Nonlinear control of WECS based on PMSG for optimal power extraction

This paper proposes a robust control strategy for optimizing the maximum power captured in Wind Energy Conversion Systems (WECS) based on permanent magnet synchronous generators (PMSG), which is integrated into the grid. In order to achieve the maximum power point (MPPT) the machine side converter regulates the rotational speed of the PMSG to track the optimal speed. To evaluate the performance and effectiveness of the proposed controller, a comparative study between the IBC control and the vector control based on PI controller was carried out through computer simulation. This analysis consists of two case studies including stochastic variation in wind speed and step change in wind speed

DFIG use with combined strategy in case of failure of wind farm

In the wind power area, Doubly Fed Induction Generator (DFIG) has many advantages due to its ability to provide power to voltage and constant frequency during rotor speed changes, which provides better wind capture as compared to fixed speed wind turbines (WTs). The high sensitivity of the DFIG towards electrical faults brings up many challenges in terms of compliance with requirements imposed by the operators of electrical networks. Indeed, in case of a fault in the network, wind power stations are switched off automatically to avoid damage in wind turbines, but now the network connection requirements impose stricter regulations on wind farms in particular in terms of Low Voltage Ride through (LVRT), and network support capabilities. In order to comply with these codes, it is crucial for wind turbines to redesign advanced control, for which wind turbines must, when detecting an abnormal voltage, stay connected to provide reactive power ensuring a safe and reliable operation of the network during and after the fault. The objective of this work is to offer solutions that enable wind turbines remain connected generators, after such a significant voltage drop. We managed to make an improvement of classical control, whose effectiveness has been verified for low voltage dips. For voltage descents, we proposed protection devices as the Stator Damping Resistance (SDR) and the CROWBAR. Finally, we developed a strategy of combining the solutions, and depending on the depth of the sag, the choice of the optimal solution is performed

Enhancing battery capacity estimation accuracy using the bald eagle search algorithm

Accurate assessment of metrics such as state of health (SOH) is of paramount importance in effective battery management systems (BMS), given the propensity of batteries to experience capacity degradation with aging. This research endeavors to significantly enhance the precision of battery capacity estimation by effectively mitigating the inherent uncertainties associated with state of charge (SOC) estimation and measurement. To address this challenge, we introduce an innovative approach leveraging the bald eagle search algorithm (BES), a method inspired by the systematic hunting behavior of bald eagles. BES strategically navigates the search space, identifying and selecting promising solutions through fitness evaluations. Our principal aim, utilizing the inherent capabilities of BES, is to pinpoint the optimal candidate that minimizes a designated cost function, while ensuring real-time cell capacity updates facilitated by the incorporation of a memory forgetting factor. The distinctiveness of this study is twofold: firstly, the strategic integration of the BES algorithm within the context of battery capacity optimization, and secondly, the inclusion of a memory forgetting factor to enhance real-time capacity estimations. The efficacy of our approach is rigorously substantiated through validation using NASA’s Prognostic Data, along with three battery scenarios for plug-in hybrid and electric vehicles. BES consistently outperformed four aggressive algorithms, demonstrating heightened accuracy with a peak error rate of only 1.06% in the most demanding scenario. Furthermore, the predictive performance measures remained consistently below 0.41%, underscoring the robustness of our proposed methodology