Voltage Stability Assessment using Artificial Neural Network

被引：0

作者：

Sharma, Ankit Kumar ^{[1
]}

Saxena, Akash ^{[2
]}

Soni, Bhanu Pratap ^{[3
]}

Gupta, Vikas ^{[3
]}

机构：

[1] Jaipur Natl Univ, Dept Elect Engn, Jaipur 302017, Rajasthan, India

[2] Swami Keshvanand Inst Technol, Dept Elect Engn, Jaipur 302017, Rajasthan, India

[3] Malaviya Natl Inst Technol, Dept Elect Engn, Jaipur 302017, Rajasthan, India

来源：

2018 IEEMA ENGINEER INFINITE CONFERENCE (ETECHNXT) | 2018年

关键词：

ANN; FVSI; FFBPN; LR; RBFN; IEEE Bus Test-System; MSE; Regression; FLOW;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In deregulated environment voltage stability has become very important factor for the purpose of analysis. In this paper some important features associated with voltage stability use in power system have discussed. Line Stability index is used for estimation of the maximum loadability and in other words index is used to recognise the weak bus in electrical power system. In this paper Artificial Neural Networks (ANNs) are used for assessment of voltage stability or to confirm secure and insecure mode of the power system. The input data of neural network are yield from the Newton-Raphson (NR) load flow analysis in the platform of MATLAB R2015b. The result obtained from the N-R method also validates through Feed-Forward Back Propagation (FFBP) Layer Recurrent (LR) and Radial Basis Function Network (RBFN) in terms of accuracy to foresee the status of the power system. The effectiveness of the analyzed methods is validated through IEEE 14 test system and IEEE 30 test bus system, using Fast Voltage Stability Index (FVSI).

引用

页数：5

共 50 条

[21] Fault Location in the Transmission Network Using Artificial Neural Network
Dashtdar, M.
Esmaeilbeig, M.
Najafi, M.
Bushehri, M. Esa Nezhad
AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2020, 54 (01) : 39 - 51
[22] Fault Location in the Transmission Network Using Artificial Neural Network
M. Dashtdar
M. Esmaeilbeig
M. Najafi
M. Esa Nezhad Bushehri
Automatic Control and Computer Sciences, 2020, 54 : 39 - 51
[23] Prediction of heat transfer enhancement of delta-wing tape inserts using artificial neural network
Khan, Mohammed Zafar Ali
Aziz, Muhammad
Wijayanta, Agung Tri
CASE STUDIES IN THERMAL ENGINEERING, 2021, 27
[24] Estimating wheat and maize daily evapotranspiration using artificial neural network
Abrishami, Nazanin
Sepaskhah, Ali Reza
Shahrokhnia, Mohammad Hossein
THEORETICAL AND APPLIED CLIMATOLOGY, 2019, 135 (3-4) : 945 - 958
[25] Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network
Hamdy, Abeer
Hussein, Walid B.
20TH INTERNATIONAL CONFERENCE ON CIRCUITS, SYSTEMS, COMMUNICATIONS AND COMPUTERS (CSCC 2016), 2016, 76
[26] Assessment of Nutrient Dynamics in Sasthamkotta Wetland Using GIS Remote Sensing and Artificial Neural Network
J. Drisiya
K. Shibu
Transactions of the Indian National Academy of Engineering, 2025, 10 (2) : 453 - 470
[27] Shear capacity assessment of steel fiber reinforced concrete beams using artificial neural network
Sharifi, Yasser
Moghbeli, Adel
INNOVATIVE INFRASTRUCTURE SOLUTIONS, 2021, 6 (02)
[28] US Dollar's Influence on Indian Gold Price: Assessment Using Artificial Neural Network
Bogale, Deepa
Muley, Aniket
Bhalchandra, Parag
Kulkarni, Govind
INFORMATION AND COMMUNICATION TECHNOLOGY FOR COMPETITIVE STRATEGIES, 2019, 40 : 81 - 88
[29] On-line Probabilistic Voltage Security Assessment using Radial Basis Function Neural Network
Singh, Pushpendra
Titare, L. S.
Arya, L. D.
Choube, S. C.
PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON ICT IN BUSINESS INDUSTRY & GOVERNMENT (ICTBIG), 2016,
[30] A hybrid artificial neural network (ANN) and Ward equivalent approach for on-line power system voltage security assessment
Fu, Y
Chung, TS
ELECTRIC POWER SYSTEMS RESEARCH, 2000, 53 (03) : 165 - 171

← 1 2 3 4 5 →