Sequential Bayesian parameter estimation of stochastic dynamic load models

Electric Power Systems Research - Tập 189 - Trang 106606 - 2020
Daniel Adrian Maldonado1, Vishwas Rao1, Mihai Anitescu1, Vivak Patel2
1Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
2Department of Statistics The University of Wisconsin-Madison Madison, WI, USA

Tài liệu tham khảo

2017, Reliability Guideline: Developing Load Model Composition Data 2018, The new aggregated distributed energy resources (der_a) model for transmission planning studies Ramasubramanian, 2019, Ability of positive sequence aggregated distributed energy resource model to represent unbalanced tripping of distribution inverters J. Undrill, Tripping, 2016, https://www.nerc.com/comm/PC/LoadModelingTaskForceDL/progmod.pdf. Accessed: 2019-09-30. J. Weber, Progressive tripping and reconnecting block, 2017, https://www.nerc.com/comm/PC/LoadModelingTaskForceDL/Presentation_of_PowerWorld_Progressive_Tripping_and_Reconnecting.pdf. Accessed: 2019-09-30. 2016, Technical Reference Document: Dynamic Load Modeling Zhang, 2017, Dependency analysis and improved parameter estimation for dynamic composite load modeling, IEEE Trans. Power Syst., 32, 3287, 10.1109/TPWRS.2016.2623629 Chaspierre, 2018, Modelling active distribution networks under uncertainty: extracting parameter sets from randomized dynamic responses Milano, 2013, A systematic method to model power systems as stochastic differential algebraic equations, IEEE Trans. Power Syst., 28, 4537, 10.1109/TPWRS.2013.2266441 Roberts, 2016, Validation of the Ornstein-Uhlenbeck process for load modeling based on μPMU measurements Maldonado, 2017, A statistical approach to dynamic load modelling and identification with high frequency measurements Takenobu, 2018, Evaluation of dynamic voltage responses of distributed energy resources in distribution systems Cappé, 2007, An overview of existing methods and recent advances in sequential Monte Carlo, Proc. IEEE, 95, 10.1109/JPROC.2007.893250 Metropolis, 1953, Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087, 10.1063/1.1699114 Hastings, 1970, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97, 10.1093/biomet/57.1.97 Schön, 2018, Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo, Mech. Syst. Signal Process., 104, 866, 10.1016/j.ymssp.2017.10.033 Andrieu, 2010, Particle Markov chain Monte Carlo methods, J. R. Stat. Soc., 72, 269, 10.1111/j.1467-9868.2009.00736.x Renmu, 2006, Composite load modeling via measurement approach, IEEE Trans. Power Syst., 21, 663, 10.1109/TPWRS.2006.873130 Foreman-Mackey, 2013, Emcee: the MCMC hammer, Publ. Astron. Soc. Pac., 125, 306, 10.1086/670067