This study examines the impact of increasing photovoltaic (PV) penetration on the transient stability of the IEEE 9-bus power system. Synchronous machines are modeled with standard subtransient dynamics, while PV units are represented as current-limited grid-following inverters. Transient stability is assessed through the Critical Clearing Time (CCT) and the post-fault dynamic behavior, obtained from time-domain simulations carried out in MATLAB/Simulink^® R2023b. Two permanent three-phase faults are considered: a primary contingency on line 7–5 and a secondary contingency on line 9–6, introduced to assess the robustness of the observed trends across different fault locations. The results show an increase in CCT as PV generation progressively replaces the active power supplied by synchronous machines, whose inertia is therefore maintained: from 210 ms (0% PV) to 440 ms (25%)/1080 ms (40%) at bus 5, 410 ms (25%)/1130 ms (40%) and 290 ms (25%)/650 ms (40%) at buses 6 and 8, respectively, demonstrating that the penetration site is a key factor for system stability. For distributed penetration among the three buses, CCT values of 340 ms (25%) and 1020 ms (40%) highlight the significant influence of PV placement at bus 8. The fault on line 9–6 consistently yields higher CCT values across all scenarios, confirming the robustness of these trends independently of fault location. Although an overall increase in CCT was observed, higher PV penetration also led to more pronounced oscillations and operability issues after the fault. In particular, 75% of the penetration scenarios under the fault on line 9–6 do not meet the active power recovery requirements of IEEE 1547-2018 and IEEE 2800-2022, a result more severe than that observed for the fault on line 7–5. These results underscore that a higher CCT does not guarantee operational compliance, and that stability-oriented control strategies—such as grid-forming operation, fast active power support, and dynamic voltage control—remain essential. They also suggest that planning practices should favor interconnections electrically closer to the slack generator. Overall, a high PV penetration level—modifying only the operating point of synchronous machines—allows longer fault durations to be tolerated; however, appropriate siting of PV units and the adoption of advanced inverter controls could mitigate the observed oscillations and post-fault operability challenges.

In this paper, a reliability analysis to determinate the most preponderant negative effect between the amplitude and intensity of Extreme Operating Gust (EOG) in back-to-back (BTB) power converter connected to PMSG-based wind turbine is proposed. For this, a 42 factorial design is proposed to analyze the impact of amplitude and intensity of the EOG gust on the electrical variables measured at the BTB power converter such as the current, voltage and active power. Note that for this analysis the La Ventosa wind database allocated in Oaxaca, Mexico is considered. The simulation of this system was developed using the capabilities of the PSIM software. Finally, the results of the reliability analysis are presented, determining the factor with the greatest impact on the reliability of the BTB power converter.

Accurate modeling of wind turbine power curves is essential for performance assessment, energy forecasting, condition monitoring, and operational optimization in wind energy systems. This study presents a comparative evaluation of power curve modeling approaches applied to a low-power Vertical Axis Wind Turbine (VAWT) operating under real environmental conditions at the University of the Isthmus, located in the Isthmus of Tehuantepec, Oaxaca, Mexico. The evaluated methods included the manufacturer’s power curve, an aerodynamic model based on Blade Element Momentum Theory (BEMT), parametric approaches using polynomial and logistic regressions, and non-parametric data-driven methods based on Random Forest (RF), Gaussian Process Regression (GPR), and Kernel Density Estimation (KDE). One year of SCADA data, including wind speed and generated power measurements, was analyzed, while model performance was assessed using RMSE, MAE and R², metrics. The results showed that machine learning approaches outperformed analytical and parametric methods, with RF providing the best performance (RMSE = 7.4%, MAE = 4.8%, R² = 0.9814), followed by GPR and KDE. Additionally, Weibull analysis yielded parameters of k = 1.887 and c = 7.875 m/s, while the largest prediction errors were concentrated within the transition region between 4 and 12 m/s. These findings demonstrate that non-parametric approaches provide a more accurate representation of VAWT power curves under real operating conditions.

Accurate representation of frequency-dependent cable parameters is essential for evaluating power losses and series-voltage effects in medium-voltage wind-farm collection networks. This paper develops a MATLAB-based electromagnetic framework that derives frequency-dependent series-impedance (Z) and shunt-admittance (Y) matrices for underground cables, including conductor skin and proximity effects and earth-return impedance. Four earth-return formulations—Saad–Gaba–Giroux, Wedepohl series, Wedepohl, and infinite-earth—are implemented and benchmarked against RSCAD from 10 Hz to 1 MHz. The AC series parameters evaluated at 60 Hz are applied to a 7.5 MW wind farm in the Isthmus of Tehuantepec, Oaxaca, Mexico, comprising five 1.5 MW turbines connected at 13.8 kV through two radial underground circuits. The Saad–Gaba–Giroux and Wedepohl-series formulations provide the closest agreement with the RSCAD reference. At a power factor of 0.9, the simplified DC core-resistance baseline underestimates active-power losses by 34.35% in Circuit 1 and 42.83% in Circuit 2 relative to the 60 Hz AC series-resistance model. At rated turbine output and the same power factor, the accumulated AC series-voltage indicators at the farthest turbine are 0.17% and 0.38%, compared with 0.11% and 0.20% using core-only DC resistance. These indicators do not represent terminal-voltage magnitudes, which require an AC network power-flow calculation. A component-level analysis for Circuit 1 identifies the core conductor as the dominant contributor to active-power dissipation, followed by the earth-return path and metallic sheath. An annual economic evaluation yields similar project-level financial indicators for the AC and DC representations, while a parametric resistance sensitivity analysis shows decreasing profitability and longer investment recovery periods as AC cable resistance increases. The frequency sweep validates the cable-parameter formulations; their application to electromagnetic-transient simulations remains future work.

Accurate modeling of wind turbine power curves is essential for performance assessment, energy forecasting, condition monitoring, and operational optimization in wind energy systems. This study presents a SCADA-based comparative assessment of established power curve modeling approaches through a single-site case study conducted on a low-power Vertical Axis Wind Turbine (VAWT) operating under real environmental conditions at the University of the Isthmus, located in the Isthmus of Tehuantepec, Oaxaca, Mexico. The evaluated methods included the maximum power curve, an aerodynamic model based on Blade Element Momentum Theory (BEMT), parametric approaches using polynomial and logistic regressions, and non-parametric data-driven methods based on Random Forest (RF), Gaussian Process Regression (GPR), and Kernel Density Estimation (KDE). One year of SCADA data, including wind speed and generated power measurements, was analyzed, while model performance was assessed using RMSE, MAE, and R² metrics. The results showed that the machine learning approaches achieved the lowest prediction errors among the evaluated models, with RF providing the best overall performance (RMSE = 7.4%, MAE = 4.8%, R² = 0.9814), followed by GPR and KDE under the investigated operating conditions. Additionally, Weibull analysis yielded parameters of k = 1.887 and c = 7.875 m/s, while the largest prediction errors were observed within the partial-load operating region (approximately 4–10 m/s) and as the turbine approached the rated operating condition (approximately 10–12 m/s). These findings indicate that, for the investigated low-power VAWT operating at the experimental site, non-parametric approaches provided the most accurate representation of the power curve among the evaluated models, highlighting the potential of SCADA-based data-driven techniques for comparative model assessment under similar operating conditions.