Integrating Radial Basis Function Neural Network with Fuzzy Control for Load Forecasting in Power System

2005 ◽  
Author(s):  
Siqing Sheng ◽  
Cong Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Load Forecasting ◽  
Radial Basis

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.

Sign in / Sign up

Export Citation Format

Share Document