Pelton Turbine Needle Control Model Development, Validation, and Governor Designs

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Randell M. Johnson ◽  
Joe H. Chow ◽  
Michael V. Dillon
Keyword(s):  
Traveling Wave ◽  
Model Development ◽  
Control Model ◽  
Frequency Regulation ◽  
Model Parameters ◽  
Small Signal Stability ◽  
Pelton Turbine ◽  
Power Characteristics ◽  
Wave Effects ◽  
Needle Control

Underspeed needle control of two Pelton turbine hydro units operating in a small power system has caused many incidents of partial system blackouts. Among the causes are conservative governor designs with regard to small signal stability limits, nonminimum phase power characteristics, and long tunnel-penstock traveling wave effects. A needle control model is developed from “water to wires” and validated for hydro-turbine dynamics using turbine test data. Model parameters are tuned using a trajectory sensitivity method. In the governor design proposed here the needle regulation gains are distributed into the power and frequency governor loops with a multi-timescale approach. Elements of speed loop gain scheduling and a new inner-loop pressure stabilization circuit are devised to improve the frequency regulation and to damp the traveling wave effects. Simulation studies show the improvements of the proposed control designs.

Pelton Turbine Needle Control Model Development, Validation, and Governor Designs

2003 ◽  
Author(s):  
Randell M. Johnson ◽  
Joe H. Chow ◽  
Michael V. Dillon
Keyword(s):  
Traveling Wave ◽  
Model Development ◽  
Control Model ◽  
Frequency Regulation ◽  
Model Parameters ◽  
Small Signal Stability ◽  
Pelton Turbine ◽  
Power Characteristics ◽  
Wave Effects ◽  
Needle Control

Underspeed needle control of two Pelton turbine hydro units operating in a small power system has caused many incidents of partial system blackouts. Among the causes are conservative governor designs with regard to small signal stability limits, non-minimum phase power characteristics, and long tunnel-penstock traveling wave effects. A needle control model is developed from “water to wires” and validated for hydro-turbine dynamics using turbine test data. Model parameters are tuned with trajectory sensitivity. Proposed governor designs decompose the needle regulation gains into the power and frequency governor loops with a multi-time-scale approach. Elements of speed loop gain scheduling and a new inner-loop pressure stabilization circuit are devised to improve the frequency regulation and to damp the traveling wave effects. Simulation studies show the improvements of the proposed control designs.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Model Development ◽  
Biogeochemical Cycling ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Glacier Forefield ◽  
Unconstrained Model

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

scholarly journals Subsurface strata failure and movement based for improving gas emission control: Model development and application

2020 ◽  
Vol 8 (9) ◽  
pp. 3285-3302
Author(s):  
Jianwei Cheng ◽  
Zui Wang ◽  
Siyuan Li ◽  
Wanting Song ◽  
Weidong Lu ◽  
...  
Keyword(s):  
Model Development ◽  
Emission Control ◽  
Control Model ◽  
Gas Emission ◽  
Strata Failure

scholarly journals Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2899
Author(s):  
Abhinandana Boodi ◽  
Karim Beddiar ◽  
Yassine Amirat ◽  
Mohamed Benbouzid
Keyword(s):  
Reference Model ◽  
Model Development ◽  
Network Models ◽  
Real Data ◽  
Stochastic Approach ◽  
Simplified Model ◽  
Model Parameters ◽  
Order Model ◽  
Parameter Values ◽  
Low Order

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model parameters. The developed simplified model is afterward validated with real data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data.

scholarly journals Towards the Development of an Operational Digital Twin

Vibration ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 235-265
Author(s):  
Paul Gardner ◽  
Mattia Dal Borgo ◽  
Valentina Ruffini ◽  
Aidan J. Hughes ◽  
Yichen Zhu ◽  
...  
Keyword(s):  
Model Development ◽  
Computational Modelling ◽  
Poor Performance ◽  
Predictive Performance ◽  
Physical Structure ◽  
Model Parameters ◽  
Digital Twin ◽  
Course Of Action ◽  
Shear Structure ◽  
Secondary Function

A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase.

Analyzing the Impact of Using Biodiesel in the Parameters of a 30 kW Micro-Turbine Control Model

2006 ◽  
Author(s):  
Manuel A. Rendo´n ◽  
Marco A. R. Do Nascimento ◽  
Pedro P. C. Mendes
Keyword(s):  
Electrical Power ◽  
Chemical Properties ◽  
Control Model ◽  
Model Parameters ◽  
Proposed Model ◽  
Complete Study ◽  
Micro Turbine ◽  
Turbine Speed ◽  
Physical And Chemical ◽  
The Impact

This work presents the modifications in a 30 kW gas micro-turbine speed control model, when it was supplied with castor bean biodiesel in several proportions. The concern about using biodiesel as an alternative fuel is increasing in the Brazilian distributed generation market. For this analytics, a complete study was developed considering the effects of using this new fuel. Characteristics like chemical composition, physical and chemical properties of the different mixtures were analyzed, especially focusing on the kinematic viscosity of the fuel. The tests results performed with the micro-turbine, originally projected for diesel, are shown. Mixtures of 5, 10, 15, 20, 25, 30, 50 e 100% of biodesel were used, and several variables were measured in the whole range of power. The influence of the biodiesel characteristics in the model parameters are commented in the conclusions. The possible application of the proposed model in studies of electrical power network is suggested in the end of the article.

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