A Comparative Analysis of Artificial Neural Network and Support Vector Machine for Online Transient Stability Prediction Considering Uncertainties
Power system transient stability is an integral part of power system planning and operation. Conventional approaches to assess transient stability are time consuming and hence, are not suitable for online application. Moreover, the current industry practices majorly ignore various uncertainties, associated with transient stability. Thus, this paper presents a comparative analysis of two different machine learning (ML) algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), for online transient stability prediction, considering various uncertainties (load, network topology, fault type, fault location, and fault clearing time). Time domain simulations were conducted, using DIgSILENT PowerFactory software, for obtaining the training data for the ML algorithms. MATLAB was used to apply the ML algorithms (ANN and SVM), and to draw a comparison between them. The results obtained for the IEEE 14-bus system demonstrated that both ANN and SVM can rapidly estimate the transient stability, considering uncertainties, with a reasonable accuracy; however, ANN outperformed SVM, as its classification performance and computational performance was determined to be superior.
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