Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Keçecioğlu, Ö. Fatih

doi:10.1002/jnm.2994

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…As a result, when the light condition changes rapidly, the variable-step P&O and BOA tracking can easily fall into the local optimal situation and cannot find the global optimal power point, while MBOA-P&O can quickly adapt to the environmental changes and can quickly and accurately find the global optimal value for the next working condition. [13][14]…”

Section: Composite Control Strategymentioning

confidence: 99%

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

2023

jeis

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Photovoltaic power generation has attracted more and more attention in the field of new energy applications, and the maximum power point tracking technology is the critical link of the photovoltaic power generation system. In the case of partial shading, the output power curve of the photovoltaic array presents a multi-peak phenomenon, and the traditional MPPT algorithm is easy to fall into the optimal local solution when tracking the maximum power, while the traditional butterfly algorithm has slow convergence and low optimization accuracy in the tracking process. In order to reduce the loss of output power of photovoltaic system, an MPPT control method combining butterfly algorithm with adaptive inertia weight and variable step size disturbance observation method is proposed. In the butterfly algorithm, the population randomly generates the initial solution, and the crossover mutation operation is carried out on the population. The inertia weight is constantly updated with the increase of iteration times, which can reduce the oscillation amplitude in the tracking process, increase the robustness of the algorithm search, and achieve the purpose of global search. Then, the perturbation observation method with variable step size is used to accelerate the convergence speed and accuracy. The simulation results show that compared with the traditional perturbation observation method and butterfly optimization algorithm, the proposed algorithm can find the maximum power point stably, quickly, and accurately in the case of sudden illumination change, which significantly improves the performance of MPPT.

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Section: Composite Control Strategymentioning

confidence: 99%

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

2023

jeis

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“…

{\overset{true¯}{μ}}_{{\overset{true˜}{A}}_{j}^{i}}

and

{\underset{true¯}{μ}}_{{\overset{true˜}{A}}_{j}^{i}}

j = 1,2 indicate the UMF and LMF of the rule i , respectively. Normalization process is performed by proportioning the firing strength of each node rule to the sum of the firing strengths of all rules 48,49 . This process is given in the following equations:

{\overset{true¯}{F}}^{i} goodbreak= \frac{{\overset{f}{true}}^{i}}{\sum {\overset{true¯}{f}}^{i}},

{\underset{true¯}{F}}^{i} goodbreak= \frac{\underset{true¯}{f_{i}}}{\sum \underset{f_{i}}{true}},

…”

Section: Design Of Proposed Control Structurementioning

confidence: 99%

“…Normalization process is performed by proportioning the firing strength of each node rule to the sum of the firing strengths of all rules. 48,49 This process is given in the following equations:…”

Section: F I G U R E 1 the Controlled Power Block Between Pv Generato...mentioning

confidence: 99%

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

Ganı

Şekkeli

2022

Circuit Theory & Apps

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The conventional angle of incremental conductance (AIC) method fails to track the maximum power point (MPP) when a rapid change in solar irradiation and/or panel temperature appears because it cannot distinguish between rapid changes in photovoltaic (PV) current and voltage under real environmental conditions. The present study describes setbacks of the conventional AIC method and proposes an effective approach based on the combination of AIC and type‐2 fuzzy. The proposed approach offers a significant advantage in terms of maximum power transfer to the load under long‐term weather patterns under which broken clouds appear frequently during a 40‐m testing period. The proposed approach is installed on 250 W module of a 10 kWp real PV plant in Kahramanmaras Sutcu Imam University. Later, its performance is validated by dSPACE1104 controller card operating in real time with MATLAB/Simulink tools using daily measured data. The experimental results obtained from the laboratory setup based on dSPACE1104 controller card demonstrate the facility and feasibility of the proposed approach for real PV plant with boost power converter and load.

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PD-Type-2 Fuzzy Neural Network Based Control of a Super-Lift Luo Converter Designed for Sustainable Future Energy Applications

Ganı

2023

Lecture Notes in Networks and Systems

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Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Cited by 3 publications

References 42 publications

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

PD-Type-2 Fuzzy Neural Network Based Control of a Super-Lift Luo Converter Designed for Sustainable Future Energy Applications

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