CONTROL OF SHUNT REACTOR OVERVOLTAGES BY CONTROLLED SWITCHING DURING POWER SYSTEM RESTORATION

2012 ◽  
Vol 21 (07) ◽  
pp. 1250051
Author(s):  
IMAN SADEGHKHANI ◽  
ABBAS KETABI ◽  
RENE FEUILLET
Keyword(s):  
Power System ◽  
Early Stage ◽  
Shunt Reactor ◽  
Load Variations ◽  
Power System Restoration ◽  
Controlled Switching ◽  
Equivalent Circuit Parameters ◽  
Artificial Neural Network Ann ◽  
Extra High Voltage ◽  
Shunt Reactors

The shunt reactors located on both line terminals and substation bus-bars are commonly used on long extra high voltage (EHV) transmission systems for controlling voltage during load variations. 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 shunt reactor with residual flux. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. A harmonic index has been introduced that it's minimum value is corresponding to the best-case switching time. In addition, in this paper an artificial neural network (ANN) is used to estimate the optimum switching instants for real time applications. ANN is trained with equivalent circuit parameters of the network, so that developed ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.

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.

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 Modelling and Design of a Low-Level Turn-to-Turn Fault Protection Scheme for Extra-High Voltage Magnetically Controlled Shunt Reactor

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4628
Author(s):  
M. Asghar Khan ◽  
Tao Zheng
Keyword(s):  
Power System ◽  
High Voltage ◽  
Main Idea ◽  
Magnitude Comparison ◽  
Shunt Reactor ◽  
Low Level ◽  
Protection Scheme ◽  
System Protection ◽  
Controlled Shunt Reactor ◽  
Extra High Voltage

The objective of this paper is to model and design a low-level turn-to-turn fault (T2TF) protection scheme for a magnetically controlled shunt reactor (MCSR), during incipient stage under 10% to 100% operating capacity. Due to the structural and functional differences of all the three windings in extra-high voltage (EHV) MCSR, a separate mechanism of detecting a T2TF in each winding is necessary. For this purpose, a detailed mathematical and structural analysis of the model is performed, and a comprehensive protection scheme based on the internal changes in magnetic and electric parameters of the windings is formulated to detect 3% T2TF in power windings (PWs), control windings (CtrWs), compensation windings (CpWs), and to differentiate it from other abnormalities. The main idea of the scheme is to perform the currents magnitude comparison of respective winding with the predefined settings values and decide necessary action. The proposed scheme is also capable of identifying the faulty winding along with faulty phase. The scheme is tested under different operating capacities (10%, 50%, 100%), and other types of unusual conditions, i.e., direct energization, pre-excited energization, power regulation, internal and external faults. The results demonstrate the effectiveness of the proposed scheme. The work of this paper is applicable in the areas of power system transmission and power system protection. The simulations are carried out on MATLAB/Simulink-based models.

Investigation of Transmission Line Models for Switching Overvoltages Studies

2013 ◽  
Vol 14 (3) ◽  
pp. 231-238
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Transmission Line ◽  
Test System ◽  
Shunt Reactor ◽  
Power System Restoration ◽  
System Restoration ◽  
Transmission Line Models ◽  
Simulation Time

Abstract Overvoltages caused by switching operation of power system equipments might damage some equipment and delay power system restoration. This paper presents a comparison between transmission line (TL) models for overvoltages study and investigates which TL model is most proper for every case study. Both simulation time and accuracy factors of TL models are considered for selecting best TL model. Various cases of switching of transformer, shunt reactor, capacitor bank, and transmission line are investigated and simulation results for a partial of 39-bus New England test system, ‎show that the proposed TL model evaluation increase accuracy and reduce simulation time (accelerate power system restoration) properly.

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 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.

scholarly journals A Study on the Harmonic Resonance during Energizing Primary Restorative Transmission Systems: Korean Power System Case

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 290
Author(s):  
Minhan Yoon ◽  
Wonkeun Yu ◽  
Junghyun Oh ◽  
Heungjae Lee
Keyword(s):  
Power System ◽  
Resonance Phenomenon ◽  
Transmission Systems ◽  
Nonlinear Characteristics ◽  
Restoration Process ◽  
Power System Restoration ◽  
Comparison Groups ◽  
System Restoration ◽  
Extra High Voltage ◽  
Harmonic Resonance

In this paper, a power system restoration study following a massive or complete blackout was performed. The power system restoration process from a complete shutdown system without the operating generation and load starts with energizing primary restorative transmission systems. During this primary restoration process, unexpected over-voltage may occur due to nonlinear interaction between the unloaded transformer and the transmission system. This is known as the harmonic resonance phenomenon that may cause the burning out of a transformer or other devices. So far, harmonic resonances have been reported in some extra-high voltage systems around the world. Since the harmonic resonance originates from the nonlinear characteristics of the power system components, it is very difficult to predict the occurrence of this phenomenon. This paper reports the analyses of the harmonic resonance that can occur in the Korean power system. In addition, through calculating the required buffer load compared to the length of the line, a solution that changes the length of the restoration path impedance considering the specificity of the Korean system was presented. The various analyses of harmonic overvoltage, including methodologies that are used internationally as comparison groups, are provided based on PSCAD/EMTDC simulations.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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