T-S Fuzzy Maximum Power Point Tracking Control of Solar Power Generation Systems

Abstract

This paper presents maximum power point tracking (MPPT) control for stand-alone solar power generation systems via the Takagi-Sugeno (T-S) fuzzy-model-based approach. In detail, we consider a dc/dc buck converter to regulate the output power of the photovoltaic panel array. First, the system is represented by the T-S fuzzy model. Next, in order to reduce the number of measured signals, a T-S fuzzy observer is developed for state feedback. Then, a fuzzy direct MPPT controller is proposed to achieve asymptotic MPPT control, in which the observer and controller gains are obtained by separately solving two sets of linear matrix inequalities. Different from the traditional MPPT approaches, the proposed T-S fuzzy controller directly drives the system to the maximum power point without searching the maximum power point and measuring insolation. Furthermore, when considering disturbance and uncertainty, robust MPPT is guaranteed by advanced gain design. Therefore, the proposed method provides an easier implementation form under strict stability analysis. Finally, the control performance is shown from the numerical simulation and experimental results. Index Terms-Linear matrix inequalities (LMIs), maximum power point tracking (MPPT), photovoltaic (PV) array, TakagiSugeno (T-S) fuzzy model.

Double-Linear Approximation Algorithm to Achieve Maximum-Power-Point Tracking for Photovoltaic Arrays

“…To draw maximum power from PV panel, a large number of researchers have proposed maximum power point tracking (MPPT) algorithms. Current MPPT algorithms include the voltage feedback method (VFM) [1], the power feedback method (PFM) [2][3][4], the perturb-and-observe method (POM) [5][6][7], the incremental conductance method (ICM) [8,9], the three-point weight comparison method (TPWCM) [10,11], the linear approximation method (LAM) [12], the neural-network-based method (NNBM) [13], and the fuzzy-logic-control approach (FLCA) [14,15].…”

Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

“…Since the information of MPP should be obtained in prior, these methods are not suitable for practical implementation. The solar insolation and panel temperature are applied as FLC inputs in [32][33][34][35][36][37][38][39]; however, most of the small PV systems are not equipped with these sensors, and hence these methods are not suitable for low cost PGS.…”

Power maximization using multiple step, load-side, current-mode sensing

“…Maximum power point tracking has attracted increasing attention in research over the last several years. There are a number of MPPT methods with different complexity, speed, cost and number of required sensors [1][2][3][4][5][6].…”