With increased cyber infrastructure in large power systems with inverter-based resources (IBRs), it remains highly susceptible to cyber attacks. Reliable and secure operations of such system under a large signal disturbance necessitate an anomaly diagnosis scheme, which is substantial for either selective operation of relays (during grid faults), or cybersecurity (during cyber attacks). This becomes a challenge for power electronic systems, as their characteristic response to such large signal disturbance is very fast. Hence, we accumulate our efforts in this paper to characterize between them accurately within a short time frame. A novel non-invasive anomaly diagnosis mechanism for IBRs is presented, which only requires locally measured voltage and frequency as inputs. Mapping these inputs in a X-Y plane, the characterization process is able to classify between the anomalies within 5 ms. To the best of our knowledge, this mechanism provides the fastest decision in comparison to the existing techniques, which also assists the equipped protection/cybersecurity technology to take corresponding decisions without enforcing any customization. The proposed scheme is validated on many systems using real-time (RT) simulations in OPAL-RT environment with HYPERSIM software and also on a hardware prototype. The results verify the effectiveness, scalability and accuracy of the proposed mechanism under different scenarios.
DC microgrids with distributed control architectures enhance the operational reliability, scalability and flexibility. However, the underlying communication infrastucture makes the system highly susceptible to cyber attacks. These attacks in DC microgrids cause severe impact, that can be easily misinterpreted as faults, which can then maloperate the protection decision. Although various protection schemes have been established, a tailor-made scheme to distinguish faults from cyber attacks is needed to ensure reliability of supply. In this paper, we use a two dimensional plane with deviation of current (δI) and voltage (δV ) at the terminal of each converter to distinguish between cyber attacks and faults in DC microgrids. As this scheme is governed based on physics of secondary controller operation, it is simple to implement and scalable to any physical topology. The performance of the proposed scheme is tested with real time simulation in OPAL-RT environment with HYPERSIM software for different topologies including radial, ring and mesh networks. In addition, the scheme is also tested and verified for simultaneous cyber attack on multiple converters. The simulation results validates that the proposed decentralized scheme is effective in both detecting and localizing cyber-physical anomalies within 2 ms.
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