Resumen
The recent efforts to extend measurement and communication infrastructures in power grids have brought about new potentials for a more efficient and optimized grid operation using the data which become available. Due to this integration, on the other hand, not only the network expansion and planning but also the development of new applications must be carried out under consideration of the interdependencies between the power grid and the underlying communication network. In this work, we present a co-simulation environment and tool chain to enable integrated planning and subsequent performance analysis of a wide area measurement system. In the first part of the paper, we investigate the performance of PMU-based state estimation techniques under several power system and communication network scenarios. In the second part of the paper, furthermore, the presented setup is extended for co-simulative investigations of the machine learning-based fault detection and classification techniques in power systems. The presented results not only validate the developed software solution for similar research studies but also scrutinize the impact of communication network performance on power system state estimation and PMU-based decision-making for fault detection and classification.