scholarly journals Radial Basis Function Neural Network Application to Power System Restoration Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Test System ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
Radial Basis ◽  
System Restoration

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of 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 estimate the peak values and duration of switching overvoltages with good accuracy.

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.

The Study of Switching Overvoltages under Power System Restoration Scenario Using Extended Delta-Bar-Delta Algorithm

2013 ◽  
Vol 14 (3) ◽  
pp. 219-230 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Random Search ◽  
Test System ◽  
Power Equipment ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
System Restoration ◽  
Artificial Neural Network Ann

Abstract This paper presents an intelligent approach to evaluate switching overvoltages during power equipment energization. Switching action is one of the most important issues in power system restoration schemes. This action may lead to overvoltages that can damage some equipment and delay ‎power system restoration. In this work, transient overvoltages caused by power equipment energization are analyzed and estimated using artificial neural network (ANN)-based approach. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. In the cases of transformer and shunt reactor energization, ANNs are trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

scholarly journals Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Test System ◽  
Power Equipment ◽  
Worst Case ◽  
Power System Restoration ◽  
Switching Action ◽  
System Restoration ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm

This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages.

Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms

2012 ◽  
Vol 13 (3) ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Early Stage ◽  
Random Search ◽  
Test System ◽  
Worst Case ◽  
Power System Restoration ◽  
Three Phase ◽  
Equivalent Circuit Parameters ◽  
System Restoration

Abstract In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a 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 three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer ‎energization. ‎ Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The ‎developed ANN is trained with the worst case of the switching condition 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 estimate the peak values and ‎durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

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