Control action based on steady-state security assessment using an artificial neural network
In this paper, the application of an Artificial Neural Network (ANN) for remedial action of a power system is presented. The aims of this study are to find the significant control action that alleviates a bus voltage violation of a power system and to demonstrate the ability of a neural network in t...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68936/1/Control%20action%20based%20on%20steady-state%20security%20assessment%20using%20an%20artificial%20neural%20network.pdf http://psasir.upm.edu.my/id/eprint/68936/ |
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| Summary: | In this paper, the application of an Artificial Neural Network (ANN) for remedial action of a power system is presented. The aims of this study are to find the significant control action that alleviates a bus voltage violation of a power system and to demonstrate the ability of a neural network in terms of evaluating the generation re-dispatch and load shedding amounts. The remedial action is based on a steady-state security assessment of the power system. The proposed algorithm has been successfully tested on a 9-bus test system. The results are compared with other conventional methods and it reveals that an ANN can provide the required amount of generation re-dispatch and load shedding accurately and instantaneously compared to other methods. On average, remedial actions were shown to have a positive effect for reducing the number of bus voltage violations and improving system security. |
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