In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.

In power systems, failures of vulnerable lines can trigger large-scale cascading failures, and vulnerability assessment is dedicated to locating these lines and reducing the risks of such failures. Based on a structure and attribute network embedding (SANE) algorithm, a novel quantitative vulnerability analysis method is proposed to identify vulnerable lines in this research. First, a two-layered random walk network with topological and electrical properties of transmission lines is established. Subsequently, based on the weighted degree of nodes in the two-layered network, the inter-layer and intra-layer walking transition probabilities are developed to obtain walk sequences. Then, a Word2Vec algorithm is applied to obtain low-dimension vectors representing transmission lines, according to obtained walk sequences for calculating the vulnerability index of transmissions lines. Finally, the proposed method is compared with three widely used methods in two test systems. Results show the network embedding based method is superior to those comparison methods and can provide guidance for identifying vulnerable lines.

This paper aims to develop an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models, i.e. sensitivity analysis, which can not only reveal the most sensitive parameter of a thermal model but also improve model output accuracies. For the first time, the nonlinear time constant (NTC) of transformer oil is proposed to reshape three practical top-oil temperature models based on an expression of nonlinear thermal conductance: the modified IEEE clause 7 model, Swift's model, and Susa's model. Then, the multi-parametric sensitivity analysis (MPSA) is undertaken to reveal the effect of each parameter on the model output accuracy. Through onsite data validation, the results show that the accuracy performance of the proposed NTC thermal models are improved significantly by considering the nonlinear effect of oil time constant. Moreover, the derived sensitivity performances can clearly reveal the most dominant parameter of the model, so as to simplify the model parameter identification process by reducing the number of insensitive parameters. Finally, the heat-run test data is used as a reference to validate parameters optimized through a genetic algorithm (GA), which demonstrates that the proposed NTC IEEE model has not only one sensitive parameter but also superior accuracy performance.