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An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities

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  • Darvish Falehi, Ali

Abstract

Doubly Fed Induction Generator (DFIG) with consideration of its exceptional capabilities, i.e.: variable speed operation, low mechanical stresses, and excellent power quality in limited-range speed applications is a famous kind of wind turbines. Even so, there is a challenge due to its nonlinear dynamic features and several uncertainties like unknown non-linear disturbances and parameter uncertainties. Hence, this paper proposes a novel Robust Perturbation Observer based Fractional Order Sliding Mode Controller (RPO-FOSMC) for DFIG to extract the maximum power and improve the Fault Ride-Through (FRT) capability. The strong nonlinear aerodynamics of wind turbine, the uncertain dynamic parameters of induction generator and the stochastic characteristics of wind waves are constructed in perturbation term which is estimated using the proposed RPO-FOSMC. Accordingly, the perturbation compensator provides an appropriate robustness concerning different uncertain models and attains an exceptional control capability during stochastic wind waves. Considering the inherent multi-objective nature of the nonlinear control design problem, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) has been implemented to augment the robustness and dynamic performance of RPO-FOSMC. Three distinct conditions are considered to compare and analyse the fast and robust dynamic performance of optimal RPO-FOSMC against other conventional approaches. Eventually, the comprehensive simulation results have revealed and validated the exceptional dynamic capability of the suggested control strategy.

Suggested Citation

  • Darvish Falehi, Ali, 2020. "An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    • Handle: RePEc:eee:chsofr:v:130:y:2020:i:c:s096007791930342x
    DOI: 10.1016/j.chaos.2019.109407
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    Cited by:

    1. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    2. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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