Real-Time Power System State Estimation and Forecasting via Deep Unrolled Neural Networks
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[1] Lang Tong,et al. State Estimation for Unobservable Distribution Systems via Deep Neural Networks , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).
[2] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[3] Georgios B. Giannakis,et al. Monitoring and Optimization for Power Grids: A Signal Processing Perspective , 2013, IEEE Signal Processing Magazine.
[4] Jian Sun,et al. Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.
[5] Gang Wang,et al. PSSE Redux: Convex Relaxation, Decentralized, Robust, and Dynamic Approaches , 2017, ArXiv.
[6] B. Ripley,et al. Robust Statistics , 2018, Wiley Series in Probability and Statistics.
[7] A. P. A. D. Silva,et al. State forecasting in electric power systems , 1983 .
[8] Gang Wang,et al. REAL-TIME POWER SYSTEM STATE ESTIMATION VIA DEEP UNROLLED NEURAL NETWORKS , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[9] Gang Wang,et al. Distribution system state estimation: an overview of recent developments , 2019, Frontiers of Information Technology & Electronic Engineering.
[10] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Gang Wang,et al. Power system state estimation via feasible point pursuit , 2017, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[12] M. Hassanzadeh,et al. Power system state forecasting using regression analysis , 2012, 2012 IEEE Power and Energy Society General Meeting.
[13] Ross Baldick,et al. Voltage regulation algorithms for multiphase power distribution grids , 2015, 2016 IEEE Power and Energy Society General Meeting (PESGM).
[14] R D Zimmerman,et al. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.
[15] Zhao Yang Dong,et al. Robust Forecasting Aided Power System State Estimation Considering State Correlations , 2018, IEEE Transactions on Smart Grid.
[16] Gang Wang,et al. Robust and Scalable Power System State Estimation via Composite Optimization , 2017, IEEE Transactions on Smart Grid.
[17] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..
[18] Michael C. Ferris,et al. A Gauss—Newton method for convex composite optimization , 1995, Math. Program..
[19] Gang Wang,et al. Moving-horizon dynamic power system state estimation using semidefinite relaxation , 2013, 2014 IEEE PES General Meeting | Conference & Exposition.
[20] J.C.S. de Souza,et al. Forecasting-Aided State Estimation—Part I: Panorama , 2009, IEEE Transactions on Power Systems.
[21] Kaveh Dehghanpour,et al. A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems , 2018, IEEE Transactions on Smart Grid.
[22] H. Vincent Poor,et al. A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification , 2017, IEEE Transactions on Smart Grid.
[23] A. Abur,et al. A fast algorithm for the weighted least absolute value state estimation (for power systems) , 1991 .
[24] Hao Zhu,et al. Robust Power System State Estimation From Rank-One Measurements , 2019, IEEE Transactions on Control of Network Systems.
[25] F. J. Soares,et al. State estimation in distribution smart grids using autoencoders , 2014, 2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014).
[26] Qing Ling,et al. Learning Deep $\ell_0$ Encoders , 2015, 1509.00153.
[27] G. Sheblé,et al. Power generation operation and control — 2nd edition , 1996 .
[28] Nikos D. Sidiropoulos,et al. Learning to optimize: Training deep neural networks for wireless resource management , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
[29] Alexandre M. Tartakovsky,et al. Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-Correlated Mechanical Input Power , 2018, IEEE Transactions on Power Systems.
[30] Gang Wang,et al. Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization , 2018, IEEE Transactions on Signal Processing.
[31] Stefanie Jegelka,et al. ResNet with one-neuron hidden layers is a Universal Approximator , 2018, NeurIPS.
[32] Georgios B. Giannakis,et al. Scalable Electric Vehicle Charging Protocols , 2015, IEEE Transactions on Power Systems.
[33] Bikash C. Pal,et al. Iteratively reweighted least-squares implementation of the WLAV state-estimation method , 2004 .
[34] Ahmed S. Zamzam,et al. Data-Driven Learning-Based Optimization for Distribution System State Estimation , 2018, IEEE Transactions on Power Systems.
[35] A. G. Expósito,et al. Power system state estimation : theory and implementation , 2004 .
[36] J.C.S. de Souza,et al. Forecasting-Aided State Estimation—Part II: Implementation , 2009, IEEE Transactions on Power Systems.
[37] Ami Wiesel,et al. Deep MIMO detection , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
[38] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[39] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[40] Lamine Mili,et al. A Short-Term Nodal Voltage Phasor Forecasting Method Using Temporal and Spatial Correlation , 2016, IEEE Transactions on Power Systems.
[41] Georgios B. Giannakis,et al. Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics , 2018, Proceedings of the IEEE.
[42] N. Sidiropoulos,et al. Learning to Optimize: Training Deep Neural Networks for Interference Management , 2017, IEEE Transactions on Signal Processing.
[43] Nikos D. Sidiropoulos,et al. Physics-Aware Neural Networks for Distribution System State Estimation , 2019, IEEE Transactions on Power Systems.
[44] Gang Wang,et al. Going beyond linear dependencies to unveil connectivity of meshed grids , 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).
[45] Razvan Pascanu,et al. How to Construct Deep Recurrent Neural Networks , 2013, ICLR.
[46] Atif S. Debs,et al. A Dynamic Estimator for Tracking the State of a Power System , 1970 .