基于改进深度残差收缩网络的电力系统暂态稳定评估

doi:10.19595/j.cnki.1000-6753.tces.200437

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1487 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In power system transient stability assessment, the measurement data of power system synchronous phasor measurement unit (PMU) may exist noise problems during acquisition and transmission process, and the transient stability and instability samples are imbalanced, resulting in the tendency of data-driven transient stability assessment model training and serious misjudgment problems. This paper proposes a power system transient stability assessment method based on improved deep residual shrinkage networks (IDRSN). First, the bottom-level measured electrical quantity is constructed as a feature map as the input of model, and the deep structure of model is used to establish the mapping relationship between the input and the stable result. Faced with noise problems, the model uses the attention mechanism to automatically learn the noise threshold through a soft threshold function to reduce noise and irrelevant feature interference; and through focus loss function (FL), the weight coefficient is introduced to correct the tendency of model training. Modulation factors is used to focus on misclassified samples to improve model training efficiency and evaluation performance. Through the simulation verification of the New England 10-machine 39-node system, the proposed model can effectively reduce the noise interference of different degrees, correct the bias of the model training on the imbalanced data set, and reduce the misclassified samples. Under different PMU configuration schemes, all are obtained better evaluation effect.

Key words： Power system transient stability assessment deep learning deep residual shrinkage network focal loss

Received: 05 May 2020

PACS:

TM712

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Lu Jinling
	Guo Luyu

Cite this article:

Lu Jinling,Guo Luyu. Power System Transient Stability Assessment Based on Improved Deep Residual Shrinkage Network[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2233-2244.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200437 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I11/2233

[1] 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): 4-15, 317.
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): 4-15, 317.
[2] 蒋海峰, 张曼, 赵斌炎,等. 基于改进Hilbert-Huang变换的电网故障诊断[J]. 电工技术学报, 2019, 34(增刊1):336-342, 351.
Jiang Haifeng, Zhang Man, Zhao Binyan, et al.Fault diagnosis of power grid based on improved Hilbert-Huangtransform[J]. Transactionsof China Electrote-chnical Society, 2019, 34(S1):336-342, 351.
[3] 倪以信, 陈寿孙, 张宝霖. 动态电力系统的理论和分析[M]. 北京:清华大学出版社,2002.
[4] 闵勇, 陈磊, 姜齐荣. 电力系统稳定分析[M]. 北京:清华大学出版社,2016.
[5] 陈厚合, 王长江, 姜涛, 等. 基于端口能量的含VSC-HVDC的交直流混合系统暂态稳定评估[J]. 电工技术学报, 2018, 33(3):498-511.
Chen Houhe, Wang Changjiang, Jiang Tao, et al.Transient stability assessment in hybrid AC/DC systems with VSC-HVDC via port energy[J]. Transactions of China Electrotechnical Society, 2018, 33(3):498-511.
[6] 刘艳芳, 顾雪平. 一种用于半监督BP算法的实用结束判据及其应用[J]. 电力系统自动化, 2003, 27(14):41-44.
Liu Yanfang, GuXueping. Ausefulending-criterionofsemi-supervised BP algorithmanditsapplication[J]. Automation of Electric Power Systems, 2003, 27(14): 41-44.
[7] Almasri A N, Kadir M Z, Hizam H, et al.A novel implementation for generator rotor angle stability prediction using an adaptive artificial neural network application for dynamic security assessment[J]. IEEE Transactions on Power Systems, 2013, 28(3): 2516-2525.
[8] 顾雪平, 李扬, 吴献吉. 基于局部学习机和细菌群体趋药性算法的电力系统暂态稳定评估[J]. 电工技术学报, 2013, 28(10): 277-285.
GuXueping, Li Yang, Wu Xianji. Transient stability assessment of power systems based on local learning machine and bacterial colony chemotaxisalgorithm[J]. Transactions of China Electrotechnical Society, 2013, 28(10): 277-285.
[9] 卢锦玲, 於慧敏. 极限学习机和遗传算法在暂态稳定评估特征选择中的应用[J]. 电力系统及其自动化学报, 2016, 28(12): 107-112.
Lu Jinling, Yu Huimin.Applicationof extreme learning machine and genetic algorithm to feature selection of transient stability assessment[J]. Proceedings of the CSU-EPSA, 2016, 28(12): 107-112.
[10] Gomez F, Rajapakse A D, Annakkage U D, et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1474-1483.
[11] 霍思敏, 王科, 陈震海, 等. 基于轨迹输入特征支持向量机的湖南电网暂态稳定在线识别[J]. 电力系统保护与控制, 2012, 40(18): 19-23, 29.
HuoSimin, Wang Ke, Chen Zhenhai, et al. Hunan power grid transient stability online detection based on support vector machine with trajectory input features[J]. Power System Protection and Control, 2012, 40(18): 19-23, 29.
[12] 邵雅宁, 唐飞, 刘涤尘, 等. 一种适用于WAMS量测数据的系统暂态功角稳定评估方法[J]. 电力系统保护与控制, 2015, 43(6): 33-39.
Shao Yaning, Tang Fei, Liu Dichen, et al.An approach of transient angle stability assessment in power system for WAMS measured data[J]. Power System Protection and Control, 2015, 43(6): 33-39.
[13] Wang Bo, Fang Biwu, Wang Yajun, et al.Power system transient stability assessment based on big data and the core vector machine[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2561-2570.
[14] 陈厚合, 王长江, 姜涛, 等. 基于投影能量函数和Pin-SVM的电力系统暂态稳定评估[J]. 电工技术学报, 2017, 32(11): 71-80.
Chen Houhe, Wang Changjiang, Jiang Tao, et al.Transient stability assessment in bulk power grid using projection energy function and support vector machine with Pinball loss[J]. Transactions of China Electrotechnical Society, 2017, 32(11): 71-80.
[15] 田芳, 周孝信, 于之虹. 基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J]. 电力系统保护与控制, 2017, 45(22): 6-13.
Tian Fang, Zhou Xiaoxin, Yu Zhihong.Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J]. Power System Protection and Control, 2017, 45(22): 6-13.
[16] Amraee T, Ranjbar S.Transient instability prediction using decision tree technique[J]. IEEE Transactions on Power Systems, 2013, 28(3):3028-3037.
[17] 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): 122-132.
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): 122-132.
[18] HeMiao, Zhang Junshan, Vittal V. Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J]. IEEE Transactions on Power Systems, 2013, 28(4):4089-4098.
[19] Chen Minghua, Liu Qunying, Chen Shuheng, et al.XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system[J]. IEEE Access, 2019: 13149-13158.
[20] 张晨宇, 王慧芳, 叶晓君. 基于XGBoost算法的电力系统暂态稳定评估[J]. 电力自动化设备, 2019, 39(3): 83-89, 95.
Zhang Chenyu, Wang Huifang, Ye Xiaojun.Transient stability assessment of power system based on XGBoostalgorithm[J]. Electric Power Automation Equipment, 2019, 39(3): 83-89, 95.
[21] 周挺,杨军, 周强明, 等. 基于改进LightGBM的电力系统暂态稳定评估方法[J]. 电网技术, 2019, 43(6): 81-90.
Zhou Ting, Yang Jun, Zhou Qiangming, et al.Power system transient stability assessment based on modified LightGBM[J]. Power System Technology, 2019, 43(6): 81-90.
[22] 李兵洋, 肖健梅, 王锡淮. 融合邻域粗糙约简与深度森林的电力系统暂态稳定评估[J]. 电工技术学报, 2020, 35(15): 3245-3257.
Li Bingyang, Xiao Jianmei, Wang Xihuai.Power system transient stability assessment based on hybrid neighborhood rough reduction and deep forest[J]. Transactions of China Electrotechnical Society,2020, 35(15): 3245-3257.
[23] 徐春华, 陈克绪, 马建, 等. 基于深度置信网络的电力负荷识别[J]. 电工技术学报, 2019, 34(19):4135-4142.
XuChunhua, Chen Kexu, Ma Jian, et al. Recognition of power loads based on deep belief network[J]. Transactions of China Electrotechnical Society, 2019, 34(19):4135-4142.
[24] 张倩, 王建平, 李帷韬. 基于反馈机制的卷积神经网络绝缘子状态检测方法[J]. 电工技术学报, 2019, 34(16):3311-3321.
Zhang Qian, Wang Jianping, Li Weitao.Insulator state detection of convolutional neural networks based on feedback mechanism[J]. Transactions of China Electrotechnical Society, 2019, 34(16):3311-3321.
[25] 周念成, 廖建权, 王强钢, 等. 深度学习在智能电网中的应用现状分析与展望[J]. 电力系统自动化, 2019, 43(4): 180-197.
Zhou Niancheng, Liao Jianquan, Wang Qianggang, et al.Analysis and prospect of deep learning application in smart grid[J]. Automation of Electric Power Systems, 2019, 43(4): 180-197.
[26] 胡伟, 郑乐, 闵勇, 等. 基于深度学习的电力系统故障后暂态稳定评估研究[J]. 电网技术, 2017, 41(10): 3140-3146.
Hu Wei, Zheng Le, Min Yong, et al.Research on power system transient stability assessment based on deep learning of big data technique[J]. Power Grid Technology, 2017, 41(10): 3140-3146.
[27] 朱乔木, 党杰, 陈金富, 等. 基于深度置信网络的电力系统暂态稳定评估方法[J]. 中国电机工程学报, 2018, 38(3): 735-743.
Zhu Qiaomu, Dang Jie, Chen Jinfu, et al.A method for power system transient stability assessment based on deep belief networks[J]. Proceedings of the CSEE, 2018, 38(3): 735-743.
[28] 李宝琴, 吴俊勇, 邵美阳,等. 基于集成深度置信网络的精细化电力系统暂态稳定评估[J]. 电力系统自动化, 2020, 44(6):17-28.
Li Baoqin, Wu Junyong, Shao Meiyang, et al.Refined transient stability evaluationfor power system based on ensemble deep beliefnetwork[J]. AutomationofElectricPowerSystems, 2020, 44(6):17-28.
[29] 杨维全, 朱元振, 刘玉田. 基于卷积神经网络的暂态电压稳定快速评估[J]. 电力系统自动化, 2019, 43(22):46-52, 139.
Yang Weiquan, Zhu Yuanzhen, Liu Yutian.Fast assessment of transient voltage stability based on convolutional neural network[J]. Automation of Electric Power Systems, 2019, 43(22):46-52, 139.
[30] 高昆仑, 杨帅, 刘思言, 等. 基于一维卷积神经网络的电力系统暂态稳定评估[J]. 电力系统自动化, 2019, 43(12): 30-38.
Gao Kunlun, Yang Shuai, Liu Siyan, et al.Transient stability assessmentfor power system based on one-dimensional convolutional neural network[J]. Automation of Electric Power Systems, 2019, 43(12): 30-38.
[31] Yan Rong, GengGuangchao, Jiang Quanyuan, et al. Fast transient stability batch assessment using cascaded convolutional neural networks[J]. IEEE Transactions on Power Systems, 2019, 34(4): 2802-2813.
[32] Shi Zhongtuo, Yao Wei, Zeng Lingkang, et al.Convolutional neural network-based power system transient stability assessment and instability mode prediction[J]. Applied Energy, 2020,263: 114586.
[33] Zhu Qiaomu, Chen Jinfu, Zhu Lin, et al.A deep end-to-end model for transient stability assessment with PMUdata[J]. IEEE Access, 2018, 6:65474-65487.
[34] 朱乔木, 陈金富, 李弘毅, 等. 基于堆叠自动编码器的电力系统暂态稳定评估[J]. 中国电机工程学报, 2018, 38(10): 2937-2946, 3144.
Zhu Qiaomu, Chen Jinfu, Li Hongyi, et al.Transient stability assessment based on stacked autoencoder[J]. Proceedings of the CSEE, 2018, 38(10): 2937-2946, 3144.
[35] 王怀远, 陈启凡. 基于代价敏感堆叠变分自动编码器的暂态稳定评估方法[J]. 中国电机工程学报, 2020, 40(7):2213-2220, 2400.
Wang Huaiyuan, Chen Qifan.A transient stability assessment method based on cost-sensitive stacked variational auto-encoder[J]. Proceedings of the CSEE, 2020, 40(7): 2213-2220, 2400.
[36] 谭本东, 杨军, 赖秋频, 等. 基于改进CGAN的电力系统暂态稳定评估样本增强方法[J]. 电力系统自动化, 2019, 43(1):203-214.
Tan Bendong, Yang Jun, Lai Qiupin, et al.Data augment method for power system transient stability assessment based on improved conditional generative adversarial network[J]. Automation of Electric Power Systems, 2019, 43(1):203-214.
[37] Zhao Minghang, ZhongShisheng, Fu Xuyun, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4681-4690.
[38] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society, Las Vegas, USA, 2016: 770-778.
[39] Lin Tsungyi, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017, Venice, Italy, 2999-3007.
[40] Shelhamer E, Long J, Darrell T, et al.Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.
[41] IoffeS, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//international conference on machine learning, Lile, France, 2015: 448-456.
[42] Martin K E, Hamai D, Adamiak M, et al.Exploring the IEEE Standard C37. 118-2005 synchrophasors for power systems[J]. IEEE Transactions on Power Delivery, 2008, 23(4): 1805-1811.
[43] KeGuolin, Meng Qi, Finley T W, et al. LightGBM: a highly efficient gradient boosting decision tree[C]//Advances in Neural Information Processing Systems, Long Beach, USA, 2017:3147-3155.