The widespread deployment of wind power generation (WPG) has adversely affected the flexibility and reliability of the power system during the operational period. Variable wind speed leads to the variable generation of wind energy systems (WES), which directly affects the operational reliability. This necessitates assessing WES's situational awareness (SA) to predict the day-ahead operational reliability for effective power system operational planning. The edifice of SA for WES is the monitoring of wind speed and WPG for the perception about the state of WES, comprehension about state dynamics, and consequent prediction of operational reliability. The wind system has been modeled using Simulink and interfaced with dSPACE hardware using a real-time interface (RTI) for real-time validation as well as the implementation of a cloud-based IoT platform for the acquisition of wind speed and WPG for the evaluation of SA. The WES is being represented using a multi-state wind speed model, considering the randomness of wind energy generation. For the prediction of wind speed and wind power for SA based operational reliability, a time series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is being used for the prediction of wind speed and WPG. The dSPACE-based real-time implementation of WES for the assessment of SA with IoT integration validates the efficacy of the proposed approach for operational planning, incorporating the wind generation infested with uncertainty.