MPPT Algorithm Based on Fuzzy Logic and Artificial Neural Network (ANN) for a Hybrid Solar/Wind Power Generation System

Elaissaoui, Hayat; Zerouali, Mohammed; Ougli, Abdelghani El; Tidhaf, Belkassem

doi:10.1109/icds50568.2020.9268747

Cited by 28 publications

(8 citation statements)

References 5 publications

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“…The production layer is also publicly connected with the influence layer head-to-head to the hidden layer in a three-layer network. With enough hidden neurons, a cascading network with two or more layers, like FF networks, may learn any arbitrarily limited I-O connection [19][20][21][22][23][24][25]. The CFNN can be used for any kind of contribution to cartography creation.…”

Section: Cascaded Feed Forward Neural Network Methodsmentioning

confidence: 99%

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Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

2022

IJE

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This research looks at how photovoltaic (PV) cells generate energy in different weather conditions. Photovoltaic power today plays a key role in the production of energy and satisfying the needs of consumers all over the world. The PV cell's ability to generate electricity was entirely dependent on sunshine and temperature fluctuations in the environment. Several researchers are working on a variety of MPPT methods for a photovoltaic system. Outdated MPPT techniques are unable to withstand a dramatic change in weather conditions. The fundamental purpose of this study is to associate the numerous unadventurous and clever controllers for MPPT of the PV system, such as the PSO, GA, and CNFF. The MATLAB environment was used to create and simulate the recommended intelligent controller for MPPT in the PV system. Furthermore, the aforementioned findings like Voltage, Current and Power with respect to different irradiance and temperature are compared and evaluated. The performance of the above-mentioned topologies has been related to the optimum intelligent controller for the PV system and cncluded that the CFFNN gives better efficiency with minimum time required to extract.

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Section: Cascaded Feed Forward Neural Network Methodsmentioning

confidence: 99%

“…To address this issue, the algorithm was tweaked to reset the initial population whenever a change in irradiance or cell temperature is detected. As a result, if the following two requirements are fulfilled, the GA is reinitialized: Equations ( 18) and (19).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

2022

IJE

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“…For this case of application and based on numerous tests applied before in the study by Zerouali et al (2019) and Elaissaoui et al (2020) and in our previous works, the chosen variables are related to these three signals.…”

Section: Fuzzy Logic Mppt Algorithmsmentioning

confidence: 99%

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

et al. 2022

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One of the most reliable and advanced renewable energy sources is wind energy. It is critical to harness as much wind energy as possible and maintain wind turbines operating at full capacity. Maximum power point tracking (MPPT) is a cutting-edge study that incorporates a variety of approaches. Because each MPPT technique has its own set of advantages and disadvantages, developing an accurate maximum power point tracking methodology for a certain case necessitates understanding. As a result, they must be checked thoroughly. This research tries to examine many algorithms that can be used to improve the wind energy system’s global MPPT performance. The traditional “Perturb and Observe” tool, the optimization method based on the “particle swarm optimization algorithm,” the neural network, and the “fuzzy logics” as intelligent tools are these techniques. The main objective of this research is to define and evaluate four different flexible algorithms that achieve the fundamental objective of this optimization. The advantages, drawbacks, and thorough analysis of MPPT systems are highlighted in terms of initial investment, responsiveness, and capacity to create maximum energy output. All of this comparison was made through simulation software, which is the MATLAB Simulink tool. The conclusions are supported by a comprehensive discussion and presentation of the results for a variety of situations and tests that reflect real-world behavior in any wind system.

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“…In a three-layer network, the production layer is also associated openly to the contribution layer adjacent to the concealed layer. As with FF networks, a cascading network with 2 or more layers may learn any arbitrarily finite I-O relationship with enough hidden neurons [19][20][21][22]. The CFNN can be used for any type of contribution to the production cartography.…”

Section: Iiiiv Cascaded Feed Forward Neural Network (Cfnn) (Machine Learning Based) Mppt Algorithmmentioning

confidence: 99%

Cascaded Feed Forward Multilayer Neural Network based MPPT Controller for Improving the Performance of Photovoltaic System

Rupesh¹

2021

TURCOMAT

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This paper deals with the energy production of photovoltaic (PV) cells in different weather conditions. Today, photovoltaic generation plays an important role in generating electricity and satisfies the demand of the island's consumers. The power generation of the PV cell was completely dependent upon sunlight and temperature, but sunlight and temperature changed forever in nature. The many researchers are working on different MPPT technologies for a PV system. Conventional MPPT controllers cannot withstand a sudden change of weather conditions. The main aim of this article is to compare the various conventional and intelligent controller such as the GA, Fuzzy, KGMO, and CNFF for MPPT of the PV system. The proposed intelligent controller was developed and simulated by the MATLAB environment for MPPT in the PV system. In addition, the above results are evaluated and compared. Based on performance, the optimal smart controller has been recommended as MPPT of the PV system

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MPPT Algorithm Based on Fuzzy Logic and Artificial Neural Network (ANN) for a Hybrid Solar/Wind Power Generation System

Cited by 28 publications

References 5 publications

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

Cascaded Feed Forward Multilayer Neural Network based MPPT Controller for Improving the Performance of Photovoltaic System

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