This paper proposes the employment of Static Synchronous Compensator (STATCOM) in reactive power compensation to enhance the Fault Ride-Through (FRT) capability and improve the dynamic performance of a grid-connected PV/wind hybrid power system during the transient grid disturbances. The hybrid power system consisting of 9 MW Doubly Fed Induction Generator (DFIG)-based wind farm and 1 MW PV station is integrated with 100 MVAR STATCOM at the Point of Common Coupling (PCC) bus. The dynamic performance of the PV/wind hybrid power system with the proposed STATCOM controller is analyzed and compared with another FRT control strategy during a grid voltage sag. The FRT control strategy is based on the injection of reactive power from the hybrid system to enhance the FRT capability during the grid faults, and also activation of the outer crowbar protection system to protect the DFIG. On the other hand, the proposed STATCOM controller adjusts the PCC bus voltage during the grid disturbances by dynamically controlling the amount of reactive power injected to or absorbed from the electrical grid. Modeling and simulation of the proposed hybrid power system have been implemented using MATLAB/SIMULINK software. The effectiveness of both the proposed STATCOM controller and the FRT control strategy is evaluated during a 50% grid voltage sag. The simulation results illustrate that the STATCOM controller decreases significantly the level of voltage drop during the voltage sag, maintains the injected active power from the PV station at its rated value, and protects effectively the PV DC-link voltage from overvoltage. Moreover, when the STATCOM controller is employed, the injected active power from the wind farm is improved considerably and the oscillations of the DFIG rotor speed are reduced efficiently during the fault. Furthermore, the comparison confirms the superior dynamic performance of the STATCOM controller in enhancement the FRT capability as compared with the FRT control strategy.

This paper proposes a novel controllable crowbar based on fault type (CBFT) protection technique for doubly fed induction generator (DFIG) wind energy conversion system connected to grid. The studied system consists of six DFIG wind turbines with a capacity of 1.5 MW for each of them. The operation mechanism of proposed technique is used to connect a set of crowbar resistors in different connection ways via activation of controllable circuit breakers (CBs) depending on the detected fault type. For each phase of DFIG, a crowbar resistor is connected in parallel with a controllable CB and all of them are connected in series to grid terminals. The adaptive neuro-fuzzy inference system (ANFIS) networks are designed to detect the fault occurrence, classify the fault type, activate the CBs for crowbar resistors associated with faulted phases during fault period, and deactivate them after fault clearance. The effectiveness of proposed CBFT protection technique is investigated for different fault types such as symmetrical and unsymmetrical faults taking into account the single-phase to ground fault is the most frequently fault type that occurs in power systems. Also, a comparison between the behaviours of studied system in cases of using traditional parallel rotor crowbar, classical outer crowbar, and proposed CBFT protection techniques is studied. The fluctuations of DC-link voltage, active power, and reactive power for studied system equipped with different protection techniques are investigated. Moreover, the impacts of different crowbar resistance values on the accuracy of proposed technique are studied. The simulation results show that, the proposed technique enhances the stability of studied wind turbine generators and contributes in protection of their components during faults.

The industrial revolution in the world resulted in a rise in the desire for energy in recent years. Therefore, the trend was strong toward the utilization of alternative and sustainable energy sources, such as solar power and wind energy. However, the growing prevalence of Distributed Energy Resources (DER) systems in the electricity network, cause several issues and operational restriction violations, including high and low voltage levels exaggerative transmission power dissipation, excess power consumption of distribution transformers and supply lines, protection breakdown, and high harmonic thresholds that are above the thresholds set by international standards. Certain issues arise when the power system’s Hosting Capacity (HC) threshold reaches the system’s hosting capability. In low-voltage distribution systems, the hosting capacity of PV systems is impacted by the Total Harmonic Distortion (THD) and Total Demand Distortion (TDD) levels. In this paper, the enhancement of hosting capacity has been achieved for the solar system in the distribution network. The proposed enhancement deals with a lot of considerations such as the capability current of the line and transformer, over and under voltage at the connection point of the distribution system and the PV system, known as the Point of Common Coupling (PCC), the limitation of total harmonic distortion, and line losses by using a new design of harmonic filter (high passive filter). Modeling of a PV system with grid-connected and passive harmonic filter has been validated based on a real-time Hardware in the Loop (HIL) OPAL-RT 4510 Powered by MATLAB SIMULINK. A harmonic filter design can enable achieving the maximum HC level in a low-voltage distribution grid, according to the study’s findings. The proposed technique offers a minimum THD, and TDD, minimum losses of the transmission line, maximum Power factor (PF) of the system, and improved power quality of the distribution network. In addition, the proposed technique has been compared to the previous technique.

To improve the performance of recent perturb and observe (P&O) maximum power point tracking (MPPT) algorithms, an intelligent and robust P&O (IR-PO) algorithm is investigated for variable-speed wind energy conversion systems (WECSs). The proposed IR-PO algorithm reduces the search area for the maximum power point (MPP) by using the optimal hypothetical position of generator speed. Initially, the proposed IR-PO algorithm assesses the optimal generator speed via a simple wind speed estimation (WSE) algorithm which adopts a considerable miscalculation. So, the specified optimal generator speed is not accurate which in turn influences the overall dynamic performance. Hence, the proposed IR-PO algorithm assumes that the operation point of the estimated optimal generator speed is hypothetical, and from this virtual point it begins to search for the real point of the optimal generator speed. Below the optimal hypothetical position, the large adaptive step-sizes are established to rapidly perturb the generator speed to the optimal position and tightens the searching area of the MPP. Otherwise, very small adaptive step-sizes are applied. The proposed IR-PO algorithm not only rapidly hunts the MPP with low-speed fluctuations around the MPP, but also it evades the estimation error of both wind and generator speeds. To verify the superiority of the proposed IR-PO algorithm, it is compared to other MPPT algorithms. As a result of the proposed IR-PO algorithm, the smallest fluctuations and settling time around the MPP with a 4.42% increase in the system efficiency, are accomplished. Furthermore, the performance assessment of the proposed IR-PO algorithm is confirmed by real wind data (Hokkaido-Island, Japan) using MATLAB/SIMULINK.

This paper describes the stability improvement of grid-connected photovoltaic (PV) system using supercapacitor (SC). The proposed technique is applied on PV system which using maximum power point tracking of perturbation and observation (P&O) algorithm. The P&O algorithm is used to extract the maximum power from the studied PV system. The effect of SC as a type of storage device on PV system appears at the abnormal conditions of grid faults. This technique is used to compensate the system parameters at different fault conditions. The control strategy of SC technique is developed for continuously charging and discharging to achieve its objective. In addition, the control system is presented in order to maintain the DC link voltage and to manage the power transit between the SC and the grid. The whole grid-connected PV system with SC energy storage system (ESS) is modeled and simulated using MATLAB/SIMULINK software and also the control system strategies are demonstrated. The effects of different fault conditions on the behaviors of studied grid-connected PV system with the SC ESS technique are investigated. A comparison between the behaviors of studied PV system in cases of without SC and with SC technique is studied. Also, the variations of DC voltage, active power, and reactive power for the studied PV system equipped without and with SC technique are investigated. The simulation results present a clear improvement for the system stability in case of using SC. Numerical results illustrate the validity of proposed technique with satisfactory values.

Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM.