Contingency screening for steady-state security analysis by using FFT and artificial neural networks

2000 ◽  
Vol 15 (1) ◽  
pp. 421-426 ◽  
Author(s):  
T.S. Sidhu ◽  
Lan Cui
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Security Analysis ◽  
Contingency Screening ◽  
State Security ◽  
Artificial Neural

Identification of a Steady-State Flow in Porous Media Using Artificial Neural Networks

2012 ◽  
Author(s):  
Marek J. Lefik ◽  
Daniela P. Boso ◽  
Bernhard A. Schrefler
Keyword(s):  
Neural Networks ◽  
Porous Media ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Concentration Field ◽  
Flow In Porous Media ◽  
Homogeneous Field ◽  
Steady State Flow ◽  
Convection Problem ◽  
Artificial Neural

For a steady state convection problem, assuming given concentration field values in a few measurement points and hydraulic head values in the same piezometers, the source of the concentration, and its intensity are deduced using Artificial Neural Networks (ANNs). ANNs are trained with data extracted from Finite Difference (FD) solution of a classical convection problem for small Peclet number. The numerical analysis is exemplified for vanishing, homogeneous and non-homogeneous field of velocity. It is shown that the diffusivity vector can also be identified. The complexity of the problem is discussed for each studied case.

An Estimation of 3-Way Catalyst Performance Using Artificial Neural Networks During Idle Speed

2004 ◽  
Author(s):  
P. N. Botsaris ◽  
D. Bechrakis ◽  
P. D. Sparis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Expert Knowledge ◽  
Operating Conditions ◽  
Experimental Results ◽  
Outlet Temperature ◽  
Practical Applications ◽  
Idle Speed ◽  
Artificial Neural

The intelligent control as fuzzy or artificial is based on either expert knowledge or experimental data and therefore it possesses intrinsic qualities like robustness and ease implementation. Lately, many researchers present studies aim to show that this kind of control can be used in practical applications such as the idle speed control problem in automotive industry. In this study, an estimation of an automobile three-way catalyst performance with artificial neural networks is presented. It may be an alternative approach for an on board diagnostic system (OBD) to predict the catalyst performance. This method was tested using data sets from two kind of catalysts, a brand new and an old one on a laboratory bench at idle speed. The catalyst operation during the “steady state” phase (the phase that the catalyst has reached its operating conditions and works normally) is examined. Further experiments are needed for different catalyst typed before the methods is proposed generally. It consists of 855 elements of catalyst inlet-outlet temperature difference (DT), hydrocarbons (HC), and carbon monoxide (CO) and carbon dioxide (CO2) emissions. The simulation: detects the values of HC, CO, CO2 using the DT as an input to our network forms a neural network. Results showed serious indications that artificial neural networks (or fuzzy logic control laws) could estimate the catalyst performance adequately depending their training process, if certain information about the catalyst system and the inputs and output of such system are known. In this study the “steady state” period experimental results are presented. In this paper the “steady state” period experimental results are presented.

On-line dynamic security contingency screening using artificial neural networks

2000 ◽  
Vol 147 (6) ◽  
pp. 367 ◽  
Author(s):  
K.W. Chan ◽  
A.R. Edwards ◽  
R.W. Dunn ◽  
A.R. Daniels
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Contingency Screening ◽  
Dynamic Security ◽  
On Line ◽  
Artificial Neural

Functional Approximation of Power System Steady-State Voltage Stability Limits by Artificial Neural Networks

2013 ◽  
Vol 24 (4) ◽  
pp. 544-554 ◽  
Author(s):  
Guilherme G. Lage ◽  
Ricardo A. S. Fernandes ◽  
Geraldo R. M. da Costa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Power System ◽  
Voltage Stability ◽  
Functional Approximation ◽  
Artificial Neural ◽  
Stability Limits

scholarly journals Power system steady state calculations using artificial neural networks

2020 ◽  
Vol 216 ◽  
pp. 01102
Author(s):  
Muzaffar Khudayarov ◽  
Nuriddin Normamatov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Power Systems ◽  
Feedforward Neural Networks ◽  
System Of Nonlinear Equations ◽  
Newton Raphson ◽  
Artificial Neural ◽  
Steady State Problem ◽  
Raphson Method

The power systems steady-state problem are described by a system of nonlinear equations, and for their solution are widely used iterative techniques such as the Newton-Raphson and others. Recently, techniques based on the use of genetic algorithms, the theory of fuzzy sets, artificial neural networks have been applied to solve this problem. In this article feedforward neural networks are used for calculating the steady-state regimes. The modeling results were obtained with the results of calculations using the Newton-Raphson method.

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