# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
### Literature Review  
Studies of power system static security assessment have been carried out for a few years [12; 13; 14] that have focused on contingency screening or ranking. In order to select and screen contingencies in static security assessment, the indices representing voltages deviation and line overloads are employed in [12; 13]. While in [PERSON] et al. [14], a database generation method is proposed for the critical contingencies that have been screened. Thus, as a commonly used method, the severity of the power system in post-contingency conditions is evaluated by the performance indices, which are computed by the variables of power systems [15]. In [PERSON] et al. [16], a single composite security index, which is obtained by Newton-Raphson load flow (NRLF) analysis, is proposed for defining the security, which provides a reference for the proposed method in this paper. The composite security index divides the power system conditions into three categories: the secure state with the index value of '0'; the alarm state with the index value between '0' to '1'; and the index value greater than '1', which represents an insecure state. The composite security index considers the violations of bus voltages and line flows, and avoids the selecting of index weights [16]. By adopting the security index in the proposal, the time consumed to evaluate severity could be reduced. However, in the research on static security assessment, the literature has an excessive focus on the accuracy and celerity [15; 16]. Furthermore, many of these studies ignore the regulation ability of the power system by employing adjustable devices, such as the transformers and reactive power compensation devices in the power system [17; 18; 19]. In [PERSON] et al. [17], [PERSON] et al. [18] and [PERSON] et al. [19], the power system demonstrated improved economic and environmental benefits by optimizing the variables of adjustable devices. Therefore, on the premise of ensuring fast and accurate screen contingencies, the effects of the adjustable devices are considered in this paper. Due to the large computing scale of NRLF analysis for obtaining the composite security index in the contingency analysis [20], the static security assessment of a power system is a heavy task. What's more, the online application of static security assessment has a higher requirement for efficiency. Thus, adopting machine learning methods is an accurate and powerful way for contingency analysis [21], whose speed and accuracy are suitable for online applications. There are three typical machine learning methods that can be used for static security assessment. One is the artificial neural network (ANN) algorithm [22; 23; 24]. In [PERSON] et al. [25]. In [PERSON] et al. [16], the static security index is predicted by adopting the ANN algorithm for contingency screening and ranking. In [PERSON] et al. [22] and [PERSON] et al. [23], the ANN algorithm is applied for enhancing the security of power systems. In [PERSON] et al. [24], an ANN module is employed for static security assessment, considering the voltage and load flow in the power system. Although the ANN algorithm has been employed in the literature, it is inappropriate for large-scale data modules, and the internal mechanism is difficult to understand. Another is the decision tree (DT) algorithm [25; 26]. In [PERSON] et al. [25], the static security assessment applies machine learning techniques, which are based on decision tree algorithms, to improve the efficiency of contingency screening and ranking. In [PERSON] et al. [26], a modified decision tree algorithm is employed for static security assessment. Static security problems can be assessed by adopting decision tree algorithms; however, it is possible to over-fit or trap decision tree algorithms into local minimum points [27]. In addition, the support vector machine (SVM) algorithm is another machine learning method for static security assessment [28; 29]. In [PERSON] et al. [28] and [PERSON] et al.