Correlation between uncertainties in system model parameters and distribution of critical electromechanical modes

2010 ◽  
Author(s):  
F.B. Alhasawi ◽  
J.V. Milanović
Keyword(s):  
System Model ◽  
Model Parameters ◽  
Electromechanical Modes

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Simulation and Simulation Software Development of the Braking Process of a Subway Train

2014 ◽  
Vol 556-562 ◽  
pp. 294-301 ◽  
Author(s):  
Long Han ◽  
Chun Tian ◽  
Yan Wang ◽  
Meng Ling Wu ◽  
Zhuo Jun Luo
Keyword(s):  
Computational Effort ◽  
Simulation Software ◽  
System Model ◽  
Brake System ◽  
Railway Vehicle ◽  
Model Parameters ◽  
Modeling Process ◽  
Time Simulation ◽  
Subway Train ◽  
Braking Process

This paper deals with the problem of braking process modeling. A subway train braking process simulation software is built, which composes of a GUI and a underlying model. The underlying model consists of a train model and a brake system model. The train model is simplified and built by assembling subcomponent element models of a railway vehicle. The brake system model is simplified and built based on experimental data in order to reduce computational effort. The GUI of the software can be use to input model parameters, display simulation results, and store simulation data. As a result of the simplifications of the modeling process, the developed software can perform real time simulation.

scholarly journals Oceanic and atmospheric methane cycling in the cGENIE Earth system model

2020 ◽  
Author(s):  
Christopher T. Reinhard ◽  
Stephanie Olson ◽  
Sandra Kirtland Turner ◽  
Cecily Pälike ◽  
Yoshiki Kanzaki ◽  
...  
Keyword(s):  
Climate Impacts ◽  
Earth System Model ◽  
System Model ◽  
Comprehensive Treatment ◽  
Model Parameters ◽  
Anaerobic Oxidation Of Methane ◽  
Earth System ◽  
Atmospheric Methane ◽  
Model Framework ◽  
Oxidation Of Methane

Abstract. The methane (CH4) cycle is a key component of the Earth system that links planetary climate, biological metabolism, and the global biogeochemical cycles of carbon, oxygen, sulfur, and hydrogen. However, currently lacking is a numerical model capable of simulating a diversity of environments in the ocean where CH4 can be produced and destroyed, and with the flexibility to be able to explore not only relatively recent perturbations to Earth’s CH4 cycle but also to probe CH4 cycling and associated climate impacts under the very low-O2 conditions characteristic of most of Earth history and likely widespread on other Earth-like planets. Here, we present a refinement and expansion of the ocean-atmosphere CH4 cycle in the intermediate-complexity Earth system model cGENIE, including parameterized atmospheric O2-O3-CH4 photochemistry and schemes for microbial methanogenesis, aerobic methanotrophy, and anaerobic oxidation of methane (AOM). We describe the model framework, compare model parameterizations against modern observations, and illustrate the flexibility of the model through a series of example simulations. Though we make no attempt to rigorously tune default model parameters, we find that simulated atmospheric CH4 levels and marine dissolved CH4 distributions are generally in good agreement with empirical constraints for the modern and recent Earth. Finally, we illustrate the model’s utility in understanding the time-dependent behavior of the CH4 cycle resulting from transient carbon injection into the atmosphere, and present model ensembles that examine the effects of atmospheric pO2, oceanic dissolved SO42− and the thermodynamics of microbial metabolism on steady-state atmospheric CH4 abundance. Future model developments will address the sources and sinks of CH4 associated with the terrestrial biosphere and marine CH4 gas hydrates, both of which will be essential for comprehensive treatment of Earth’s CH4 cycle during geologically recent time periods.

Discrete-Time Nonlinear Singularly Perturbed System Identification Using Coupled Multimodel Representation

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Asma Ben Rajab ◽  
Nesrine Bahri ◽  
Majda Ltaief
Keyword(s):  
Discrete Time ◽  
Perturbation Parameter ◽  
Singularly Perturbed ◽  
System Model ◽  
Model Parameters ◽  
Singularly Perturbed Systems ◽  
Perturbed System ◽  
Marquardt Algorithm ◽  
Perturbed Systems ◽  
Full Knowledge

Abstract Many control and observability theories for singularly perturbed systems require the full knowledge of system model parameters exceptionally if the system is considered as black box. To overcome this problem and to obtain an accurate and faithful model, this paper describes a new identification method for discrete-time nonlinear singularly perturbed systems (NLSPS) using the coupled state multimodel representation. The Levenberg–Marquardt algorithm is used to identify not only the submodels parameters but also the perturbation parameter ε. Two cases are considered to identify these systems. The first one supposes that the perturbation parameter ε of the real system is known and thus only the submodels parameters are identified. The second case supposes that this perturbation parameter is unknown and has to be identified with the other submodels parameters. The simulation example demonstrates the effectiveness of the proposed identification.

Simulation of Basin Scale Runoff Reduction by Infiltration Systems

1994 ◽  
Vol 29 (1-2) ◽  
pp. 267-275 ◽  
Author(s):  
S. Herath ◽  
K. Musiake
Keyword(s):  
Urban Areas ◽  
Infiltration Rate ◽  
System Model ◽  
Richards Equation ◽  
Model Parameters ◽  
Runoff Reduction ◽  
Water Head ◽  
Basin Scale ◽  
Runoff Model ◽  
Modelling Approach

A modelling approach is presented to simulate infiltration systems in urban areas. The model consists of a hydrological sub-model and an infiltration system sub-model. Infiltration characteristics of individual facilities are first established using steady state numerical simulation of Richards' equation. These are represented as linear relations between the facility water head and infiltration rate for given facility widths. The infiltration system model is obtained by applying continuity equation to infiltration facilities lumped over a sub-catchment. This model is then coupled to a catchment runoff model to simulate storm runoff with infiltration systems. The model is applied to an infiltration system installation in a residential area, where stormwater runoff is monitored in a pilot area and a comparative area. The observed results suggest the method is adequate to evaluate the performance of infiltration systems. Except for the catchment storage routing parameter, all model parameters are determined from physical catchment characteristics.

Modeling and Enhancement of Mckibben Pneumatic Muscle Actuators

2012 ◽  
Vol 591-593 ◽  
pp. 793-796 ◽  
Author(s):  
He Zuo ◽  
Guo Liang Tao ◽  
Xiao Cong Zhu
Keyword(s):  
Dynamic Performance ◽  
System Model ◽  
Model Parameters ◽  
Mass Ratio ◽  
Slow Time ◽  
Nonlinear Characteristics ◽  
Pneumatic Muscle ◽  
Fast Switching ◽  
Dynamic Performances ◽  
Muscle Actuators

Mckibben pneumatic muscle actuators (PMA) have many advantages such as high power-mass ratio and low price, but their strong nonlinear characteristics makes modeling and controlling very hard, which limits their applications. This paper presents the modeling of PMA and methods to enhance their dynamic performances. Considering the incorporated models of fast-switching valves and PMAs, the entire system model is modified in some aspects and the dominant model parameters are determined through experimental results to estimate the dynamic characteristics precisely. Simulation and proper experiments reveal that the dynamic performance of PMA can be improved through filling the PMA with materials of high thermal conductivity. The slow time-varying disturbance caused by the temperature variation of inner gas can be reduced much, which lowers the difficulty of controlling.

A Method for On-Line Identification of Power System Model Parameters in the Presence of Noise

1982 ◽  
Vol PER-2 (9) ◽  
pp. 27-28
Author(s):  
K. E. Bollinger ◽  
H. S. Khalil ◽  
L. C. C. Li ◽  
W. E. Norum
Keyword(s):  
Power System ◽  
System Model ◽  
Model Parameters ◽  
Line Identification ◽  
On Line ◽  
Power System Model
Sign in / Sign up

Export Citation Format

Share Document