Tracing the real power transfer of individual generators to loads using Least Squares Support Vector Machine with Continuous Genetic Algorithm
| Autor: | Mohd Wazir Mustafa, Siti Rafidah Abd Rahim, Saifulnizam Abd. Khalid, Mohd Herwan Sulaiman, Omar Aliman, Hussain Shareef |
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| Rok vydání: | 2011 |
| Předmět: |
Computer science
business.industry Supervised learning Electric generator Tracing Machine learning computer.software_genre law.invention Support vector machine Electric power system law Least squares support vector machine Genetic algorithm Maximum power transfer theorem Artificial intelligence business computer Algorithm |
| Zdroj: | International Conference on Electrical, Control and Computer Engineering 2011 (InECCE). |
| DOI: | 10.1109/inecce.2011.5953853 |
| Popis: | This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, and adapt a supervised learning approach to train the LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique. |
| Databáze: | OpenAIRE |
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