An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system
This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised lear...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/26118/1/An%20application%20of%20genetic%20algorithm%20and%20least%20squares%20support%20vector%20machine.pdf http://umpir.ump.edu.my/id/eprint/26118/ https://doi.org/10.1109/PEOCO.2011.5970400 |
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| Summary: | This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. The well known proportional sharing method (PSM) is used to trace the loss at each transmission line which is then utilized as a teacher in the proposed hybrid technique called GA-SVM method. Based on load profile as inputs and PSM output for transmission loss allocation, the GA-SVM model is expected to learn which generators are responsible for transmission losses. In this paper, IEEE 14-bus system is used to show the effectiveness of the proposed method. |
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