The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon.

Frequency stability analysis of large power systems are extremely time consuming, laborious and may even exceed the computational capacity of modern computers. Hence, simplified power system models have being developed in the literature. These models are usually called System Frequency Response (SFR). In SFR models, generators are represented by transfer functions, nonlinearities are generally neglected and the grid is not taken into account. Conventional SFR models only contemplate the mechanical behavior of speed governors, turbines and synchronous machines of generators. This is because, a common simplification is to consider that frequency and voltage can be controlled independently. However, it is demonstrated that there is an interaction between them, so frequency can be affected by the effect of power system stabilizers (PSSs) over excitation system controllers. In this work, a modified SFR model is proposed, considering the influence of generators excitation control on frequency. Simulation results show an improvement of the accuracy in the estimation of frequency response of the power system.

La incorporación de potencia eólica en microredes está creciendo progresivamente. Esta integración puede introducir problemas en la dinámica y calidad de potencia del sistema eléctrico debido a fluctuaciones del viento. Este trabajo propone un controlador compuesto por un sistema de acondicionamiento de potencia (PCS), un almacenador flywheel (FESS) y una batería de flujo redox de vanadio (VRFB) para mitigar problemas introducidos por la generación eólica en microredes. Se presenta un modelo del PCS/FESS-VRFB y se propone un método para controlar la potencia intercambiada entre el controlador y la red eléctrica. El método tiene dos modos: nivelación de potencia y control de frecuencia. A través de simulaciones, se estudia el desempeño del PCS/FESS-VRFB cuando opera con generación eólica en microredes. Los resultados demuestran un adecuado desempeño del método propuesto y una alta efectividad para nivelar las fluctuaciones de potencia eólica y proveer soporte para el control de frecuencia de la microred.

Fault detection is one of the most common studies on wind turbines. In this case, the doubly fed induction generator (DFIG) is specifically analyzed. The fault cases analyzed are: inter-turn short circuit and open circuit, in addition to normal operating conditions. The K-means algorithm was used for analysis and classification. The data set is obtained from multiple simulations in MATLAB/Simulink, in which the stator resistance (Rs) and stator inductance (Ls) were varied. From these simulations, the current and voltage signals are processed using tools such as the Park transform, stator current imbalance, and harmonic analysis to obtain relevant characteristics for the classification of each case. It is known that Rs is a determining parameter in fault detection: in a short circuit, Rs tends to fall below its nominal value due to the appearance of a low-impedance path, while in an open circuit, Rs tends to rise above the nominal value due to the interruption of the conductor. The results obtained indicate that the K-means algorithm, together with the proposed methodology, are efficient in classifying the different stator states. This suggests that the proposed solution, based on the results, could be effective and economical for wind turbine monitoring.