Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China
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Peifeng Shen | Zhinong Wei | Guoqiang Sun | Haixiang Zang | Yi Liu | Ziyu Bai | Ming Zhang | Zhi-nong Wei | Guo-qiang Sun | Haixiang Zang | Peifeng Shen | Zi-Jun Bai | Ming Zhang | Yi Liu
[1] Jinfu Chen,et al. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach , 2018 .
[2] Tao Ding,et al. Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China , 2019, Renewable Energy.
[3] C. L. Philip Chen,et al. Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting , 2015, IEEE Transactions on Sustainable Energy.
[4] Bo-Sheng Lin,et al. The establishment of human-computer interaction based on Word2Vec , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).
[5] S. S. Venkata,et al. A fuzzy expert system for the integrated fault diagnosis , 2000 .
[6] Ahmed Patel,et al. A dominance based rough set classification system for fault diagnosis in electrical smart grid environments , 2016, Artificial Intelligence Review.
[7] Li-Ping Jing,et al. Improved feature selection approach TFIDF in text mining , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.
[8] Hua Fan,et al. Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information , 2018, Energies.
[9] Tao Ding,et al. Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach , 2018, Energies.
[10] S. Asha Kiranmai,et al. Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy , 2018, Protection and Control of Modern Power Systems.
[11] Aldo Dagnino,et al. An initial study of predictive machine learning analytics on large volumes of historical data for power system applications , 2014, 2014 IEEE International Conference on Big Data (Big Data).
[12] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[13] Julio Cesar Stacchini de Souza,et al. A hybrid intelligent system for alarm processing in power distribution substations , 2010, Int. J. Hybrid Intell. Syst..
[14] Geoffrey E. Hinton,et al. Learning distributed representations of concepts. , 1989 .
[15] Martin Wattenberg,et al. Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow , 2018, IEEE Transactions on Visualization and Computer Graphics.
[16] Qi Wang,et al. Fault diagnosis model based on Bayesian network considering information uncertainty and its application in traction power supply system , 2018 .
[17] H.H. Zurn,et al. Application of neural-network modules to electric power system fault section estimation , 2004, IEEE Transactions on Power Delivery.
[18] Qiang Zhang,et al. Research on practical power system stability analysis algorithm based on modified SVM , 2018 .
[19] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[20] Christopher D. Manning,et al. Advances in natural language processing , 2015, Science.
[21] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[22] Hermann Ney,et al. From Feedforward to Recurrent LSTM Neural Networks for Language Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[23] Xianggen Yin,et al. An Analytic Method for Power System Fault Diagnosis Employing Topology Description , 2019, Energies.
[24] Diogo E. Aguiam,et al. Estimation of X-Mode Reflectometry First Fringe Frequency Using Neural Networks , 2018, IEEE Transactions on Plasma Science.
[25] P.A. Crossley,et al. Building Knowledge for Substation-Based Decision Support Using Rough Sets , 2007, IEEE Transactions on Power Delivery.
[26] Jianhui Wang,et al. Data-Driven Power Outage Detection by Social Sensors , 2016, IEEE Transactions on Smart Grid.
[27] Pandian Vasant,et al. Rough Set-Based Text Mining from a Large Data Repository of Experts' Diagnoses for Power Systems , 2017, KES-IDT.
[28] Wen-Hui Chen,et al. Online Fault Diagnosis for Power Transmission Networks Using Fuzzy Digraph Models , 2012, IEEE Transactions on Power Delivery.
[29] Sihua Liu,et al. Power System Fault Diagnosis Based on protection coordination and Petri net theory , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.
[30] Yuan Liao,et al. Wide area measurements based fault detection and location method for transmission lines , 2019 .
[31] Chao Hu,et al. A Classification Model of Power Equipment Defect Texts Based on Convolutional Neural Network , 2019, ICAIS.
[32] M. Kezunovic,et al. Implementing Fuzzy Reasoning Petri-Nets for Fault Section Estimation , 2008, IEEE Transactions on Power Delivery.
[33] Dongsheng Guo,et al. Novel Recurrent Neural Network for Time-Varying Problems Solving [Research Frontier] , 2012, IEEE Computational Intelligence Magazine.
[34] Fang Wang,et al. Bayesian network approach based on fault isolation for power system fault diagnosis , 2014, 2014 International Conference on Power System Technology.
[35] Ali Emadi,et al. Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.
[36] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[37] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[38] Ashok Kumar Pradhan,et al. Online identification of protection element failure using wide area measurements , 2015 .
[39] Tao Ding,et al. Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.
[40] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[41] Zhu Yongli,et al. Bayesian networks-based approach for power systems fault diagnosis , 2006, IEEE Transactions on Power Delivery.
[42] Emilio García Moreno,et al. Fault Diagnosis of Electric Transmission Lines using Modular Neural Networks , 2016 .
[43] A. Jaya Laxmi,et al. Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy , 2018, Protection and Control of Modern Power Systems.
[44] Shuai Yue,et al. Method of power grid fault diagnosis using intuitionistic fuzzy Petri nets , 2017 .
[45] Kwang-Ho Kim,et al. Adaptive fault section estimation using matrix representation with fuzzy relations , 2004 .
[46] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[47] Gerard Ledwich,et al. An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers , 2010, IEEE Transactions on Power Delivery.
[48] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[49] Yuan Zhang,et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.
[50] A. Abur,et al. Optimal Deployment of Wide-Area Synchronized Measurements for Fault-Location Observability , 2013, IEEE Transactions on Power Systems.
[51] Young-Moon Park,et al. A fault diagnosis expert system for distribution substations , 2000 .