scholarly journals Fast Prediction of Voltage Stability Index Based on Radial Basis Function Neural Network: Iraqi Super Grid Network, 400-kV

2011 ◽  
Vol 5 (4) ◽  
Author(s):  
Omer H. Mehdi ◽  
Noor Izzri ◽  
Mohammad K. Abd
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Stability ◽  
Stability Index ◽  
Grid Network ◽  
Voltage Stability Index ◽  
Radial Basis ◽  
Fast Prediction

scholarly journals Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Akash Saxena ◽  
Ankit Kumar Sharma
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Stability ◽  
Control Strategies ◽  
Additive White Gaussian Noise ◽  
Stability Margin ◽  
Operating Conditions ◽  
Voltage Stability Margin ◽  
Radial Basis

Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.

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