Abstract
In the last two decades, the maximum power point tracking (MPPT) methods for PV system is becoming very interesting subject. Among these methods there is Fuzzy-MPPT, which is mainly based on two inputs and at least 25 rules. This structure need more calculation time and not easy to be implemented in hardware. For these reasons and in order to facilitate the MPPT synthesis, this paper proposes an intelligent MPPT controller based on a single-input Takagi–Sugeno fuzzy logic controller (SI-TS-FLC) with three linguistic variables and three rules. The proposed controller is simulated using MATLAB-SIMULINK for a PV system, which consists of a PV generator, DC-DC converter, and resistive load under varied temperature and irradiance levels. An experimental study is conducted using DSPACE 1104 card real-time board under partial shading condition. Simulation and experimental results show that the proposed controller exhibits less settling time and lower overshoot than the commonly used perturb and observe (P&O) algorithm in the transient state and minimum oscillation around the optimal operating point.DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21476
Highlights
Environment pollution issues, climate change, cannot be neglected
In [20], a new approach was developed based on a single-input fuzzy logic controller (FLC) for maximum power point tracking (MPPT) to reduce the controller complexity; the input of the fuzzy controller is the signed perpendicular distance ds calculated from error (E) and the change of error (CE) of conventional FLC
An intelligent control technique of a single input T-S FLC is proposed for the MPP tracking of a PV system with DCDC converter and resistive load
Summary
Environment pollution issues, climate change, cannot be neglected. To eliminate oscillation around MPP, authors in [17] proposed an MPPT control algorithm based on FLC technique with two inputs and 49 membership function rules. In [20], a new approach was developed based on a single-input FLC for MPPT to reduce the controller complexity; the input of the fuzzy controller is the signed perpendicular distance ds calculated from error (E) and the change of error (CE) of conventional FLC. This algorithm uses more than three normalized membership function rules. The proposed approach is validated by simulation under various conditions of temperature and irradiance and through experiment under partial shading condition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
