Dynamic signal recovery in distribution grids using compressive lossy measurements

Gupta, 2000, The capacity of wireless networks, IEEE Trans. Inform. Theory, 46, 388, 10.1109/18.825799 Primadianto, 2016, A review on distribution system state estimation, IEEE Trans. Power Syst., 32, 3875, 10.1109/TPWRS.2016.2632156 Dehghanpour, 2018, A survey on state estimation techniques and challenges in smart distribution systems, IEEE Trans. Smart Grid, 10, 2312, 10.1109/TSG.2018.2870600 Bhela, 2018, Enhancing observability in distribution grids using smart meter data, IEEE Trans. Smart Grid, 9, 5953, 10.1109/TSG.2017.2699939 Wen, 2018, Compression of smart meter big data: A survey, Renew. Sustain. Energy Rev., 91, 59, 10.1016/j.rser.2018.03.088 Alam, 2014, Distribution grid state estimation from compressed measurements, IEEE Trans. Smart Grid, 5, 1631, 10.1109/TSG.2013.2296534 Karimi, 2017, Compressive sensing based state estimation for three phase unbalanced distribution grid, 1 Joshi, 2018, A framework for efficient information aggregation in smart grid, IEEE Trans. Ind. Inf., 15, 2233, 10.1109/TII.2018.2866302 Tripathi, 2018, An efficient data characterization and reduction scheme for smart metering infrastructure, IEEE Trans. Ind. Inf., 14, 4300, 10.1109/TII.2018.2799855 Das, 2020, Neuralcompression: A machine learning approach to compress high frequency measurements in smart grid, Appl. Energy, 257, 10.1016/j.apenergy.2019.113966 Wang, 2019, Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal, Appl. Energy, 251, 10.1016/j.apenergy.2019.05.037 Zhang, 2019 Karimi, 2021, Joint topology identification and state estimation in unobservable distribution grids, IEEE Trans. Smart Grid, 12, 5299, 10.1109/TSG.2021.3102179 R. Madbhavi, H.S. Karimi, B. Natarajan, B. Srinivasan, Tensor Completion based State Estimation in Distribution Systems, in: 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference, ISGT, 2020, pp. 1–5. Zamzam, 2020 Karimi, 2020, Recursive dynamic compressive sensing in smart distribution systems, 1 Dahale, 2020, Sparsity based approaches for distribution grid state estimation-a comparative study, IEEE Access, 8, 198317, 10.1109/ACCESS.2020.3035378 Serban, 2020, Communication requirements in microgrids: A practical survey, IEEE Access, 8, 47694, 10.1109/ACCESS.2020.2977928 Rana, 2017, Distributed state estimation over unreliable communication networks with an application to smart grids, IEEE Trans. Green Commun. Netw., 1, 89, 10.1109/TGCN.2017.2675542 Sharma, 2018, Power system tracking state estimator for smart grid under unreliable PMU data communication network, IEEE Sens. J., 18, 2107, 10.1109/JSEN.2018.2789353 Mohammed, 2019, Modified re-iterated Kalman filter for handling delayed and lost measurements in power system state estimation, IEEE Sens. J., 20, 3946, 10.1109/JSEN.2019.2959663 Švenda, 2020, Flexible hybrid state estimation for power systems with communication irregularities, IET Gener. Trans. Distrib., 14, 2111, 10.1049/iet-gtd.2019.1148 Zheng, 2020, Decentralized state estimation of combined heat and power system considering communication packet loss, J. Mod. Power Syst. Clean Energy, 8, 646, 10.35833/MPCE.2020.000120 Karimi, 2019, Kalman filtered compressive sensing with intermittent observations, Signal Process., 163, 49, 10.1016/j.sigpro.2019.05.004 Candes, 2008, The restricted isometry property and its implications for compressed sensing, C. R. Math., 346, 589, 10.1016/j.crma.2008.03.014 Vaswani, 2008, Kalman filtered compressed sensing, 893 Vaswani, 2016, Recursive recovery of sparse signal sequences from compressive measurements: A review, IEEE Trans. Signal Process., 64, 3523, 10.1109/TSP.2016.2539138 Rana, 2017, Distributed state estimation of smart grids with packet losses, Asian J. Control, 19, 1306, 10.1002/asjc.1578 Lu, 2009, Modified compressive sensing for real-time dynamic MR imaging, 3045 Jiang, 2001, Input-to-state stability for discrete-time nonlinear systems, Automatica, 37, 857, 10.1016/S0005-1098(01)00028-0 Sinopoli, 2004, Kalman filtering with intermittent observations, IEEE Trans. Automat. Control, 49, 1453, 10.1109/TAC.2004.834121 Candès, 2008, An introduction to compressive sampling, IEEE Signal Process. Mag., 25, 21, 10.1109/MSP.2007.914731 Zhao, 2017, A framework for robust hybrid state estimation with unknown measurement noise statistics, IEEE Trans. Ind. Inf., 14, 1866, 10.1109/TII.2017.2764800 Cilden-Guler, 2019, Nanosatellite attitude estimation using Kalman-type filters with non-Gaussian noise, Aerosp. Sci. Technol., 92, 66, 10.1016/j.ast.2019.05.055 Joshi, 2019, Effect of transformation in compressed sensing of smart grid data, 177 2019 Malekpour, 2015, Radial test feeder including primary and secondary distribution network, 1 2018