Smart Grid Surveillance With Unmanned Aerial Vehicle (UAV) Using K-Resiliency Modeling

Smart grid being a widely distributed engineering system may run through deep forests to long rivers, and over the cities as well. Physical damage to the power line from natural calamities or, cyber attacks by malicious people on the control system will hamper the functional integrity of the power grid. To ensure the usual operational flow, the control center needs to take immediate steps caused by these phenomena. Hence, inspection of the power line provides a means for the smart grid surveillance. Since any physical damage to the power network can be occurred in hardly reachable remote areas, understanding the amount of impairment will be time-consuming. Autonomous systems like Unmanned Aerial Vehicles (UAV), instead of the traditional human patrol, are one way to enable regular monitoring of the safety critical situations. The critical lines will be monitored by a fleet of UAVs to ensure a resilient surveillance system. In this work, we present a formal model for UAV surveillance resiliency over the power lines with a set of k UAVs from the whole set. The proposed approach at first considers the n-1 contingency analysis using actual system data, and state estimation procedures. By using linear sensitivity factors, we find the critical transmission lines in the smart grid and in accordance with that, placements and communication topology of the UAVs are done. Then, we evaluate k-resiliency for the UAV surveillance to cover all the critical lines satisfying the circumstances in case of k failure in the fleet of UAVs

Energy Consumption Estimation for Routing EVs based on Driver Behavior

There has been a significant increase in the sales of Electric Vehicles (EVs) in the United States and abroad in the last few years. Nevertheless, the overall adoption of these trends are hindered by range limits of EVs in conjunction with long charging times relative to traditional vehicles. In this context, determination of current and prediction of future energy demands is essential. This paper presents approaches for predicting energy consumption of EVs and discusses their eligibility for this purpose. Four modeling approaches have shown to be mostly used in recent literature. In order to predict an EV’s energy demand, several modeling techniques are combined to give an accurate prediction of the future energy consumption. For the system EVs a combination of physics -based modeling together with statistical modeling has shown to be an efficient choice