Converters of permanent magnet synchronous generator (PMSG), driven by wind turbines, are controlled by a classical proportional–integral controller. However, many research studies highlighted the challenge in PMSG due to the poor performance of the classical proportional–integral controller, especially in the event of faults or wind speed variations. This article proposes a solution for the limitations of the classical proportional–integral controller with PMSG driven by a wind turbine. The proposed solution includes two optimization techniques: gray wolf optimizer and whale optimizer algorithm. To ensure the effectiveness of the proposed techniques, step change and random variation of wind speed are studied. Moreover, fault ride-through capability of the PMSG is studied with gray wolf optimizer and whale optimizer algorithm techniques during the occurrence of a three-phase fault incident. In this case, a braking chopper controlled by a hysteresis controller is connected to the DC-link capacitor. The simulated results show that compared with the classical proportional–integral controller, gray wolf optimizer and whale optimizer algorithm techniques are greatly efficient in improving the dynamic behavior of the PMSG during wind speed variations. Moreover, gray wolf optimizer and whale optimizer algorithm techniques present their effectiveness during the fault incident by suppressing the transient variations of all the PMSG parameters, improving the fault ride-through capability, and decreasing the total harmonic distortion of the current waveforms. All simulations are performed with MATLAB/ Simulink program package.

Fault ride-through (FRT) capability enhancement for the growth of renewable energy generators has become a crucial issue for their incorporation into the electricity grid to provide secure, reliable, and efficient electricity. This paper presents a new FRT capability scheme for a permanent magnet synchronous generator (PMSG)-based wind energy generation system using a hybrid solution. The hybrid solution is a combination of a braking chopper (BC) and a fuzzy logic controller (FLC). All proportional-integral (PI) controllers which control the generator and grid side converters are replaced with FLC. Moreover, a BC system is connected to the dc link to improve the dynamic response of the PMSG during fault conditions. The PMSG was evaluated on a three-phase fault that occurs on an electrical network in three scenarios. In the first two scenarios, a BC is used with a PI controller and FLC respectively. While the third scenario uses only FLC without a BC. The obtained results showed that the suggested solution can not only enhance the FRT capability of the PMSG but also can diminish the occurrence of hardware systems and reduce their impact on the PMSG system. The simulation tests are performed using MATLAB/SIMULINK software.

Owing to the significant number of hybrid generation systems (HGSs) containing various energy sources, coordination between these sources plays a vital role in preserving frequency stability. In this paper, an adaptive coordination control strategy for renewable energy sources (RESs), an aqua electrolyzer (AE) for hydrogen production, and a fuel cell (FC)-based energy storage system (ESS) is proposed to enhance the frequency stability of an HGS. In the proposed system, the excess energy from RESs is used to power electrolysis via an AE for hydrogen energy storage in FCs. The proposed method is based on a proportional-integral (PI) controller, which is optimally designed using a grey wolf optimization (GWO) algorithm to estimate the surplus energy from RESs (i.e., a proportion of total power generation of RESs: Kn). The studied HGS contains various types of generation systems including a diesel generator, wind turbines, photovoltaic (PV) systems, AE with FCs, and ESSs (e.g., battery and flywheel). The proposed method varies Kn with varying frequency deviation values to obtain the best benefits from RESs, while damping the frequency fluctuations. The proposed method is validated by considering different loading conditions and comparing with other existing studies that consider Kn as a constant value. The simulation results demonstrate that the proposed method, which changes Kn value and subsequently stores the power extracted from the RESs in hydrogen energy storage according to frequency deviation changes, performs better than those that use constant Kn. The statistical analysis for frequency deviation of HGS with the proposed method has the best values and achieves large improvements for minimum, maximum, difference between maximum and minimum, mean, and standard deviation compared to the existing method.