Online Voltage Stability Assessment Using ANN and Continuation Power Flow
Using AI for Real-time Analysis
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This book presents an Artificial Neural Network (ANN) based method, involving the usage of Continuation Power Flow Methods, for on-line voltage stability assessment of power systems. A continuation power flow type algorithm is implemented through MATLAB, using the M-file programming approach along with some implementations from the "Voltage Stability Toolbox courtesy of Centre for Electric Power Engineering,Drexel University" and the Power System Analysis Toolbox. This implementation generates the nose curves, used for voltage stability analysis, for IEEE 30 bus test system which, in turn, are...
This book presents an Artificial Neural Network (ANN) based method, involving the usage of Continuation Power Flow Methods, for on-line voltage stability assessment of power systems. A continuation power flow type algorithm is implemented through MATLAB, using the M-file programming approach along with some implementations from the "Voltage Stability Toolbox courtesy of Centre for Electric Power Engineering,Drexel University" and the Power System Analysis Toolbox. This implementation generates the nose curves, used for voltage stability analysis, for IEEE 30 bus test system which, in turn, are used as target outputs for training the ANNs, by finding the distance to voltage collapse from the current system operating point.A single feed forward type Artificial Neural Network (ANN) is to be trained for all the vulnerable load buses of the test systems.A Modal Analysis technique has been implemented to identify the most vulnerable load buses of the IEEE 30 bus test system.The trainedANN is supposed to provide, as output, the Voltage Collapse Proximity Indicators (VCPI) for all the vulnerable load buses of the system, which are a measure of the voltage stability margin for such buses.
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