Risk-based voltage collapse assessment using generalized regression neural network
This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its seve...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2017
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| Summary: | This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system. © 2011 IEEE. |
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