Abstract
The large scale integration of Distributed Energy Resources (DER) at the Low Voltage (LV) distribution network offers new opportunities for the improvement of power quality and network reliability. Currently, the occurrence of large disturbances at the transmission network causing severe voltage sags at the distribution level could lead to the disconnection of a large share of DER units connected to the LV network, causing a more severe disturbance. In this paper, Low-Voltage-Ride Through (LVRT) requirements and current support strategies are proposed to mitigate the impact of severe voltage sag at the distribution level for DER units connected to LV network. The impact of adopting the proposed LVRT strategies will be analyzed through simulation and experimentally. A developed in house ESS prototype incorporating the developed LVRT strategies is also presented, and its capacity to comply with the proposed LVRT requirements is demonstrated using an experimental Power Hardware-in-the-Loop (PHIL) setup.

Abstract
This paper presents an active fault diagnosis (AFD) method with reduced excitation for detection and identification of sensor faults of vehicles in a platoon formation. By introducing a probing signal into the platooning, it will allow an active excitation of the system, reveling a residual component, with the same frequency, that can be explored to obtain a fault identification of specific system faults. A supervisor is introduced to monitor the platoon behavior and activate the auxiliary input whenever the system natural excitation is insufficient for a clear fault diagnosis. This solution will allow the fault diagnosis to behave as active or passive through the adaptive signal provided by the supervisor. A dual Youla-Jabr-Bongiorno-Kucera (YJBK) matrix transfer function, also known as fault signature matrix (FSM) is investigated to get a fault diagnosis. In order to obtain an online identification of specific faults in the system, a Taylor approximation of the FSM is pursued. Computational simulations with a high-fidelity full-vehicle model, provided by CarSim, are carried out to demonstrate the effectiveness of the proposed active approach. A direct comparison between an active and a passive behavior in the same scenario shows that the active fault diagnosis method outperforms the passive approach whenever the dynamic behavior does not provide sufficient excitation. Furthermore, the excitation supervisor is able to significantly reduce the amount of artificial excitation introduced into the system ensuring a more energy efficient active fault diagnosis.

Abstract
The development of advanced driver assistance systems relies on an accurate estimation of the tire-road friction coefficient and cornering stiffness of the vehicle, which are closely linked to internal and external driving conditions. In this paper, an identification algorithm capable of simultaneously estimate the friction coefficient and cornering stiffness of the front and rear tires is pursued. A nonlinear adaptive law is proposed for the estimation of vehicle parameters under certain excitation conditions. It is shown that, by exploring the lateral dynamic of the vehicle, the convergence of the parameters to their true values can be guaranteed. A comprehensive study has been carried out in order to reveal the necessary conditions for convergence and observability of the parameters. Simulation results with a high fidelity full order Carsim model show a good performance of the proposed identification method.