## 1 Introduction  
Short-term voltage stability (STVS) is one of the major constraints that must be considered in order to keep today's power supply safe and reliable. The ability of the power system to maintain the steady equilibrium state of its bus voltage subjected to large disturbance within a short period is termed as STVS. The time frame of interest for STVS is about only a few seconds subjected to large disturbance [1]. Short-term voltage instability is caused by the dynamics of induction motor load, electronically controlled load, HVDC link power regulation, and inverter-interfaced renewable generators [2; 3]. In recent times, there has been a huge interest in understanding the impact of integration of different renewable energy resources (RESs) such as photovoltaic generation (PVG) and wind turbine generation (WTG) on different aspects of power systems such as small-signal, transient, and voltage stability [4; 5; 6; 7]. The integration of RESs affects the dynamics of power systems and has a detrimental impact on the STVS of the power system. Short-term voltage instability causes catastrophic power system outages all around the world [8; 9]. The online STVS assessment and its accompanying preventive strategy are effective countermeasures against short-term voltage instability in the post-disturbance state [2]. Thus, this work focuses on earlier assessment of STVS considering the impact of RESs so that the power system operator can make a quick decision to ensure the overall stability of the power system. The goal of the STVS assessment is to look into the dynamic voltage performance subject to large disturbance. The problems related to STVS are the fast voltage collapse, sustained low voltage without recovery, and fault-induced delayed voltage recovery (FIDVR) [10]. Furthermore, an FIDVR event many lead to the tripping of the under-voltageload-shedding devices and tripping of wind turbines due to low voltage ride-through capability in a wind-integrated system. The traditional power system model-based approach of STVS assessment using time domain simulation (TDS) increases the computational time, making it unsuitable for online application. However, the real-time data of bus voltage samples can be available at the control centers using a wide area measurement system (WAMS) and phasor measurement units (PMUs), making the real-time STVS assessment feasible [11; 12]. Recently, most of the research concentrates on the traditional machine learning algorithms for online assessment of STVS assessment using the voltage samples available using PMUs [10; 13; 14]. These algorithms, on the other hand, neglect the spectral and temporal structure of the data, omitting some of the most important features throughout the learning process, resulting in skewed outcomes. Deep learning algorithms have recently been used to learn high-level features from data, alleviating the need for domain expertise, and a separate feature extraction technique, which are required in a traditional machine learning approach. In addition, deep learning algorithms works well on prediction the time series data compared to machine learning algorithms [15; 16]. Furthermore, deep learning algorithms such as the convolutional neural network (CNN), the long short-term memory (LSTM) neural network, and the combined graph convolutional network and long short-term memory (GCN-LSTM) have been developed to address the problem related to short-term voltage instability [17; 18; 19]. Apart from this, the deep learning algorithms such as a bi-directional gated recurrent unit with an attention mechanism [20] and spatial-temporal graph convolutional network (STGCN) [21] which can learn temporal features and spatial-temporal features, respectively, from the time series data are used to predict the short-term voltage instability state of the power system. However, these studies are confined to predicting the voltage instability state: stable or unstable.