Distinguishing Between Cyber Attacks and Faults in Power Electronic Systems—A Noninvasive Approach

Abstract

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.

An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models

“…Attack #15-PE Hardware Attack: Intentional PE hardware layer attack can include tempering, swapping, and vandalism to defect the PE hardware layer by physical attackers (AV18). This attack can cause degrading inverter performance, PE hardware components' faults, and shutting-down the device by the protection circuits [84].…”

“…PE-ID3 provides a comprehensive diagnosis against attacks. A remarkable example method distinguishing between cyberattacks and PE hardware faults can be found in [84], which can further provide designing PEF. However, Fallah et al [108] assume that firmware is secure and malware cases are not considered.…”

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

“…Examples of such faults include over voltages or overheating [30].  Environmental disturbances: External factors that exert an influence on the performance or operation of power electronic systems.…”

“…The [30] study, uses a non-invasive anomaly diagnosis mechanism to distinguish between cyber-attacks and faults in power electronic systems such as Inverter-Based Resources (IBRs) or microgrids. This method only requires locally measured voltage and frequency as input and can distinguish these anomalies within 5 ms.…”

Cyber-physical testbeds for smart grid and electric vehicle-charging infrastructure

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