Analysis and Evaluation Performance of MPPT Algorithms: Perturb &amp; Observe (P&amp;O), Firefly, and Flower Pollination (FPA) in Smart Microgrid Solar Panel Systems

Suyanto, Suyanto; Mohammad, Luthfansyah; Setiadi, Iwan Cony; Roekmono, Roekmono

doi:10.1109/ieeeconf48524.2019.9102532

Cited by 12 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a stand-alone PV system, Metaheuristic techniques are used to optimize FLC [22]. The PV efficiency is further improved by the research work on Firefly Optimization Algorithm [23][24][25][26], Gray Wolf Algorithm [27][28][29], Grasshopper Algorithm [30], Ant Colony Optimization algorithm [30], Genetic Optimization Algorithm [32,33] and many other algorithms, optimization techniques are also used in the optimal selection of FLC parameters [34]. Ultra-innovative algorithms for determining the output step size and tracking the MPP, have been used in many popular articles such as in [35], the authors have used the cuckoo search algorithm for MPPT.…”

Section: Introductionmentioning

confidence: 99%

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

et al. 2022

View full text Add to dashboard Cite

The performance of a photovoltaic (PV) array depends on temperature, radiation, shading and load size. Conventional maximum power point tracking (MPPT) methods have acceptable efficiencies under uniform conditions (irradiance = 1000 W/m 2 and temprature = 25 °C), but in dynamic weather conditions, load changes, and also in partial shading conditions due to the presence of several local maximum power points (MPP) in the P-V characteristic, the conventional tracking method does not work well in finding the main MPP. To extract maximum power in all conditions, many algorithms have been proposed, all of which have limitations in terms of convergence speed, output power ripple and efficiency. This research proposes an optimized Fuzzy Logic Controller (FLC) based on the Cuckoo Optimization Algorithm (COA) for MPPT under uniform conditions, dynamic weather conditions, partial shading and under load changes. Finally, the research compared the simulation results with four other popular methods. According to the simulation observations and the result, COA-FLC overcomes the mentioned limitations such as low convergence speed, output power ripple and low tracking efficiency in all conditions. Simulations are performed with MATLAB / Simulink software.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The FPA outperformed both in all weather conditions in terms of efficiency, tracking speed, and convergence speed. The drawbacks of FPA are its complex structure, high computation time, procedural complexity, difficulty to tune parameters [69], and the decision of selecting a fixed value for switching probability so the local and global pollination can be used effectively and efficiently [70]. A Modified FPA (MFPA) was proposed for MPPT of a solar PV system [71].…”

Section: K Flower Pollination (Fpa) Algorithmmentioning

confidence: 99%

Technical Review of MPPT Algorithms for Solar Photovoltaic System: SWOT Analysis of MPPT Algorithms

Awan

2022

SSURJET

View full text Add to dashboard Cite

To continuously operate the Photovoltaic (PV) system at its Maximum Power Point (MPP) under changing weather is a challenging task. To accomplish this, multiple MPP Tracking (MPPT) algorithms have been proposed, which can be portioned into two: 1) Conventional algorithms, have the strengths of a simple structure, fewer computations, and low memory requirement, and cheap implementation. Whereas, trapping under Partial Shading Conditions (PSC), steady-state oscillations, and system dependency are the associated drawbacks. Conversely, 2) Soft computing algorithms, perform efficiently under all weather conditions with zero steady-state oscillations, and are system independent. The structural complexities, giant computations, huge memory requirements, and expensive implementation, are the accompanying concerns. The core contribution of this study is to present a deep analysis of all the MPPT algorithms at the standard benchmarks defined in the published literature, for the readers so they could decide which algorithm to choose under certain circumstances.

show abstract

“…FPA is one algorithm that can be used to make the best decision in an optimization problem. The MPPT case is an example of an optimization problem that can be solved by the FPA algorithm [9]. In the case of MPPT, the FPA will continue to look for the best duty cycle value by comparing the output power before and after it.…”

Section: Flower Pollination Algorithm (Fpa)mentioning

confidence: 99%

Evaluation and Comparison of DC-DC Power Converter Variations in Solar Panel Systems Using Maximum Power Point Tracking (MPPT) Flower Pollination Algorithm (FPA) Control

2020

Self Cite

View full text Add to dashboard Cite

Maximum Power Point Tracking (MPPT) is a method that can be used to optimize the electrical power output from solar panels. The performance of the MPPT method on solar panel systems can be influenced by many variables. One of them is the selection of a DC-DC power converter. DC-DC-DC Converter is a component that is used to optimize the performance of solar panels. Several types of DC-DC Converter are Buck, Buck-Boost, Single Ended Primary Inductance Converter (SEPIC), and CUK. Each converter has a different effect on solar panels output power. In order to observe and make a comprehensive analysis, simulations are performed through PSIM (Power Simulator) software on the performance of several DC-DC Converters that use Flower Pollination Algorithm (FPA) as the MPPT algorithm. Variables that observed are the output power characteristic, the response of the voltage-current ripple signal, and the accuracy of the converter in the process of reaching the maximum power point condition. As a result, CUK converter can obtain the highest value of solar panel output power, 145.02 W. A low ripple level with a stable power value response is entirely generated by CUK and SEPIC Converter. Overall, for this system, the CUK converter has better performance than the other converters.

show abstract

Analysis and Evaluation Performance of MPPT Algorithms: Perturb & Observe (P&O), Firefly, and Flower Pollination (FPA) in Smart Microgrid Solar Panel Systems

Cited by 12 publications

References 7 publications

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

Technical Review of MPPT Algorithms for Solar Photovoltaic System: SWOT Analysis of MPPT Algorithms

Evaluation and Comparison of DC-DC Power Converter Variations in Solar Panel Systems Using Maximum Power Point Tracking (MPPT) Flower Pollination Algorithm (FPA) Control

Contact Info

Product

Resources

About