A risk assessment approach for the cascading failure of electric cyber-physical system (ECPS) considering multiple information factors is proposed. First, considering hardware, software and personnel factors existing in the information system, the reliability model of the control function of the information system is established. Second, the reliability model of the physical component considering the control function of the information system is established. Finally, the risk assessment approach for the cascading failure of ECPS is introduced, and the probability of the cascading failure and the expected energy not supplied are used as the risk indices. Test results on an improved RTS-79 system show the effectiveness and significance of the method for choosing the optimal power communication network topology and the measures to reduce the risk.

One of the most addressed attacks in power networks is false data injection (FDI) which affects monitoring, fault detection, and state estimation integrity by tampering measurement data. To detect such devastating attack, the authors propose a statistical anomaly detection approach based on Gaussian mixture model, while some appropriate machine learning approaches are evaluated for detecting FDI. It should be noted that a finite mixture model is a convex combination of some probability density functions and combining the properties of several probability functions, making the mixture models capable of approximating any arbitrary distribution. Simulations results confirm superior performance of the proposed method over conventional bad data detection (BDD) tests and other learning approaches that studied in this article. It should be noted that using data which change significantly over a day can be highly clustered, and therefore, detected much easier compared with small changes in the loads. So without loss of generality, in the simulations it is assumed that the power demand follows a uniform distribution in a small range. However, the detector can be trained regularly based on the updated load profile.

In order to apply the cyber-physical system (CPS) co-simulation method based on state cache to a complex distribution network (CDN), a physical model corresponding to multiple operating states should be proposed. As the different spatial resolution of the physical model leads to different performances of the cyber-physical models, a multi-resolution model based on time-state-machine is presented for CDN here. First, the operation process of CDN is divided into five operating states by using time-state-machine method, and the boundary conditions between states are qualitatively discussed. Second, three different spatial resolution physical models of CDN are built, and their simulation performances under different operating states are quantitatively analysed to form a multi-resolution physical model of CDN. Finally, the multi-resolution model of CDN is validated by use of a cyber-physical co-simulation example. Simulation results show that the multi-resolution model is capable to automatically transmission from anyone operating state to another.

In recent years, cyber-physical system (CPS) applications have been extensively utilised in the electric power grid to enable wide-area protection, control, and monitoring of power systems. Many of these applications in a smart grid CPS depend on reliable time synchronisation. For example, synchrophasor data from geographically distributed phasor measurement units (PMU) utilise global positioning system (GPS) for precise timing. However, these units are exposed to GPS time spoofing attacks that can lead to inaccurate monitoring and trigger unnecessary, and possibly destabilising, remedial control actions. The authors develop an end-to-end case study demonstrating the effect of GPS spoofing attacks on the phase angle monitoring and control functions of a PMU-based load shedding scheme. The evaluation of authors attack strategy is performed in a hardware-in-the-loop real-time digital simulator-enabled power system testbed.

Cyber attacks and extreme events can cause severe consequences to the grid that require immediate response. Conventional remedial action schemes (RAS) use offline calculations to determine corrective control actions to deploy for a predetermined set of credible contingencies. Yet, cyber attacks cannot be sufficiently represented in a look-up table approach; such contingencies are highly dynamic and unpredictable. Online RAS with real-time calculation of corrective controls provides the most suitable and effective response. To achieve rapid computation and reduce the search space to only the most effective candidate control(s), the analytic corrective control selection method using clustering and factorisation techniques is developed based on controllability analysis. The resulting critical controls comprise a minimum set that is most effective in reducing the violations in the stressed areas of the system. While this study focuses on generators as the critical control mechanism, this methodology is broadly applicable to any corrective control for which a sensitivity matrix in relation to the violated components can be derived. The algorithm is evaluated with the IEEE 24-bus and IEEE 118-bus systems under compromised generator outage scenarios, and the identified critical control set is shown to be highly effective for reducing violations and improving RAS computation time.

Transient stability of power systems has become even more critical due to increasing complexity created by large penetration of renewable energy sources and massive deployment of information and communication technology. Fortunately, the two-way real-time data exchange capacity of smart grids allows designing advanced digital control schemes to better address the power system stability. In this study, a non-linear model-free-based robust controller in conjunction with a state estimation architecture is designed to enhance transient stability margins. The designed controller addresses uncertainties arising from communication and control input delay, sensor errors, varying plant parameters, and unmodelled dynamics effects. A novel time-delay compensation technique is presented in the control development to mitigate the effect of delay and the robustness of the proposed controller is proven by conducting a Lyapunov stability analysis with respect to additive disturbance and time delay. Furthermore, the proposed control framework is validated on the IEEE 39 bus test system through MATLAB simulation. The results show that the proposed framework is capable of stabilising the power system after a fault, also showing robustness to noise, latency in communication, delay in control input, and malicious data injection.