Multiple-classification of power system states using multidimensional neural network
By: Tiwary, Shubhranshu Kumar
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Contributor(s): Pal, Jagadish.
Publisher: USA Springer 2023Edition: Vol.104(4), Aug.Description: 893-900p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: For the optimum operation and control of power networks, its operation is usually divided in to several states. Depending upon the severity of any contingency and its effect on the power network operations, a set of prompt corrective actions are predetermined to help in keeping the operational parameters of the grid in check. In this work, a multidimensional artificial neural network is exploited for multi-class classification of power network states based on the Fink and Carlsen’s approach. The parameters monitored here are active power, reactive power, voltage magnitude and bus voltage angles. A set of multiple layered neural networks were developed and utilized to monitor the state transition of these parameters from one state to the other. The time-consumption and advantages are then discussed.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0315 |
For the optimum operation and control of power networks, its operation is usually divided in to several states. Depending upon the severity of any contingency and its effect on the power network operations, a set of prompt corrective actions are predetermined to help in keeping the operational parameters of the grid in check. In this work, a multidimensional artificial neural network is exploited for multi-class classification of power network states based on the Fink and Carlsen’s approach. The parameters monitored here are active power, reactive power, voltage magnitude and bus voltage angles. A set of multiple layered neural networks were developed and utilized to monitor the state transition of these parameters from one state to the other. The time-consumption and advantages are then discussed.
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