Abstract-This work proposes several analysis and design results for primary droop control and secondary control in inductive microgrids. Building on our recent work, we study the problem of set point design in primary-controlled microgrids, and provide a choice of droop coefficients leading to the desired power flows. We then compare and contrast two distributed secondary-control schemes, and extend them to the case where only a fraction of inverters cooperates in regulating the network frequency. We show that both these secondary-control schemes achieve frequency regulation in the presence of network losses.

This paper proposes a model to identify the noise source in a virtualized infrastructure. This phenomenon appears when network functions running under virtual machines that are deployed on the same physical server compete for physical resources. First, an anomaly detection model is proposed to identify the machines that are in an abnormal state in the infrastructure by performing an unsupervised learning. An investigation of the root cause is later achieved by searching how anomalies are propagated in the system. To do this, a supervised learning of the anomaly propagation paths is proposed. A propagation graph is automatically created with a score assigned to its components. With a testbed created using Openstack, an experimentation study with real data is held giving promising results.