Damage modeling framework for resilience hardening strategy for overhead power distribution systems

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

“…The aggregation and post-processing of the uncertainty information synthesized as PDFs for an enhanced decision-making has been addressed for degradation modelling [35], diagnostics [36], damage modelling frameworks to propagate and evaluate the influence of storms [37], post-contingency power-flow analysis [38] and other application areas [39]. However, in the area of probabilistic forecasting, generally, the probabilistic prediction stage finishes with the predicted probabilistic information and this is not further post-processed for an enhanced decision-making in subsequent modelling stages.…”

A Distributionally Robust Resilience Enhancement Strategy for Distribution Grids Considering Decision-Dependent Contingencies

“…The system voltage base is 12.66 kV, and voltage lower and upper limits are set as 0.9 p.u. or vegetation management are randomly tuned based on data from [5] and [8]. Robust level ε in the nominal test is set as 0.3 times of the empirical failure rate.…”

“…Given the limited budget in the real-world planning, it is critical to identify the most vulnerable components Accurately modeling the uncertain contingencies under EWEs is the prerequisite for avoiding suboptimal enhancement strategies. While the physical hardening problems of ADNs have been extensively investigated (see, e.g., [3], [4], [5], [6], [7]), two fundamental issues with contingency quantification remain unresolved: 1) probabilistic contingency models are always subject to misspecification, due to the scarcity of historical data on EWEs as well as the insufficient knowledge of interactions between weather and infrastructure [8]; 2) the decision-dependent uncertainty (DDU) inherent in contingencies has not been adequately characterized in the existing literature.…”

“…Concerning the first issue, while multiple studies have applied data-driven approaches [9], physical models [10] and their hybridization [8] to forecast contingencies, many evidences exhibit their limited accuracy. Figure 1(a) illustrates the potential divergences between the empirical failure probabilities and the estimated fragility curve of a distribution line [11], which mainly arise from the unavoidable model bias and inherent variance of the component's fragility.…”

A Statistical Framework for Evaluating the Effectiveness of Vegetation Management in Reducing Power Outages Caused during Storms in Distribution Networks

“…The authors of [26] created partial dependence plots that show that more frequent trimming results in a reduction of outages for a utility territory spanning two states in the central Gulf Coast region of the US. In another study that addressed reducing outages during storms, the authors of [27] developed a hybrid physics-based and data-informed Monte Carlo simulation (MCS) to examine how pole replacements in the electric grid would have affected outages in Connecticut during Hurricane Sandy, and suggested including vegetation conditions as a future research path. Other aspects of the interaction between vegetation and reliable power delivery that have been researched include comparing attitudes of residents about roadside vegetation management programs [28] and developing methodologies to reduce the expense of monitoring vegetation near power lines [29].…”