On-line harmonic estimation in power system based on sequential training radial basis function neural network

2011 ◽  
Author(s):  
Eyad Almaita ◽  
Johnson A. Asumadu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
On Line ◽  
Sequential Training

SCR Control System Design Based on On-Line Radial Basis Function and Backpropagation Neural Networks

2017 ◽  
Author(s):  
Wenbo Sui ◽  
Carrie M. Hall
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Nox Reduction ◽  
Control Technique ◽  
Urea Solution ◽  
Backpropagation Neural Network ◽  
Reduction Efficiency ◽  
Radial Basis ◽  
On Line

Because of its high NOx reduction efficiency, selective catalyst reduction (SCR) has become an indispensable part of diesel vehicle aftertreatment. This paper presents a control strategy for SCR systems that is based on an on-line radial basis function neural network (RBFNN) and an on-line backpropagation neural network (BPNN). In this control structure, the radial basis function neural network is employed as an estimator to provide Jacobian information for the controller; and the backpropagation neural network is utilized as a controller, which dictates the appropriate urea-solution to be injected into the SCR system. This design is tested by simulations based in Gamma Technologies software (GT-ISE) as well as MATLAB Simulink. The results show that the RBF-BPNN control technique achieves a 1–5 % higher NOx reduction efficiency than a PID controller.

On-line fault section estimation in power systems with radial basis function neural network

2002 ◽  
Vol 24 (4) ◽  
pp. 321-328 ◽  
Author(s):  
Tianshu Bi ◽  
Zheng Yan ◽  
Fushuan Wen ◽  
Yixin Ni ◽  
C.M. Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
On Line

scholarly journals Radial Basis Function Neural Network Application to Measurement and Control of Shunt Reactor Overvoltages Based on Analytical Rules

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Early Stage ◽  
Rbf Neural Network ◽  
Test System ◽  
Worst Case ◽  
Shunt Reactor ◽  
Radial Basis

This paper presents an artificial intelligence application to measure switching overvoltages caused by shunt reactor energization by applying analytical rules. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a reactor with residual flux. A radial basis function (RBF) neural network has been used to estimate the overvoltages due to reactor energization. Equivalent circuit parameters of network have been used as artificial neural network (ANN) inputs; thus, RBF neural network is applicable to every studied system. The developed ANN is trained with the worst case of the switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can measure the peak values and duration of switching overvoltages with good accuracy.

scholarly journals Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
M. Madhiarasan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Wind Farm ◽  
Prediction Models ◽  
Accurate Prediction ◽  
Renewable Energy Resources ◽  
Radial Basis

Abstract Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning, energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.

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