Lv Raoying, Ahmad Bhat Rayees
School of Civil Engineering Architecture, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang322100, China.
Department of Tourism & Hospitality, Bhagwant University, Ajmer, India.
Heliyon. 2024 Jul 2;10(14):e33942. doi: 10.1016/j.heliyon.2024.e33942. eCollection 2024 Jul 30.
In this study, the use of an Unscented Kalman Filter as an indicator in predictive current control (PCC) for a wind energy conversion system (WECS) that employs a permanent magnetic synchronous generator (PMSG) and a superconducting magnetic energy storage (SMES) system connected to the main power grid is presented. The suggested UKF indication in the hybrid WECS-SMES arrangement is in charge of estimating vital metrics such as stator currents, electromagnetic torque, rotor angle, and rotor angular speed. To optimize control strategies, PCCs use these projected properties rather than direct observations. To control the unpredictable wind energy's nature, SMES must be regulated to minimize fluctuations in the DC-link voltage and power output to the main grid. Fractional order-PI (FOPI) controllers are used in a novel control structure for the SMES system to regulate the output power and DC-link voltage. An artificial bee colony optimization approach is employed to optimize the FOPI controllers. Three commonly utilized indicators, including sliding-mode, EKF, and Luenberger, were evaluated using "MATLAB" to evaluate the performance of the UKF estimate. Assessment criteria such as mean absolute percentage error and root mean squared error were used to gauge the accuracy of the estimates. Simulation findings showed the efficiency of fractional order-PI controllers for SMES and the proposed UKF indication for predictive current control, especially in the presence of measurement noise and over a variety of wind speeds. An improvement in estimation accuracy of up to 99.9 % was demonstrated by the UKF indicator. Moreover, the stability of the suggested UKF-based PCC control for the hybrid WECS-SMES combination was confirmed using Lyapunov stability criteria."
在本研究中，提出了将无迹卡尔曼滤波器（Unscented Kalman Filter）用作预测电流控制（PCC）中的一个指标，该预测电流控制用于一个风能转换系统（WECS），此系统采用永磁同步发电机（PMSG）以及连接到主电网的超导磁储能（SMES）系统。在混合式WECS - SMES装置中，所建议的无迹卡尔曼滤波器指标负责估计诸如定子电流、电磁转矩、转子角度和转子角速度等关键指标。为了优化控制策略，预测电流控制使用这些预测特性而非直接测量值。为了控制不可预测的风能特性，必须对超导磁储能进行调节，以最小化直流母线电压波动以及向主电网的功率输出波动。分数阶比例积分（FOPI）控制器被用于超导磁储能系统的一种新型控制结构中，以调节输出功率和直流母线电压。采用人工蜂群优化方法对分数阶比例积分控制器进行优化。使用“MATLAB”对包括滑模、扩展卡尔曼滤波器（EKF）和鲁棒观测器（Luenberger）在内的三个常用指标进行评估，以评价无迹卡尔曼滤波器估计的性能。使用平均绝对百分比误差和均方根误差等评估标准来衡量估计的准确性。仿真结果表明了分数阶比例积分控制器对超导磁储能的有效性以及所提出的用于预测电流控制的无迹卡尔曼滤波器指标的有效性，特别是在存在测量噪声以及多种风速情况下。无迹卡尔曼滤波器指标显示估计精度提高了高达99.9%。此外，使用李雅普诺夫稳定性准则证实了所建议的基于无迹卡尔曼滤波器的预测电流控制对于混合式WECS - SMES组合的稳定性。
