A Novel Converter-Based PV Emulator Control Using Lambert W Method and Fractional-Order Fuzzy Proportional-Integral Controller Trained by Harris Hawks Optimization

Photovoltaic (PV) emulators are designed to reproduce the exact current-voltage characteristic of a PV module or array in different ambient conditions. PV emulators are essential for power converter testing in PV systems, particularly for grid-tied inverters. Certain factors, such as real-time output and simple implementation with high accuracy, should be considered when designing a PV emulator. Conventional PV emulators utilize traditional PV mathematical models and controllers, like the Newton-Raphson (NR) method and PI controllers, respectively. This matter makes a PV emulator unable to reproduce the curve in different conditions because the PI controller is unable to tolerate changing operating points while drastically preserving robust performance with those alterations. Also, NR requires considerable iterations, leading to an intense computational burden. Accordingly, this paper proposes a more effective and simple control methodology to optimize the performance of converter-based PV emulators. In this regard, the PV model is based on the single-diode model with five unknown parameters while considering a simple buck converter. This paper utilizes the Lambert W method to solve the exponential equation of the PV model, thus changing an “ implicit ” function into an “ explicit ” one. It also reduces the number of iterations; therefore, the computational burden is diminished while maintaining the performance and accuracy of converter-based PV emulators. Also, this paper introduces a novel adaptive intelligent fractional-order controller to control converter-based PV emulators. In this strategy, the proposed fuzzy inference system obtains the three coefficients of the controller using Harris hawks optimization and gradient descent algorithm. With an adaptation of the three controller parameters, the performance of the converter-based PV emulator is improved by reducing the tracking error while inducing closed-loop system stability. Comparative simulations and experimental results reveal the effectiveness of the proposed control methodology.