An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning  
[PERSON]  
1 Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS] (H.S. ); [EMAIL_ADDRESS] (Y.L.) 2 Northwest Subsection of State Grid Corporation of China, Xi'an 710048, China; lasa0918@163. Traditional transient stability assessment (TSA) methods include a time-domain method based on (1) electromechanical transient simulations [11; 12; 13], and (2) direct methods based on [PERSON]'s stability theory [14; 15], transient energy function (TEF) related approaches [2; 16], equal area criterion (EAC) related approaches [9; 17], and so on. Time-domain simulation with detailed dynamic component models is more accurate and can be used as a standard for testing other TSA methods. However, simulation-based methods are often used offline, because of the large number of calculations required when considering large-scale power systems and large contingency sets. Direct methods can reflect the transient characteristics of power systems, by constructing an appropriate Lyapunov energy function, which is a sufficient but unnecessary condition for TSA, with a strict mathematical foundation. However, it is usually rather difficult to formulate an energy function that satisfies the complex operation conditions of a nonlinear power system. Therefore, the two traditional TSA methods cannot meet the requirements of fast and accurate online transient stability assessment. Since the late 1980s, with the rise of data mining techniques, [PERSON] et al. began to apply machine learning (ML) algorithms such as the decision tree (DT) and artificial neural network (ANN) to power system transient stability predictions [18]. The key issue to transient stability prediction is to establish the mapping relationship between system features \(X\) and transient stability \(\mathrm{y}\) that is, to find the nonlinear function of \(\mathrm{y}=f(X)\). In order to learn this mapping relationship, a large number of samples need to be obtained through offline time-domain simulations; and this nonlinear function can then be fitted using the ML algorithm. Due to the rapid development of machine learning and its related mathematical theories, ML-based TSA research has been widely carried out worldwide; (1) ANN related models, including adaptive ANN application for dynamic security assessment [19], convolutional neural networks [20; 21], deep imbalanced learning framework [22], and deep belief network and model interpretation method [23] for transient stability assessment; (2) DT related models to predict system vulnerability [24], and transient instability [25]; (3) support vector machines (SVM) related models, such as an improved SVM, were proposed for real-time TSA in power systems in Reference [26]. Rotor speed, rotor angle of generators and voltage amplitude of buses after fault were extracted as the input features to train and test the SVM model in Reference [27]. A two-stage feature selection method based on SVM was presented In Reference [28]. Reference [29] introduced the information fusion technology into TSA, in order to obtain feature sets with reduced dimensions. Additionally, a variant of SVM, like core vector machine (CVM), was also proposed to solve the TSA problem based on phasor measurement units (PMUs) big data [30]; (4) other ML models, such as the extreme learning machine (ELM) algorithm [31], the least absolute shrinkage and selection operator (LASSO) [32], etc., have also been applied to the transient stability prediction of power systems. Among all ML algorithms, SVM is one of the most commonly used methods in transient stability prediction, because the TSA problem can be seen as a simple binary classification problem.