含风电继电保护应用中的电流互感器饱和电流重构方法

doi:10.19595/j.cnki.1000-6753.tces.221263

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Abstract Conventional researches on relay protection regards anti-saturation for current transformer (CT) as a necessary condition for off-line criterion setting. Actually, a new idea is to use the on-line reconstructed current to participate in the protection calculation. However, after the wind power is connected to the system, mixed fault current of wind power and synchronous generators flows through CT. Among existing saturation current reconstruction studies, numerical calculation methods are difficult to model the interharmonics of wind turbines, and data-driven methods are lacking in calculating speed and noise immunity. In this paper, a CT saturation current reconstruction method in relay protection application was proposed, which can be mainly divided into two stages. In the offline stage, firstly, multiple factors affecting CT saturation were considered when generating the dataset. Next, the stacked long short-term memory network (Stacked LSTM) was trained to build the mapping model of CT primary current to secondary current. Then, the optimal hyper parameter was obtained through Bayesian optimization. In the online stage, noise reduction was carried out for the secondary current in the half cycle after fault, then the secondary current after noise reduction was input into the off-line model to reconstruct the primary current, which can be used in protection calculation. The simulation results show that the proposed method has good robustness and high reconstruction accuracy for current with interharmonics and noise, and can provide effective data for relay protection.

Key words： Current transformer saturation current reconstruction long short-term memory network Bayesian optimization

Received: 30 June 2022

PACS:

TM452

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	Articles by authors
	Huang Zixin
	Lin Xiangning
	Ma Xiao
	Wu Yuqi
	Wei Fanrong

Cite this article:

Huang Zixin,Lin Xiangning,Ma Xiao等. Reconstruction Method of Saturation Current of Current Transformer in Relay Protection Application Related to Wind Power[J]. Transactions of China Electrotechnical Society, 2022, 37(19): 4823-4834.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.221263 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I19/4823

[1] 戎子睿, 金能, 林湘宁, 等. 基于Hausdorff距离的自适应母线保护新原理[J]. 电网技术, 2021, 45(1): 312-321.
Rong Zirui, Jin Neng, Lin Xiangning, et al.A novel criterion of adaptive busbar protection based on Hausdorff distance algorithm[J]. Power System Technology, 2021, 45(1): 312-321.
[2] 金能, 邢家维, 林湘宁, 等. 一种抗电流互感器饱和的工频变化量保护新方案[J]. 电工技术学报, 2018, 33(增刊1): 213-220.
Jin Neng, Xing Jiawei, Lin Xiangning, et al.A new scheme of frequency variation protection resisting current transformer saturation[J]. Transactions of China Electrotechnical Society, 2018, 33(S1): 213-220.
[3] 杨兴雄, 束洪春, 单节杉, 等. 计及阻容式撬棒动作时间的双馈风机短路电流分析[J]. 电工技术学报, 2021, 36(22): 4716-4725.
Yang Xingxiong, Shu Hongchun, Shan Jieshan, et al.Short circuit current analysis of DFIG considering resistance-capacitance type crowbar protection action time[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4716-4725.
[4] 贾科, 杨哲, 赵其娟, 等. 适用于新能源场站送出线路的高频突变量距离保护[J]. 电网技术, 2019, 43(9): 3271-3279.
Jia Ke, Yang Zhe, Zhao Qijuan, et al.High-frequency fault component based distance protection for transmission lines connected to renewable energy power plants[J]. Power System Technology, 2019, 43(9): 3271-3279.
[5] 贾科, 郑黎明, 毕天姝, 等. 基于余弦相似度的风电场站送出线路纵联保护[J]. 中国电机工程学报, 2019, 39(21): 6263-6274.
Jia Ke, Zheng Liming, Bi Tianshu, et al.Pilot protection based on cosine similarity for transmission line connected to wind farms[J]. Proceedings of the CSEE, 2019, 39(21): 6263-6274.
[6] 郑浩, 林湘宁, 金能, 等. 母线保护TA饱和快速闭锁与再开放判据研究[J]. 中国电机工程学报, 2019, 39(19): 5805-5816, 5908.
Zheng Hao, Lin Xiangning, Jin Neng, et al.Research on TA saturation fast blocking and reopening criterion for bus protection[J]. Proceedings of the CSEE, 2019, 39(19): 5805-5816, 5908.
[7] 邢家维, 金能, 林湘宁, 等. 基于小波包变换的电流互感器饱和识别及有效数据运用策略[J]. 电工技术学报, 2019, 34(6): 1170-1179.
Xing Jiawei, Jin Neng, Lin Xiangning, et al.A novel strategy of current transformer saturation identification and valid data application based on wavelet packet[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1170-1179.
[8] 张弛, 康小宁, 郑永康, 等. 罗氏线圈电流互感器的暂态传变特性[J]. 高电压技术, 2018, 44(12): 4105-4112.
Zhang Chi, Kang Xiaoning, Zheng Yongkang, et al.Transient characteristics of rogowski coil based current transformers[J]. High Voltage Engineering, 2018, 44(12): 4105-4112.
[9] Yang Songhao, Zhang Yubo, Hao Zhiguo, et al.CT saturation detection and compensation: a hybrid physical model-and data-driven method[J]. IEEE Transactions on Power Delivery, 2022, 37(5): 3928-3938.
[10] Naseri F, Kazemi Z, Farjah E, et al.Fast detection and compensation of current transformer saturation using extended Kalman filter[J]. IEEE Transactions on Power Delivery, 2019, 34(3): 1087-1097.
[11] 刘杰, 裴杰, 田明, 等. 基于支持向量机的电流互感器非线性校正方法研究[J]. 电机与控制学报, 2020, 24(10): 130-138.
Liu Jie, Pei Jie, Tian Ming, et al.Research on nonlinear correction method of current transformer based on support vector machine[J]. Electric Machines and Control, 2020, 24(10): 130-138.
[12] Ballal M S, Wath M G, Suryawanshi H M.A novel approach for the error correction of CT in the presence of harmonic distortion[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(10): 4015-4027.
[13] Key S, Kang S H, Lee N H, et al.Bayesian deep neural network to compensate for current transformer saturation[J]. IEEE Access, 9: 154731-154739.
[14] 于永进, 姜雅男, 李长云. 基于鲸鱼优化-长短期记忆网络模型的机-热老化绝缘纸剩余寿命预测方法[J]. 电工技术学报, 2022, 37(12): 3162-3171.
Yu Yongjin, Jiang Yanan, Li Changyun.Prediction method of insulation paper remaining life with mechanical-thermal synergy based on whale optimization algorithm-long-short term memory model[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3162-3171.
[15] 王琛, 王颖, 郑涛, 等. 基于ResNet-LSTM网络和注意力机制的综合能源系统多元负荷预测[J]. 电工技术学报, 2022, 37(7): 1789-1799.
Wang Chen, Wang Ying, Zheng Tao, et al.Multi-energy load forecasting in integrated energy system based on ResNet-LSTM network and attention mechanism[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1789-1799.
[16] 黄凯, 丁恒, 郭永芳, 等. 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测[J]. 电工技术学报, 2022, 37(15): 3753-3766.
Huang Kai, Ding Heng, Guo Yongfang, et al.Prediction of remaining useful life of lithium-ion battery based on adaptive data preprocessing and long short-term memory network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766.
[17] 范小红, 安德超, 孙士云, 等. 计及撬棒保护动作时间的双馈风机三相短路电流特性分析[J]. 电工技术学报, 2019, 34(16): 3444-3452.
Fan Xiaohong, An Dechao, Sun Shiyun, et al.Three-phase short circuit current characteristic analysis of doubly fed induction generator considering crowbar protection action time[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3444-3452.
[18] 贾科, 杨哲, 魏超, 等. 基于斯皮尔曼等级相关系数的新能源送出线路纵联保护[J]. 电力系统自动化, 2020, 44(15): 103-111.
Jia Ke, Yang Zhe, Wei Chao, et al.Pilot protection based on Spearman rank correlation coefficient for transmission line connected to renewable energy source[J]. Automation of Electric Power Systems, 2020, 44(15): 103-111.
[19] 赵洋, 王瀚墨, 康丽, 等. 基于时间卷积网络的短期电力负荷预测[J]. 电工技术学报, 2022, 37(5): 1242-1251.
Zhao Yang, Wang Hanmo, Kang Li, et al.Temporal convolution network-based short-term electrical load forecasting[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1242-1251.
[20] 葛磊蛟, 赵康, 孙永辉, 等. 基于孪生网络和长短时记忆网络结合的配电网短期负荷预测[J]. 电力系统自动化, 2021, 45(23): 41-50.
Ge Leijiao, Zhao Kang, Sun Yonghui, et al.Short-term load forecasting of distribution network based on combination of Siamese network and long short-term memory network[J]. Automation of Electric Power Systems, 2021, 45(23): 41-50.
[21] 刘友波, 吴浩, 刘挺坚, 等. 集成经验模态分解与深度学习的用户侧净负荷预测算法[J]. 电力系统自动化, 2021, 45(24): 57-64.
Liu Youbo, Wu Hao, Liu Tingjian, et al.User-side net load forecasting method integrating empirical mode decomposition and deep learning[J]. Automation of Electric Power Systems, 2021, 45(24): 57-64.
[22] 姜惠兰, 王绍辉, 贾燕琪, 等. 基于定子电流微分前馈控制的双馈异步风力发电机低电压穿越复合控制策略[J]. 高电压技术, 2021, 47(1): 198-204.
Jiang Huilan, Wang Shaohui, Jia Yanqi, et al.Low voltage ride-through compound control strategy of doubly-fed induction generator based on stator current differential feedforward control[J]. High Voltage Engineering, 2021, 47(1): 198-204.
[23] 李东东, 刘宇航, 赵阳, 等. 基于改进生成对抗网络的风机行星齿轮箱故障诊断方法[J]. 中国电机工程学报, 2021, 41(21): 7496-7506.
Li Dongdong, Liu Yuhang, Zhao Yang, et al.Fault diagnosis method of wind turbine planetary gearbox based on improved generative adversarial network[J]. Proceedings of the CSEE, 2021, 41(21): 7496-7506.
[24] 李一泉, 吴梓亮, 王峰, 等. 基于Hausdorff距离算法的自适应线路差动保护方案[J]. 电力自动化设备, 2019, 39(7): 175-180, 187.
Li Yiquan, Wu Ziliang, Wang Feng, et al.Self-adaptive differential protection scheme for transmission line based on Hausdorff distance algorithm[J]. Electric Power Automation Equipment, 2019, 39(7): 175-180, 187.