two-stage optimization control of transient overvoltage based on trajectory sensitivity[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1888-1900, 1913.
[18] 朱存浩, 马世英, 郑超, 等. 基于实测响应轨迹的电力系统暂态不稳定判别[J]. 中国电机工程学报, 2021, 41(15): 5077-5090.
Zhu Cunhao, Ma Shiying, Zheng Chao, et al.Power system transient instability identification based on real-time response trajectory[J]. Proceedings of the CSEE, 2021, 41(15): 5077-5090.
[19] 杨少波, 刘道伟, 安军, 等. 基于长短期记忆网络的电网动态轨迹趋势预测方法[J]. 中国电机工程学报, 2020, 40(9): 2854-2865.
Yang Shaobo, Liu Daowei, An Jun, et al.Trend prediction method of power network dynamic trajectory based on long short term memory neural networks[J]. Proceedings of the CSEE, 2020, 40(9): 2854-2865.
[20] 戎子睿, 林湘宁, 金能, 等. 基于相空间轨迹识别和多判据融合的变压器保护新策略[J]. 中国电机工程学报, 2020, 40(6): 1924-1937.
Rong Zirui, Lin Xiangning, Jin Neng, et al.A new transformer protection scheme based on the recognition of phase space trajectory and multi-criteria fusion[J]. Proceedings of the CSEE, 2020, 40(6): 1924-1937.
[21] 李宗博, 焦在滨, 何安阳. 基于等效磁化曲线智能识别的变压器保护原理[J]. 电工技术学报, 2020, 35(7): 1464-1475.
Li Zongbo, Jiao Zaibin, He Anyang.Equivalent magnetization curve intelligent recognition based transformer protection[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1464-1475.
[22] 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.
[23] 陈宗遥, 卜旭辉, 郭金丽. 基于神经网络的数据驱动互联电力系统负荷频率控制[J]. 电工技术学报, 2022, 37(21): 5451-5461.
Chen Zongyao, Bu Xuhui, Guo Jinli.Neural network based data-driven load frequency control for interconnected power systems[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5451-5461.
[24] 吴月宝, 赵晋斌, 张少腾, 等. 基于径向基神经网络的多负载无线电能传输系统自适应阻抗匹配方法[J]. 电工技术学报, 2021, 36(19): 3969-3977.
Wu Yuebao, Zhao Jinbin, Zhang Shaoteng, et al.An adaptive impedance matching method based on radial basis function neural network in multi-load wireless power transfer systems[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 3969-3977.
[25] 薛禹胜, 赖业宁. 大能源思维与大数据思维的融合(一)大数据与电力大数据[J]. 电力系统自动化, 2016, 40(1): 1-8.
Xue Yusheng, Lai Yening.Integration of macro energy thinking and big data thinking part one big data and power big data[J]. Automation of Electric Power Systems, 2016, 40(1): 1-8.
[26] Peng Hanchuan, Long Fuhui, Ding C.Feature selection based on mutual information criteria of max-dependency, max-relevance, and Min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238.
[27] Li Yang, Zhu Zhichuan, Hou Alin, et al.Pulmonary nodule recognition based on multiple kernel learning support vector machine-PSO[J]. Computational and Mathematical Methods in Medicine, 2018, 2018: 1461470.
[28] 黄彦浩, 于之虹, 史东宇, 等. 基于海量在线历史数据的大电网快速判稳策略[J]. 中国电机工程学报, 2016, 36(3): 596-603.
Huang Yanhao, Yu Zhihong, Shi Dongyu, et al.Strategy of huge electric power system stability quick judgment based on massive historical online data[J]. Proceedings of the CSEE, 2016, 36(3): 596-603.
[29] 周艳真, 吴俊勇, 冀鲁豫, 等. 基于两阶段支持向量机的电力系统暂态稳定预测及预防控制[J]. 中国电机工程学报, 2018, 38(1): 137-147, 350.
Zhou Yanzhen, Wu Junyong, Ji Luyu, et al.Two-stage support vector machines for transient stability prediction and preventive control of power systems[J]. Proceedings of the CSEE, 2018, 38(1): 137-147, 350.
[30] Laube P, et al.Movement beyond the snapshot - dynamic analysis of geospatial lifelines[J]. Computers, Environment and Urban Systems, 2007, 31(5): 481-501.
[31] 王学民. 应用概率统计[M]. 上海: 上海财经大学出版社, 2005.
[32] 石访, 张林林, 胡熊伟, 等. 基于多属性决策树的电网暂态稳定规则提取方法[J]. 电工技术学报, 2019, 34(11): 2364-2374.
Shi Fang, Zhang Linlin, Hu Xiongwei, et al.Power system transient stability rules extraction based on multi-attribute decision tree[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2364-2374.
[33] 李阳, 常佳乐, 王宇阳. 基于群体智能优化的MKL-SVM算法及肺结节识别[J]. 工程科学学报, 2021, 43(9): 1157-1165.
Li Yang, Chang Jiayue, Wang Yuyang.MKL-SVM algorithm for pulmonary nodule recognition based on swarm intelligence optimization[J]. Chinese Journal of Engineering, 2021, 43(9): 1157-1165.
[34] 江涛, 张志安, 程志, 等. 改进遗传算法与领航跟随法的机器人编队方法[J]. 计算机工程与应用, 2020, 56(3): 240-245.
Jiang Tao, Zhang Zhian, Cheng Zhi, et al.Robot formation method with improved genetic algorithm and leader-follower[J]. Computer Engineering and Applications, 2020, 56(3): 240-245.