MPPT Algorithm for Photovoltaic Panel Based on Augmented Takagi-Sugeno Fuzzy Model

This paper deals with the Maximum Power Point Tracking (MPPT) for photovoltaic energy system. It includes photovoltaic array panel, DC/DC converter, and load. The operating point for photovoltaic energy system depends on climatic parameters and load. For each temperature and irradiation pair, there exists only one optimal operating point which corresponds to the maximum power transmitted to the load. The photovoltaic energy system is described by nonlinear equations. It is transformed into an augmented system which is described with a Takagi-Sugeno (T-S) fuzzy model. The proposed MPPT algorithm which permits transfering the maximum power from the panel to the load is based on Parallel Distributed Compensation method (PDC). The control parameters have been computed based on Linear Matrix Inequalities tools (LMI). The Lyapunov approach has been used to prove the stability of the system. Some reliable simulation results are provided to check the efficiency of the proposed algorithm. 1. Introduction In the most recent years, photovoltaic (PV) energy has been the subject of several research projects. It is well known that the PV array power panel depends on climatic variables such as temperature and irradiation as shown in Figures 2 and 3. Actually, the operating point of the PV array panel depends on three parameters such as temperature, irradiation, and the load. In fact, the operating point results from the intersection of the I-V characteristic and the load characteristic as shown in Figure 4. In most cases, the value of load is constant and the climatic parameters vary in the day, so the load characteristic remains fixed and the characteristic of the panel varies according to climatic variables. Consequently, the operating point is variable and the load cannot extract maximum power from the panel. To overcome this disadvantage, a DC/DC converter is inserted between the panel and the load. In this way, the load value seen by the PV panel can be changed by varying the duty cycle. In this context, several studies have been developed. Most of papers dealing with the MPPT control algorithms are based on perturb and observe (P&O) [1–4], Incremental Conductance [3, 5], Mamdani type fuzzy logic controller (FLC) [4, 6], and some different approaches as neural network controller (NNC) [7]. In this paper, the PV array panel has been modelled by fuzzy system approach. At every time, the desired state variables have been computed based on the measurement of temperature and irradiation. Also, the MPP tracker algorithm has been developed based on Parallel Distributed