ANN based MPPT Algorithm Design using Real Operating Climatic Condition

Bouakkaz, Mohammed Salah; Boukadoum, Ahcene; Boudebbouz, Omar; Bouraiou, Ahmed; Boutasseta, Nadir; Attoui, Issam

doi:10.1109/icmit47780.2020.9046972

Cited by 33 publications

(15 citation statements)

References 11 publications

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“…Four people are applied sequentially to the starting population, which is made up of chromosomal parents. Equation (20) gives the population's initial locations.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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 Outdated MPPT techniques are unable to withstand a dramatic change in weather conditions.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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“…Four people are applied sequentially to the starting population, which is made up of chromosomal parents. Equation (20) gives the population's initial locations.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

2022

IJE

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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%

“…Where fi is the activation function and wii is weight from the contribution layer to the production layer. If a bias is introduced to the contribution layer and the activation function of each neuron in the concealed layer is fh then equation becomes 20) In this investigation, the CFNN model is applied in time series data. Thus, the neurons in the contribution layer are the lags of time series data Xt-1, Xt-2, ..., Xt-p, whereas the production is the current data Xt.…”

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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“…The advantages of ANN include exceptional accuracy in modelling non-linearity and resolving problems without any prior knowledge or any model [41]. ANN can be utilized in modelling and predicting the output power of the solar power system to improve the tracking speed and accuracy [42]. It is proven to have better response time and less oscillation around MPP [43].…”

Section: B Annmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

174

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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ANN based MPPT Algorithm Design using Real Operating Climatic Condition

Cited by 33 publications

References 11 publications

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

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

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

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