Power system stability recognition using SVM classifier
Corressponding author's email:
aunn@hcmute.edu.vnKeywords:
recognition, classification, power system stability, uport vector machine, neural networkAbstract
Investment in developing power system infrastructure cannot keep up with the growth of load. The power system must operate under stressful condition, and operating point of power system is close to its stability limit. Therefore, the power system is more vulnerable to incidents. A instability of the power system needs to be detected early. Since then, opportunity drives the power system into re-stability state easier. Conventional methods are highly time-consuming for transient stability analysis of power system. So, the methods are unsuitable for on-line application. Pattern recognition is a promising method for on-line power system stability evaluation. The paper introduces a Suport Vector Machine (SVM) classifier and suggests applying SVM classifier to assessment of power system stability. The study is implemented on IEEE 39-bus power system network. The accuracy recognition of SVM classifier is compared with that of MLP (Multilayer Perceptron Neural Network) classifier. The results showed that the SVM classifier achieved higher accuracy recognition than the MLP classifer.
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