The ability to model a system with high accuracy plays an important role in finite-control-set model-predictive-control (FCS-MPC)-controlled LCL-interfaced grid-connected converters (LCL-GCCs). However, the effect of aging, unmeasured noise, and temperature change on LCL-GCCs may result in parameter perturbations between the prediction model and the actual system. A model mismatch may occur, which may lead to violations of constraints, worsen the power quality of the grid current, and even threaten the system stability. This paper presents a novel nature-inspired optimization paradigm named Moth-Flame-Optimization (MFO), which applies the spiral logarithmic function to simulate the flight of a moth approaching a flame. The method is designed to efficiently identify and update the model parameters, and the fitness function for the state variables is designed and solved iteratively to minimize mismatches with the model. The advantages of the proposed method are its fast convergence and ability to determine parameters with high accuracy. These advantages effectively prevent the algorithm from converging to local optima. To achieve the harmonic rejection capability, a sliding discrete Fourier transform (SDFT) algorithm is also proposed to predict the harmonic at each sampling interval, thus the harmonics are considered in the cost function. Experimental comparisons under different scenarios validate the effectiveness of the proposed SDFT based MFO-MPC method.
|Publicación||IEEE Journal of Emerging and Selected Topics in Power Electronics|
|Estado||En prensa - 2022|
Áreas temáticas de ASJC Scopus
- Ingeniería energética y tecnologías de la energía
- Ingeniería eléctrica y electrónica