Transient Security Assessment and Classification using Support Vector Machine

Power system security poses an important issue in planning and operation stages of a power system. Conventional method of security evaluation involves solving full AC load flow equations and transient stability analysis by time domain simulation. This technique is highly time consuming and infeasible for real time applications. The Pattern Recognition (PR) approach is recognized as an alternative tool for online security evaluation. This paper proposes a Support Vector Machine (SVM) based classification for transient security assessment. The proposed SVM-PR approach is tested on 9 Bus WSCC, 30 Bus and 57 Bus IEEE standard systems and the transient security status of the system is accessed. The performance of the SVM classifier is gauged in terms of measures like classification accuracy, execution time and misclassification rate and the results are compared with the Multilayer Perceptron (MLP) classifier. The proposed method helps to identify the most dominant system dependent features responsible for transient security classification and yields fairly high classification accuracy with less computation time. Further, the proposed approach can realistically be applied to large scale systems, as the number of input attributes selected for classification is independent of the system size.