Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network

Baoqin LI; Junyong WU; Meiyang SHAO; Ruoyu ZHANG; Liangliang HAO

doi:10.7500/AEPS20190528009

您当前的位置：

首页 >

文章列表页 >

Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network

Basic Research | 更新时间：2020-07-14

- Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network
- Automation of Electric Power Systems Issue 6, Pages: 17-26(2020)
- 作者机构：
  
  北京交通大学电气工程学院，北京市 100044
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20190528009
  CLC：
- Received：28 May 2019，
  
  Revised：2019-08-26，
  
  Published Online：03 February 2020，
  
  Published：25 March 2020
- 稿件说明：
移动端阅览
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(06):17-26.

LI Baoqin,WU Junyong,SHAO Meiyang,et al.Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network[J].Automation of Electric Power Systems,2020,44(06):17-26.
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(06):17-26. DOI： 10.7500/AEPS20190528009.

LI Baoqin,WU Junyong,SHAO Meiyang,et al.Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network[J].Automation of Electric Power Systems,2020,44(06):17-26. DOI： 10.7500/AEPS20190528009.

摘要

为了进一步提高电力系统暂态稳定的预测精度及给出更精细化的评估结果，将深度学习与电力系统暂态稳定相结合，根据故障切除后发电机功角“轨迹簇”特征，提出一种基于集成不同结构的深度置信网络（DBN）的精细化电力系统暂态稳定评估模型。该模型的基分类器DBN能够有效地利用深层架构所具有的特征提取能力，充分挖掘出输入特征与暂态稳定评估结果之间的非线性映射关系。在新英格兰10机39节点系统上的实验结果表明，该方法不仅优于浅层学习框架，也比部分深度学习模型的性能更加优越。除此之外，该集成DBN算法不仅有较高的预测精度，而且可以有效地评估系统的稳定裕度和不稳定程度等级；在部分同步相量测量装置信息缺失以及含有噪声时，表现出较强的鲁棒性。

Abstract

In order to further improve the prediction accuracy of transient stability for power system and give more refined evaluation results

the deep learning is combined with the transient stability of power system. A refined evaluation model of transient stability for power system based on ensemble deep belief network (DBN) with different structures is proposed based on the characteristics of the generator power angle "trajectory cluster" after fault removal. The base classifier DBN of the model can effectively utilize the feature extraction ability of the deep architecture and fully exploit the nonlinear mapping relationship between the input features and the evaluation results of transient stability. Experimental results on the New England 10-machine 39-node system show that this method is not only superior to the shallow learning framework

but also superior to the partial deep learning model. In addition

the ensemble DBN algorithm not only has higher prediction accuracy

but also can effectively evaluate the stability margin and instability level of the system. It shows strong robustness when some information of phasor measurement unit (PMU) is missing and contains noise.

关键词

Keywords

references

IEEE/CIGRE Joint Task Force on Stability Terms and Definitions . Definition and classification of power system stability [J]． IEEE Transactions on Power Systems ， 2004 ， 19 ( 2 )： 1387 - 1401 .

郭志忠 , 柳焯 . 快速高阶Taylor级数法暂态稳定计算 [J]. 中国电机工程学报 , 1991 , 11 ( 3 ): 8 - 16 .

GUO Zhizhong , LIU Zhuo . Fast transient stability simulation by higher order Taylor series expansions [J]. Proceedings of the CSEE , 1991 , 11 ( 3 ): 8 - 16 .

吴红斌 , 丁明 . 用于电力系统暂态稳定仿真的可变步长牛顿法 [J]. 中国电机工程学报 , 2010 , 30 ( 7 ): 36 - 41 .

WU Hongbin , DING Ming . Newton method with variable step size for power system transient stability simulation [J]. Proceedings of the CSEE , 2010 , 30 ( 7 ): 36 - 41 .

SIDDIQEE M W . Transient stability of an a.c. generator by Lyapunov's direct method [J]. International Journal of Control , 1968 , 8 ( 2 ): 131 - 144 .

XUE Y , VAN CUTSEM T , RIBBENS-PAVELLA M . A simple direct method for fast transient stability assessment of large power systems [J]. IEEE Transactions on Power Systems , 1988 , 3 ( 2 ): 400 - 412 .

XUE Y , VAN CUSTEM T , RIBBENS-PAVELLA M . Extended equal area criterion justifications, generalizations, applications [J]. IEEE Transactions on Power Systems , 1989 , 4 ( 1 ): 44 - 52 .

LITVINOV E . Real-time stability in power systems: techniques for early detection of the risk of blackout [J]. IEEE Power and Energy Magazine , 2006 , 4 ( 3 ): 68 - 70 .

付川 , 余贻鑫 , 王东涛 . 电力系统暂态稳定概率 [J]. 电力系统自动化 , 2006 , 30 ( 1 ): 24 - 28 .

FU Chuan , YU Yixin , WANG Dongtao . Transient stability probability of power system [J]. Automation of Electric Power Systems , 2006 , 30 ( 1 ): 24 - 28 .

姜涛 , 王长江 , 陈厚合 , 等 . 基于正则化投影孪生支持向量机的电力系统暂态稳定评估 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 141 - 148 . DOI: 10.7500/AEPS20180601005 http://dx.doi.org/10.7500/AEPS20180601005 .

JIANG Tao , WANG Changjiang , CHEN Houhe , et al . Transient stability assessment of power system based on projection twin support vector machine with regularization [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 141 - 148 . DOI: 10.7500/AEPS20180601005 http://dx.doi.org/10.7500/AEPS20180601005 .

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 .

KAMWA I , SAMANTARAY S R , JOOS G . Catastrophe predictors from ensemble decision-tree learning of wide-area severity indices [J]. IEEE Transactions on Smart Grid , 2010 , 1 ( 2 ): 144 - 158 .

叶圣永 , 王晓茹 , 刘志刚 , 等 . 基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估 [J]. 中国电机工程学报 , 2011 , 31 ( 1 ): 46 - 51 .

YE Shengyong , WANG Xiaoru , LIU Zhigang , et al . Power system transient stability assessment based on severely disturbed generator attributes and machine learning method [J]. Proceedings of the CSEE , 2011 , 31 ( 1 ): 46 - 51 .

BENGIO Y . Learning deep architectures for AI [J]. Foundations and Trends in Machine Learning , 2009 , 2 ( 1 ): 1 - 127 .

周悦 , 谭本东 , 李淼 , 等 . 基于深度学习的电力系统暂态稳定评估方法 [J]. 电力建设 , 2018 , 39 ( 2 ): 103 - 108 .

ZHOU Yue , TAN Bendong , LI Miao , et al . Transient stability assessment of power system based on deep learning technology [J]. Electric Power Construction , 2018 , 39 ( 2 ): 103 - 108 .

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 .

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 System Technology , 2017 , 41 ( 10 ): 3140 - 3146 .

姚德全 , 贾宏杰 , 赵帅 . 基于复合神经网络的电力系统暂态稳定评估和裕度预测 [J]. 电力系统自动化 , 2013 , 37 ( 20 ): 41 - 46 .

YAO Dequan , JIA Hongjie , ZHAO Shuai . Power system transient stability assessment and stability margin prediction based on compound neural network [J]. Automation of Electric Power Systems , 2013 , 37 ( 20 ): 41 - 46 .

史鹤欢 , 许悦雷 , 杨志军 , 等 . 基于深度置信网络的目标识别方法 [J]. 计算机应用 , 2014 , 34 ( 11 ): 3314 - 3317 .

SHI Hehuan , XU Yuelei , YANG Zhijun , et al . Target recognition method based on deep belief network [J]. Journal of Computer Applications , 2014 , 34 ( 11 ): 3314 - 3317 .

HINTON G E . A practical guide to training restricted Boltzmann machines [M]// BELLARE M, NAMPREMPRE C, NEVEN G. Lecture Notes in Computer Science . Berlin, Heidelberg : Springer Berlin Heidelberg , 2012 : 599 - 619 .

CHEN E K , YANG X K , ZHA H Y , et al . Learning object classes from image thumbnails through deep neural networks [C]// 2008 IEEE International Conference on Acoustics , Speech and Signal Processing, March 31-April 4 , 2008 , Las Vegas, USA.

HINTON G E . Reducing the dimensionality of data with neural networks [J]. Science , 2006 , 313 : 504 - 507 .

赵小强 , 宋昭漾 . Adam优化的CNN超分辨率重建 [J]. 计算机科学与探索 , 2019 , 13 ( 5 ): 858 - 865 .

ZHAO Xiaoqiang , SONG Zhaoyang . Adam optimized CNN super-resolution reconstruction [J]. Journal of Frontiers of Computer Science and Technology , 2019 , 13 ( 5 ): 858 - 865 .

冀鲁豫 , 吴俊勇 , 周艳真 , 等 . 基于WAMS受扰电压轨迹簇特征的电力系统暂态稳定性预测 [J]. 高电压技术 , 2015 , 41 ( 3 ): 807 - 814 .

JI Luyu , WU Junyong , ZHOU Yanzhen , et al . Transient stability prediction of power system based on WAMS characteristic of perturbed voltage trajectory clusters [J]. High Voltage Engineering , 2015 , 41 ( 3 ): 807 - 814 .

SU M C , LIU C W , TSAY S S . Neural-network-based fuzzy model and its application to transient stability prediction in power systems [J]. IEEE Transactions on Systems , Man and Cybernetics: Part C (Applications and Reviews) , 1999 , 29 ( 1 ): 149 - 157 .

陈明华 , 刘群英 , 张家枢，等 . 基于XGBoost的电力系统暂态稳定预测方法研究 [J/OL]. 电网技术 [ 2019-05-24 ]. https://doi.org/10.13335/j.1000-3673.pst.2018.1649 https://doi.org/10.13335/j.1000-3673.pst.2018.1649 .

CHEN Minghua , LIU Qunying , ZHANG Jiashu , et al . Research on transient stability prediction method of power system based on XGBoost [J/OL]. Power System Technology [ 2019-05-24 ]. https://doi.org/10.13335/j.1000-3673.pst.2018.1649 https://doi.org/10.13335/j.1000-3673.pst.2018.1649 .

任进军 , 王宁 . 人工神经网络中损失函数的研究 [J]. 甘肃高师学报 , 2018 , 23 ( 2 ): 61 - 63 .

REN Jinjun , WANG Ning . Research on cost function in artificial neural network [J]. Journal of Gansu Normal Colleges , 2018 , 23 ( 2 ): 61 - 63 .

LUSTED L B . Logical analysis in roentgen diagnosis [J]. Radiology , 1960 , 74 ( 2 ): 178 - 193 .

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 .

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 .

刘友波 , 刘俊勇 , Gareth Taylor , 等 . 面向同步相量轨迹簇规则的电力系统暂态稳定实时评估 [J]. 中国电机工程学报 , 2011 , 31 ( 16 ): 32 - 39 .

LIU Youbo , LIU Junyong , Gareth Taylor , et al . Rule-based combined algorithm for power system real-time transient stability assessment using synchronized phasor trajectory clusters [J]. Proceedings of the CSEE , 2011 , 31 ( 16 ): 32 - 39 .

PADIYAR K R . Energy function analysis for power system stability [J]. Electric Machines & Power Systems , 1990 , 18 ( 2 ): 209 - 210 .

CHOW J H , CHEUNG K W . A toolbox for power system dynamics and control engineering education and research [J]. IEEE Transactions on Power Systems , 1992 , 7 ( 4 ): 1559 - 1564 .

ZHOU Y Z , GUO Q L , SUN H B , et al . A novel data-driven approach for transient stability prediction of power systems considering the operational variability [J]. International Journal of Electrical Power & Energy Systems , 2019 , 107 : 379 - 394 .

周艳真 , 吴俊勇 , 于之虹 , 等 . 基于转子角轨迹簇特征的电力系统暂态稳定评估 [J]. 电网技术 , 2016 , 40 ( 5 ): 1482 - 1487 .

ZHOU Yanzhen , WU Junyong , YU Zhihong , et al . Power system transient stability assessment based on cluster features of rotor angle trajectories [J]. Power System Technology , 2016 , 40 ( 5 ): 1482 - 1487 .

HU W , LU Z X , WU S , et al . Real-time transient stability assessment in power system based on improved SVM [J]. Journal of Modern Power Systems and Clean Energy , 2019 , 7 ( 1 ): 26 - 37 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning

Transient Stability Assessment of Power Systems Based on Multi-scale Graph Attention Network

Review and Prospect of Explainable Artificial Intelligence and Its Application in Power Systems

Multi-task Transient Stability Assessment Based on Feature-level Fusion of Heterogeneous Data

Related Author

LI Baoqin

WU Junyong

LI Lusu

SHI Fashun

ZHAO Pengjie

WANG Yi

FU Taiguoyi

DU Youtian

Related Institution

School of Automation Science and Engineering, Xi’an Jiaotong University

National Key Laboratory of Grid Security (China Electric Power Research Institute)

School of Electrical Engineering, Xi’an Jiaotong University

School of Electrical Engineering, Xi'’an Jiaotong University

China Electric Power Research Institute

⁰