A New IoT-Driven Coherency Assessment Strategy for Enhancing Power System Stability in Power Grids with Renewable Energy Sources

Identification of coherent generators (CGs) is crucial for situational awareness of electrical power systems under emergencies. Sudden disturbances, in addition to uncertainties of renewables, can perturb the equilibrium state among synchronous generators, unleashing subsequent electromechanical oscillations. Under this situation, recognizing coherent generators is an essential step for proper control actions to maintain system stability and security. This study proposes an IoT-based strategy for coherency assessment in hybrid power systems. Coherency analysis is performed based on frequency deviation signals from all generating units, including renewables. Phasor measurement units (PMUs) measure the frequency of each generator, and then frequency deviations are calculated locally at edge devices. This step significantly enhances the accuracy of calculations while also reducing the amount of data sent to the cloud, thereby decreasing network latency. Then, Spearman’s rank correlation coefficient is adopted to measure the similarity level between all pairs of generators. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to define coherent generators autonomously based on the obtained similarity indices. The proposed approach is tested on the modified IEEE 39-bus system. This study also investigates the impact of integrating renewable energy sources (RESs) on the obtained coherency patterns. All time-domain simulations are performed on the DIgSILENT PowerFactory software connected to the ThingSpeak platform for cloud computing. Simulation results demonstrate the efficiency of the proposed strategy. It also illustrates the potential of coherent generating groups to change due to the insertion of low inertia resources.