Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this article, we propose a novel high-dimensional data-driven cyber physical attack detection and identification (HCADI) approach. First, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in the distribution power grids. Then, we construct a high-dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score-based attack detection and binary matrix factorization-based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.

With the continuous development of intellectualization and network interconnection, the cyber–physical security of power electronics systems has become increasingly prominent. Particularly, powertrain systems composed of one or more electric drives in modern electric vehicles (EVs) are becoming more vulnerable to cyberthreats due to the connection to external networks in the intelligent traffic environment. In this article, we design a physics-guided machine learning to detect cyberattacks on EVs considering varying driving scenarios, which, to the best if our knowledge, has not been attempted before. To reflect the transient physical characteristics of EVs, we collect both device-level (e.g., current and voltage in the motor drive) and vehicle-level signals. Then, innovative data features concerning critical system performance and physical dynamics of the vehicle are proposed, with which we leverage data-driven methodology with high-fidelity physical power electronics and vehicular models. Based on the physics-guided data features, a machine-learning-based classifier is developed and validated in an OPAL-RT hardware-in-the-loop (HIL) simulation testbed and demonstrates high accuracy under various driving scenarios.

In this article, a systematic and generalized methodology is originally proposed to assess the vulnerability of electric drive systems due to sensor data integrity attacks. Novel evaluation metrics from the perspectives of steady-state and transient performance of electric drive systems are established to evaluate the system condition under different attacks. By using these metrics, innovative index-based resilience and security criteria, together with the stability theorem, are proposed specifically for electric drive systems, which can then be used for cyber-attack detection and diagnosis in a more systematic manner. Then, based on the simulation results under 15 attack cases (five typical types), the qualitative attack impacts on the dynamic characteristics and the statistical damage of different cyber-attacks to the defined metrics are analyzed, which can serve as useful guidelines for attack detection, diagnosis, and countermeasures.

The primary controller of a grid-forming (GFM) inverter governs the internal reference voltage and angle, which enables the inverter to naturally synchronize and share power with the connected grid; however, during disturbances on the grid caused by, e.g., line faults, overloading, or frequency and phase shifts, a GFM inverter can be pushed into a current-limited operation to protect the device against thermal damage. This can lead to a windup of the internal reference frequency generated by the primary controller and cause a total loss of synchronism with the grid. To make the primary controller of GFM inverters more robust against transient instability, this article proposes a method that manipulates the reference signals governed by the primary controller by introducing the concept of fictitious power. The fictitious power counteracts the integrator windup in the primary controller, which enables the current-limited inverter to conceive a stable operating point. This method is benchmarked against five state-of-the-art GFM inverter stabilization methods to evaluate its stability-enhancing performance. Full-order electromagnetic-transient simulations and experimental results of a hardware inverter test setup validate the benefits of the method to improve the transient stability of GFM inverters in the face of grid disturbances.