Biblio

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

The restoration of power distribution systems has a crucial role in the electric utility environment, taking into account both the pressure experienced by the operators that must choose the corrective actions to be followed in emergency restoration plans and the goals imposed by the regulatory agencies. In this sense, decision-aiding systems and self-healing networks may be good alternatives since they either perform an automated analysis of the situation, providing consistent and high-quality restoration plans, or even directly perform the restoration fast and automatically in both cases reducing the impacts caused by network disturbances. This work proposes a new restoration strategy which is novel in the sense it deals with the problem from the operator viewpoint, without simplifications that are used in most literature works. In this proposal, a permutation based genetic algorithm is employed to restore the maximum amount of loads, in real time, without depending on a priori knowledge of the location of the fault. To validate the proposed methodology two large real systems were tested: one with 2 substations, 5 feeders, 703 buses, and 132 switches, and; the other with 3 substations, 7 feeders, 21,633 buses, and 2,808 switches. These networks were tested considering situations of single and multiple failures. The results obtained were achieved with very low processing time (of the order of ten seconds), while compliance with all operational requirements was ensured.