A comparative analysis of artificial neural network and support vector machine for online transient stability prediction considering uncertainties

Abstract

ABSTRACT 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. 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 ML algorithms. MATLAB was used to apply the ML algorithms (ANN and SVM), and to draw a comparison between them. The classification accuracy of ANN and SVM was found to be 98.4% and 94.4% (fixed topology), respectively. The classification accuracy of ANN and SVM was found to be 97.7% and 93.2% (topology change), respectively. The training time of ANN was less than that of SVM (for both topology cases). These results 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.