High penetration of wind power plants may have an adverse impact on power systems’ stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. However, most recent grid codes include an emulation inertia requirement for wind power plants, because modern wind turbines are capable of providing virtual inertia through power electronic converter controls to improve frequency stability issues. Because of this, it is necessary that the inertia estimation analyze and quantify the impact of the inertia reduction in power systems. In this paper, an implementation of a methodology for the inertia estimation of wind power plants is presented. It is evaluated through synchrophasor measurements obtained from a Real-Time Digital Simulator (RTDS) implementation, using industrial Phasor Measurement Units (PMUs). This methodology is based on the swing equation. Furthermore, a comparison of the results obtained between two professional tools RSCAD and DIgSILENT PowerFactory is performed, in order to evaluate the accuracy and the robustness of the methodology. This methodology is applied for the inertia estimation of an equivalent of the southeast zone of the Mexican power system, where there is a large-scale penetration of wind power plants. The results demonstrate that this methodology can be applied in real power systems using PMUs.

This paper presents the application of Newton-based methods in the time-domain for the computation of the periodic steady state solutions of microgrids with multiple distributed generation units, harmonic stability, and power quality analysis. Explicit representation of the commutation process of the power electronic converters and closed-loop power management strategies are fully considered. Case studies under different operating scenarios are presented: grid-connected mode, islanded mode, variations in the Thevenin equivalent of the grid and the loads. Besides, the close relation between the harmonic distortion, steady state performance of the control systems, asymptotic stability, and power quality is analyzed in order to evaluate the importance and necessity of using full models in stressed and harmonic distorted scenarios.

This contribution presents the application of genetic programming to the problem of time series forecasting. This forecast technique is applied to wind speed time series. The results obtained from the forecasting are used to determine the power generation capacity of a fixed-speed wind turbine, which includes a squirrel cage induction generator. The forecast values obtained with the genetic programming are compared against the original time series data in order to show the precision of this forecast technique.