Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

This paper examines the efficiency of a fuzzy logic control (FLC) based maximum power point tracking (MPPT) of a photovoltaic (PV) system under variable climate conditions and connected load requirements. The PV system including a PV module BP SX150S, buckboost DC-DC converter, MPPT, and a resistive load is modeled and simulated using Matlab/Simulink package. In order to compare the performance of FLC-based MPPT controller with the conventional perturb and observe (P&O) method at different irradiation (G), temperature (T) and connected load (RL) variations -rising time (tr), recovering time

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“…The FLC is used to convert the human information to the rule-based model that can control a plant with linguistic explanations [12,13].…”

Section: Fuzzy Logic Control (Flc) Methodsmentioning

confidence: 99%

Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic

Al-Gizi¹,

Al-Chlaihawi²,

Crăciunescu³

2017

Adv. sci. technol. eng. syst. j.

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“…The fuzzy logic controller for the MPPT has two real time inputs measured at every sampling time, named 'E' and 'CE' and one output named 'Duty cycle (DC)' for each of the phases [21,22]. The error at sample time k is calculated by [20,23,24].…”

Section: Fuzzy Logic Control For Mpptmentioning

confidence: 99%

Optimization of photovoltaic power system: a comparative study

Дабра

Paliwal

Sharma

et al. 2017

Prot Control Mod Power Syst

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This paper presents a comparative study of P&O, fuzzy P&O and BPSO fuzzy P&O control methods by using MATLAB software for optimizing the power output of the solar PV grid array. The voltage, power output and the duty cycle of the solar PV array are well presented and analyzed with an algorithm. The model consists of 66 PV Cells connected parallel and 5 PV cells connected in series to make solar PV array. The BPSO Fuzzy method generates 43.4820 MW output power more than P&O method and 150 KW more than P&O fuzzy method. This also shows that the time response of the photovoltaic system reduces to perturbations and insures the continuity of the operation at the time in response to the continued maximum power point. It also eliminates the fluctuations around MPPT. Simulation results also revealed that BPSO fuzzy P&O controller is more effective as compare with P&O and fuzzy P&O models.

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“…The control problem is to find a suitable control law so the PV output voltage and current position can track specific maximum power point trom V vs. P characteristic curve of the photovoltaic (reference) trajectories. Defme a tracking error as (15) The fr rst step of sliding-mode control design is to select a sliding surface that models the desired closed loop performance in state variable space. Then design the control such that the system state trajectories are forced toward the sliding surface and stay on it.…”

Section: Modeling Of the Cuk Convertermentioning

confidence: 99%

“…Application of Adaptive Neural Fuzzy Inference System (ANFIS) to build MPPT system by detecting non linear characteristics of photovoltaic current and voltage at Total Cross Tied (TCT) point [9 -14], related to power absorption and its controlling [15], and others.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking control for stand-alone photovoltaic system using fuzzy sliding mode control

Rajagukguk

Ashari

Riawan

2014

2014 the 1st International Conference on Information Technology, Computer, and Electrical Engineering

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Photovoltaic (PV) technology, as one of renewable energy resource with green technology characteristic, has a great chance to develop in Indonesia since this country has tropical climate. This paper proposed the other method to control of maximum power point tracker for stand-alone photovoltaic system using fuzzy sliding mode control. A DC-DC Cuk converter was chosen to regulate the DC voltage from the PV module. To obtain optimum PV power, duty cycle of Cuk converter was controlled by the 'MPPT_Fuzzy Sliding Mode (MPPT_FSM) controller. PV voltage and current values at the Maximum Power Point (MPP) point, as controller references, were obtained using Perturb and Observe algorithm. It can be concluded from the simulation results that by using MPPT_FSM controller, Cuk converter was able to optimize the PV power, in accordance with its MPP point at V vs. P characteristic curve. In constant load tests, with the input radiation and temperature constant and varied, it was seen that the MPPT_FSM controller was capable to eliminate overshot and reduce oscillations (harmonics) due to the ripple factor of 60%.

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Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

Cited by 57 publications

References 3 publications

Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic

Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic

Optimization of photovoltaic power system: a comparative study

Maximum Power Point Tracking control for stand-alone photovoltaic system using fuzzy sliding mode control

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