Search Results (2)

Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study proposes a novel automated method for reconstructing medium-voltage (MV) power grids. The novelty of the proposed method lies in combining OpenStreetMap energy and land-use data in a unified and automated framework, thereby reducing the need for expert input. The proposed method employs a systematic aggregation of data, an estimation of energy demand, and the application of algorithmic techniques to generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if” scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios. Full article

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions. Full article