Abstract The increasing penetration of inverter based resources (IBRs) is fundamentally changing the dynamic and protection behavior of modern power systems. Unlike synchronous generators, IBRs exhibit low physical inertia, control limited fault current contributions, and complex interactions among multiple power electronic control loops. Grid forming (GFM) inverters are particularly important because they can internally establish voltage and frequency references, support weak grids, and provide inertia-like... The increasing penetration of inverter based resources (IBRs) is fundamentally changing the dynamic and protection behavior of modern power systems. Unlike synchronous generators, IBRs exhibit low physical inertia, control limited fault current contributions, and complex interactions among multiple power electronic control loops. Grid forming (GFM) inverters are particularly important because they can internally establish voltage and frequency references, support weak grids, and provide inertia-like and frequency support behavior. However, the nonlinear control limits embedded within GFM inverters can significantly alter fault current signatures and affect the performance of conventional protection schemes. This study presents a control loop analysis of a GFM inverter model extracted from an electromagnetic transient (EMT)-based transmission distribution testbed developed for high IBR protection studies. The analysis is based on the open access PNNL PSCAD/EMTDC testbed, which includes WECC-compliant GFM and grid-following inverter models and enforces IEEE 1547 and IEEE 2800 ride through requirements. The GFM control architecture is characterized from the black box model files, revealing active power/frequency droop control, reactivepower/ voltage droop control, PI-based overload mitigation, reactive power limiting, voltage reference limiting, phase angle integration, and explicit phase current limiting. Key parameters identified include a Pf droop coefficient of 0.01 pu, a Q–V droop coefficient of 0.02 pu, active power limits of 0.0–0.9 pu, reactivepower limits of ± 1.0 pu, a voltage reference limit of 1.15 pu, and a maximum transient current limit of 2.0 pu. The same GFM control source file is retained across the Base, PL19, and PL39 cases, representing approximately 0%, 19%, and 39% transmission system IBR penetration, respectively. This consistent control implementation enables isolation of the system level effects of increasing IBR penetration, including reduced inertia, altered short circuit capacity, and modified fault current behavior. The analysis demonstrates that GFM hard limits, particularly the 2.0 pu transient current limit and 1.15 pu voltage ceiling, can suppress fault current magnitude, distort early cycle voltage and current waveforms, and potentially compromise distance, directional, and overcurrent protection functions. Because the dataset does not include time domain simulation outputs, dynamic metrics such as rise time, settling time, overshoot, and damping ratio are not numerically evaluated. The study establishes a structured foundation for future EMT simulation-based assessment of protection performance under increasing IBR penetration. Read More Read Less

Abstract The rapid adoption of electric vehicles (EVs) has intensified demands on charging infrastructure, yet conventional performance metrics based on energy delivery alone fail to capture operational inefficiencies such as post charging idle occupation. This study investigates station level utilization and idle time behavior in campus EV charging infrastructure using the SFU-EVP longitudinal dataset, comprising 91,518 charging sessions across 32 stations at Simon Fraser University.... The rapid adoption of electric vehicles (EVs) has intensified demands on charging infrastructure, yet conventional performance metrics based on energy delivery alone fail to capture operational inefficiencies such as post charging idle occupation. This study investigates station level utilization and idle time behavior in campus EV charging infrastructure using the SFU-EVP longitudinal dataset, comprising 91,518 charging sessions across 32 stations at Simon Fraser University. Three temporal indicators such ascharging time utilization, occupancy utilization, and idle time ratio are combined with demand and throughput measures to construct a seven dimensional station performance feature space. Descriptive analysis reveals that approximately 23.57% of total plugged in time is idle, with median occupancy utilization (26.64%) exceeding median charging utilization (20.78%), confirming pervasive post-charging occupation. Principal Component Analysis captures 86.01% of station level variation in two components, and K-Means clustering (silhouette score ï‚» 0.50) identifies two distinct station typologies: underutilized/low demand stations (28 stations, 16.96% charging utilization, 24.82% idle ratio) and high performing/high demand stations (4 stations, 46.93% charging utilization, 19.38% idle ratio). Only four stations, 12.5% of the infrastructure, account for approximately 43.6% of all sessions, demonstrating a strong demand concentration effect accompanied by greater behavioral efficiency. Hierarchical clustering validates the two group structure. The findings indicate that effective infrastructure planning must distinguish between capacity expansion at high demand hubs and behavioral interventions to reduce idle occupancy across the broader network, shifting analytical focus from how much infrastructure is required to how effectively existing infrastructure is utilized. Read More Read Less

Abstract This study investigates the discriminatory power of hierarchical atomic, crystal/structural, and energetic descriptors for classifying semiconductor materials into three Electronic Property Vector (EPV) categories: Band Gap, Conduction Band Minimum (CBM), and Valence Band Maximum (VBM). Using a structured dataset of 9,700 semiconductor records, the analysis employs one way ANOVA with effect size measures (²), mut ual-information scoring, stratified cross validated hierarchical classification, Pearson correlation... This study investigates the discriminatory power of hierarchical atomic, crystal/structural, and energetic descriptors for classifying semiconductor materials into three Electronic Property Vector (EPV) categories: Band Gap, Conduction Band Minimum (CBM), and Valence Band Maximum (VBM). Using a structured dataset of 9,700 semiconductor records, the analysis employs one way ANOVA with effect size measures (²), mut ual-information scoring, stratified cross validated hierarchical classification, Pearson correlation network analysis, and principal component analysis (PCA). Univariate results reveal that no individual descriptor achieves meaningful class separation; the strongest energetic descriptor, DOS Peak Value, accounts for only 0.11% of EPV variance (² = 0.00109, p = 0.005), while all other descriptors yield smaller or non significant effects. Hierarchical multi class classification across seven progressively combined descriptor blocks produces accuracies ranging from 33.09% to 34.87%, representing gains of at most 1.5 percentage points above the 33.3% random chance baseline. The best configuration (Atomic + Energetic) attains a Macro F1 of only 0.346. Correlation network and PCA analyses demonstrate that the four energetic descriptors are nearly orthogonal (maximum |r|  0.021) and span a genuinely four dimensional feature space with near uniform variance distribution. These findings establish that the currently available low level descriptors provide only marginal discriminatory information for EPV classification. The results underscore the necessity for higher order, interaction rich, or graph based representations to achieve reliable semiconductor electronicproperty classification. Read More Read Less