## 1 Introduction
Different methodologies for assessing power system resilience are presented in [39; 40; 41; 42; 43; 44; 45; 46]. [PERSON] et al. [39] analyze the grid resilience to False Data Injection (FDI) attacks with different magnitudes and number of false data inputs. [PERSON] et al. [40] propose an approach to prioritize power system resilience capabilities in order to contain the impact and restore the network quickly with a framework for assessing different disaster scenarios. [PERSON] et al. [41] propose a sequential-simulation based time-series model for evaluating the effect of wind on transmission lines and entire power infrastructure. [PERSON] et al. [42] propose a toughness approach to quantify the robustness of a power system against potential disasters. [PERSON] at al. [43; 44] present a risk-based resilience assessment methodology in operation-planning mode to predict the riskiest contingencies including threat intensity and component vulnerability that will affect the power system resilience. [PERSON] et al. [45] present a literature survey on power distribution system resilience assessment. A variety of studies have addressed the resilience solutions based on recovery and restoration to minimize the impact of extreme and catastrophic events [46; 47; 48; 49; 50]. [PERSON] et al. [46] present and review the research towards methods and tools of forecasting natural disaster related power system disturbances, hardening and pre-storm operations, and restoration models. [PERSON] et al. [47] present a significant change in power grid response and recovery schemes by developing a framework for proactive recovery of power assets to enhance the resilience. [PERSON] et al. [48] present the different blackout recovery mechanisms available to the System Operator to respond to and recover from such an extreme event. [PERSON] et al. [49] propose the use of price functions to estimate people's willingness to pay for more resilience in the power supply. [PERSON] et al. [50] present a reconfiguration model for load restoration in radial distributed systems that includes multiple energy services, including local combined heat and power (CHP) plants, to meet the demand of critical loads during post-disaster horizons. Outage data obtained from bulk transmission equipment play an important role in BPS planning, operations, and maintenance practices. Outage data statistics are considered essential when evaluating past, present, and future grid resilience. NERC has been collecting continent-wide transmission outage and inventory data in Transmission Availability Data System (TADS) since 2008 [51; 52]. TADS has been used to (a) assess the root cause of outages on major BPS elements; (b) to calculate typical reliability indices; and to (c) identify reliability risks due to independent, common mode, and dependent outages [53; 54; 55; 56; 57; 58]. The fundamental aspects of common mode and dependent outages in the power systems are presented and reviewed in [59; 60; 61; 62; 63]. Another major mechanism of failure in the power grid is a cascading outage. A cascading outage is a defined as a sequence of dependent outages that successively weaken or degrade the power transmission system [64]. The work in [65; 66; 67; 68; 69; 70; 71] shows how to assess cascading via a sequence of dependent outages, how to benchmark the proposed analysis methodologies, and how to evaluate the cascading from historical outage data. The authors of [72; 73; 74; 75; 76; 77; 78; 79; 80] present a variety of methodologies for assessment of BPS resilience using historical outage data for major BPS components such as lines and transformers. [PERSON] et al. [72] present a multi-dimensional machine learning model to improve power grid resilience through predictive outage estimation. [PERSON] et al. [73] present an approach to compute overall transmission grid resilience using historical utility outage data. [PERSON] et al. [74] evaluate transformer historical failure data to assess the facility resilience and reliability.