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Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547x10-4 , 7.64801x10-4 , and 7.59756 x10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

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Integrating wind power plants (WPPs) into power systems are increasing dramatically now a day. However, the dynamic performance of power systems will be affected by the large penetration level of such renewable sources of energy. From this context power system operators and transmission system operators have put regulation rules to keep pushing wind power plants to safeguard limits that keep power system more stable and reliable. One of these rules is providing a low voltage ride through (LVRT) for wind farms without disconnecting it from the power system. The current paper implements the STATCOM as a LVRT for a 9 MW wind farm connected to the grid through transmission system of 120 kV. For enhancing the dynamic performance of STATCOM, two types of optimization methodologies: ant colony (ACO) and particle swarm optimization (PSO), are proposed to fine tune the coefficients of PI controllers to optimally manage the STATCOM dynamics.

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The present paper aims to introduce an effective control system which enhances the dynamics of a doubly fed induction generator (DFIG) operating at fixed and variable speeds. To visualize the effectiveness of the formulated control algorithm, the performance of the DFIG is evaluated using other control techniques as well. Each control algorithm is primarily described by showing its operation principles and how it is adapted to manage the DFIG’s operation. The main used control strategies are stator voltage-oriented control (SVOC), model predictive current control (MPCC), model predictive direct torque control (MPDTC), and the formulated predictive voltage control (PVC) algorithm. A detailed comparison is performed between the controllers’ performances, through which the advantages and shortcomings of each method are outlined, and finally, the most effective technique is identified amongst them. The obtained results reveal that the proposed PVC approach possesses multiple advantages such as a faster dynamic response and simpler control structure when compared with SVOC and a faster dynamic response, reduced ripples, and reduced computational burdens when compared with the MPCC and MPDTC approaches. In addition, the robustness of the proposed PVC scheme is confirmed by performing extensive performance evaluation tests considering the parameters’ variation.

The paper introduces a cost effective predictive flux control (PFC) approach for a sensorless doubly fed induction generator (DFIG). The base operation of the proposed PFC depends on controlling the rotor flux (α-β) components using a cost function which is derived through analyzing the relationship between the developed torque and the angular slip frequency. To improve the rotor flux estimation and prediction, an effective rotor flux observer is proposed. A robust rotor position estimator is proposed to guarantee a precise coordinate transformation. In order to save the cost, only one rotor current sensor is utilized to evaluate the rotor currents. The finite control set (FCS) principle is utilized to select the voltage vectors which enables the elimination of the pulse width modulation (PWM). To validate the feasibility of the proposed sensorless PFC approach, a comprehensive comparison is carried out between the proposed sensorless PFC and the predictive torque control (PTC) for the DFIG. The obtained results confirm and emphasize the superiority of the proposed PFC in achieving the control objectives with lower ripples content and less computational burdens. Moreover, the effectiveness of the rotor position and rotor flux estimators has been confirmed through the obtained results. INDEX TERMS Doubly fed induction generator (DFIG), predictive control, flux control, torque control, sensorless, flux estimation, current estimation, finite control set (FCS).

The present paper is concerned with introducing an effective direct power control (DPC) approach for a sensor-less doubly fed induction generator (DFIG). The derivation of the proposed DPC approach is performed in a systematic manner in which the design of the rotor current controllers is well analyzed, which clarifies the real base of the control system as when and why it works properly. The operation of the proposed DPC approach is based on the stator voltage-oriented control principle in which the stator voltage is aligned with the quadrature axis of the rotating reference frame. To obtain maximum generation efficiency, the reactive power reference value is derived based on a loss minimization criterion (LMC) that is described and analyzed in detail. To enhance the robustness of the control system, an effective rotor position estimator is proposed that is robust against the system uncertainties, such as the parameters’ variation. To validate the effectiveness of the proposed sensor-less DPC approach, the DFIG dynamic performance is tested for a wide range of operating speeds. The obtained results confirm and emphasize the feasibility of the proposed control approach and its LMC methodology in improving the generation efficiency and in obtaining high dynamic performance from the DFIG.