Online Voltage Stability Assessment Using ANN and Continuation Power Flow: Using AI for Real-time Analysis

 
9783639363746: 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 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 trained ANN 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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About the Author:

Rhythm Singh is Assistant Professor in Electrical & Electronics Engineering Department in Galgotias College of Engineering & Technology India, Chakresh Kumar is Assistant Professor in Electronics & Communication Engineering Department in Galgotias College of Engineering & Technology,India

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Rhythm Singh (author)
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Book Description Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | Using AI for Real-time Analysis | 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 trained ANN 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. | Format: Paperback | Language/Sprache: english | 64 pp. Seller Inventory # K9783639363746

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Book Description VDM Verlag Jul 2011, 2011. Taschenbuch. Condition: Neu. Neuware - 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 trained ANN 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. 64 pp. Englisch. Seller Inventory # 9783639363746

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Book Description VDM Verlag Jul 2011, 2011. Taschenbuch. Condition: Neu. Neuware - 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 trained ANN 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. 64 pp. Englisch. Seller Inventory # 9783639363746

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Rhythm Singh, CHAKRESH KUMAR
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Book Description VDM Verlag, United States, 2011. Paperback. Condition: New. Language: English . Brand New Book. 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 trained ANN 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. Seller Inventory # KNV9783639363746

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Book Description VDM Verlag Dr. Mueller AG & Co. Kg. Paperback. Condition: New. 64 pages. Dimensions: 8.7in. x 5.9in. x 0.1in.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 trained ANN 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783639363746

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