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).
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页数：5
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