###### Abstract
Many studies have focused on the installation of POD controllers within Flexible AC Transmission Systems (FACTS) [17; 18; 19], as well as in PV plants [20; 21] and WFs [22; 23; 24; 25], testing their efficiency in isolated and controlled simulations. Some investigations have also incorporated measurements from Phasor Measurement Units (PMUs), enabling the monitoring of inter-area oscillations [26] and real-time data integration to assess the performance of POD controllers in existing power systems [27; 28; 29]. Despite this, these works are often limited by short-duration simulations or the manual activation of POD controllers, which restricts the system's ability to respond autonomously to grid fluctuations. Furthermore, while activation functions have been employed to detect oscillations in some research, such as the Fast Fourier Transform (FFT) [30; 31; 32], the Prony Analysis [33; 34; 35], and the Matrix Pencil Method, among others [36; 37], these studies have not fully addressed the complexities of grid modeling or the need for effective POD controller parametrization under varying grid conditions. Moreover, extensive research explores the coordination of damping controllers between PSSs and POD using different optimization algorithms, such as Particle Swarm Optimization (PSO) [38; 39] and the Nobel Bat Algorithm (NBA) [40; 41], among others [42; 43]. However, they often neglect the critical aspect that is POD activation. While these studies provide valuable insights into the optimal configuration of POD controllers, they typically overlook the real-time, dynamic activation of these controllers. This aspect is critical for managing renewable energy integration challenges in modern power grids. The main objective of this article is to develop a comprehensive approach to oscillation detection, POD activation, inter-area oscillation damping, and POD deactivation, carrying out the entire process within DlgSILENT PowerFactory (DSPF) [44] without interruptions. This approach simulates complete POD operation in a real-world environment and represents a novel contribution, as no previous works have examined the entire process in a single simulation environment without requiring external software or stopping the simulation. This study focuses on the entire process, detailing the detection technique (FFT is used, noted for its computational efficiency [45]), the POD-P controller (more versatile than POD-Q when considering frequency inputs and multiple oscillations [46]), and the POD tuning method (using NBA, demonstrated to outperform PSO even with fewer iterations [47]). The New England IEEE-39 power system model is extended with PV plants for realistic simulation. The Power Plant Controller (PPC) is modified to incorporate a POD-P controller and detection functions, creating a realistic scenario with three renewable generation conditions, reflecting real-world variability. This study establishes a comprehensive foundation for future work in integrated grid stability simulations. Examining realistic conditions with varying availabilities of renewable energy highlights critical aspects of stability control in grids with high renewable penetration, providing valuable insights for future deployments of POD controllers in renewable-dominant systems.