The transformation of the European energy system poses considerable challenges to electrical grids, including increased resilience to climate change, the capacity to meet escalating energy demands of modern societies, and the integration of a greater share of variable renewable energy sources. The EU aims for climate neutrality by 2050, pursuing a net-zero greenhouse gas economy and a connectivity objective of at least 15% by 2030 to enhance the interconnection of installed electrical generation capacity among member states. Grid expansion is a viable alternative for achieving these objectives, offering significant benefits in integrating, transporting, and distributing renewable energy sources. In the RENEWIES project-based lab access at the Austrian Institute of Technology (AIT) in Vienna, we investigate control stability mechanisms and renewable energy integration in a real-time Microgrid (MG) system, incorporating power line communication infrastructure, distributed energy resources (DER), and Smart Grid Converters (SGC). The simulation framework, developed using MATLAB Simulink, includes photovoltaic (PV) energy sources, with a detailed analysis of power quality under varying MG conditions. To provide comprehensive validation of the multi-domain and large-scale smart grid, HIL approaches could be integrated with additional simulations and infrastructures. The concept of incorporating real-time Hardware-in-the-Loop (HIL) into a comprehensive framework underpins the ensuing ERIGrid methodologies. These solutions provide a comprehensive understanding of the communication network's behaviour and the power system's states. The study commences here, as monitoring grid stability necessitates the examination of changeable dynamic factors within a comprehensive framework utilising various approaches in the HIL infrastructure. With this aim, this study focuses on improving grid stability through non-parametric control methods, particularly PRBS-based impedance measurement, active damping, virtual impedance integration, and real-time solar panel data incorporation for high-penetration scenarios. PRBS signal injection is applied via a SGC to measure the system's impedance response, enabling the evaluation of grid behaviors. Furthermore, active damping strategies have been explored to enhance grid stability, particularly in scenarios involving high renewable penetration. The study also incorporates Hardware-in-the-Loop (HIL) testing using the Typhoon HIL 602+ platform to validate the simulation outcomes under real-world conditions. The HIL setup replicates power converter switching states, grid-connected harmonic loads, and network dynamics, allowing for a comprehensive evaluation of the proposed control methodologies. No issues arose during the utilisation of SGC, which may substitute the WFZ device in the project proposal and possesses a more sophisticated structure than the WFZ device. It was determined that SGC, offering both frequency control and enhanced regulation, can be employed in stability analyses in lieu of WFZ. The effectiveness of the SGC in dynamic stability enhancement is tested through impedance-based stability analysis, Nyquist diagrams, and phase/gain margin calculations. A non-parametric harmonic stability monitoring method applicable to both single and multiple converter systems was aimed to be developed. For this purpose, a non-parametric stability algorithm was successfully modeled and tested in real-time on Typhoon HIL to detect the grid stability of the MG network, which had previously been designed in MATLAB with real-time data. The findings indicate that active damping combined with virtual impedance provides the most effective solution for enhancing grid stability, particularly in high R/X ratio networks. These methods demonstrated superior performance in mitigating oscillations, improving impedance matching, and stabilizing the system under fluctuating DER inputs. Further improvements in grid stabilitycan be achieved through adaptive PRBS injection and real-time optimization of active damping parameters. This research contributes to the advancement of MG stability analysis, bridging the gap between simulation-based studies and real-world grid implementation. Future work will focus on extending impedance-based stability assessments to larger-scale networks and enhancing real-time control adaptability through machine learning-assisted optimization techniques.