An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system

• Main Authors: Mustafa, Mohd. Wazir, Sulaiman, M. H., Shareef, H., Abd. Khalid, S. N., Abd. Rahim, S. R., Alima, O.
• Format: Conference or Workshop Item
• Published: 2011
• Online Access: http://eprints.utm.my/id/eprint/45552/; http://dx.doi.org/10.1109/PEOCO.2011.5970400

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.