scholarly journals Self-commissioning calculation of dynamic models for synchronous machines with magnetic saturation using flux as state variable

2019 ◽  
Vol 2019 (17) ◽  
pp. 3609-3613 ◽  
Author(s):  
Riccardo Antonello ◽  
Luca Peretti ◽  
Fabio Tinazzi ◽  
Mauro Zigliotto
Keyword(s):  
Dynamic Models ◽  
Synchronous Machines ◽  
Magnetic Saturation ◽  
State Variable

Saturable and Decoupled Constant-Parameter VBR Model for Six-Phase Synchronous Machines in State-Variable Simulation Programs

2019 ◽  
Vol 34 (4) ◽  
pp. 1868-1880 ◽  
Author(s):  
Navid Amiri ◽  
Seyyedmilad Ebrahimi ◽  
Juri Jatskevich ◽  
Hermann W. Dommel
Keyword(s):  
Synchronous Machines ◽  
Constant Parameter ◽  
State Variable

scholarly journals Using model-based proposals for fast parameter inference on discrete state space, continuous-time Markov processes

2015 ◽  
Vol 12 (107) ◽  
pp. 20150225 ◽  
Author(s):  
C. M. Pooley ◽  
S. C. Bishop ◽  
G. Marion
Keyword(s):  
State Space ◽  
Markov Processes ◽  
Continuous Time ◽  
Dynamic Models ◽  
Model Systems ◽  
Model Parameters ◽  
Discrete State ◽  
State Variable ◽  
Model Based ◽  
Discrete State Space

Bayesian statistics provides a framework for the integration of dynamic models with incomplete data to enable inference of model parameters and unobserved aspects of the system under study. An important class of dynamic models is discrete state space, continuous-time Markov processes (DCTMPs). Simulated via the Doob–Gillespie algorithm, these have been used to model systems ranging from chemistry to ecology to epidemiology. A new type of proposal, termed ‘model-based proposal’ (MBP), is developed for the efficient implementation of Bayesian inference in DCTMPs using Markov chain Monte Carlo (MCMC). This new method, which in principle can be applied to any DCTMP, is compared (using simple epidemiological SIS and SIR models as easy to follow exemplars) to a standard MCMC approach and a recently proposed particle MCMC (PMCMC) technique. When measurements are made on a single-state variable (e.g. the number of infected individuals in a population during an epidemic), model-based proposal MCMC (MBP-MCMC) is marginally faster than PMCMC (by a factor of 2–8 for the tests performed), and significantly faster than the standard MCMC scheme (by a factor of 400 at least). However, when model complexity increases and measurements are made on more than one state variable (e.g. simultaneously on the number of infected individuals in spatially separated subpopulations), MBP-MCMC is significantly faster than PMCMC (more than 100-fold for just four subpopulations) and this difference becomes increasingly large.

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