A developed control strategy for mitigating wind power generation transients using superconducting magnetic energy storage with reactive power support

• Developing WECS and SMES models.
• VSC control to keep the voltage within a preset limit.
• SMES active/reactive power control strategy.
• Developing fuzzy logic controller.
• Minimizing the impacts of wind gusts.

The fast variations of wind speed during extreme wind gusts result in fluctuations in both generated power and the voltage of power systems connected to wind energy conversion system (WECS). This paper presents a control strategy which has been tested out using two scenarios of wind gusts. The strategy is based on active and reactive powers controls of superconducting magnetic energy storage (SMES). The WECS includes squirrel cage induction generator (SCIG) with shunt connected capacitor bank to improve the power factor. The SMES system consists of step down transformer, power conditioning unit, DC–DC chopper, and large inductance superconducting coil. The WECS and SMES are connected at the point of common coupling (PCC). Fuzzy logic controller (FLC) is used with the DC–DC chopper to control the power transfer between the grid and SMES coil. The FLC is designed so that the SMES can absorb/deliver active power from/to the power system. Moreover, reactive power is controlled to regulate the voltage profile of PCC. Two inputs are applied to the FLC; the wind speed and SMES current to control the amount active and reactive power generated by SMES. The proposed strategy is simulated in MATLAB/Simulink®. The proposed control strategy of SMES is robust, as it successfully controlled the PCC voltage, active and reactive powers during normal wind speeds and for different scenarios of wind gusts. The PCC voltage was regulated at 1.0 pu for the two studied scenarios of wind gusts. The fluctuation ranges of real power delivered to the grid were decreased by 53.1% for Scenario #1 and 56.53% for Scenario #2. The average reactive power supplied by the grid to the wind farm were decreased by 27.45% for Scenario #1 and 31.13% for Scenario #2.