Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area

BA, Amadou; Ndiaye, Alphousseyni; Ndiaye, El Hadji Mbaye; Mbodji, Senghane

doi:10.1016/j.mex.2022.101959

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Reinforcement learning, on the other hand, offers strategies for realtime optimization by adapting to changing conditions and user demands [14]. Furthermore, hybrid models combining multiple AI techniques have been proposed, aiming to harness the strengths of individual methods for superior performance [15].…”

Section: Ai Techniques In Battery Performance Enhancementmentioning

confidence: 99%

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

2023

Proceeding Book of 2nd International Conference on Contemporary Academic Research ICCAR 2023

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In the rapidly evolving landscape of electric vehicles (EVs), optimizing battery performance remains a pivotal challenge. This study presents a comprehensive comparison between traditional battery management techniques and an AI-driven approach leveraging Convolutional Neural Networks (CNNs) for EVs. Our investigations focused on three primary metrics: prediction accuracy concerning the State of Charge (SoC) and battery health, potential battery life extension, and computational efficiency. Results unequivocally showed that the CNN-based model surpassed traditional methodologies in all examined metrics, with improved prediction accuracies, a significant increase in estimated battery lifespan, and reduced computational times. This research underscores the potential of integrating AI into EV battery management systems, promising not only enhanced battery performance but also signaling a paradigm shift towards a more sustainable, efficient, and reliable electric transportation era.

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Section: Ai Techniques In Battery Performance Enhancementmentioning

confidence: 99%

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

2023

Proceeding Book of 2nd International Conference on Contemporary Academic Research ICCAR 2023

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“…The ANN used the temperature and solar irradiance as inputs to predict the MPPT voltage, current and power. Two intelligent controllers based on an ANN and adaptive neuro-fuzzy inference system (ANFIS) are proposed to optimize the PV system's output in non-uniform weather conditions and compared in [32].…”

Section: Introductionmentioning

confidence: 99%

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network

Lo,

Chung,

Hsung

et al. 2024

Sensors

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Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles’ population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

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“…Artificial intelligence, particularly neural networks (NN), has demonstrated immense potential for enhancing the MPPT process in PV systems. By leveraging the capabilities of neural networks (4)(5)(6)(7) , it becomes possible to model the complex relationships between the various environmental factors and the maximum power point (8)(9)(10) .…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking in A Photovoltaic System Based on Artificial Neurons

Ourici,

Abderaouf

2023

IJST

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Objectives: This study seeks to assess the efficacy of an approach utilizing artificial neural networks for tracking the maximum power point (MPP) in photovoltaic systems. The main objective is to compare this method with the traditional perturb and observe technique, evaluating its effectiveness, particularly when faced with changing weather conditions. Methods: An artificial neural network was employed to model the relationship between the weather conditions such as irradiation and temperature and the maximum power point of the 100 Kw photovoltaic system. They collected data from the installation, including performance measurements and corresponding environmental conditions. The artificial neural network was trained using this dataset to accurately estimate and track the MPP. In our study we have only one input variable which is the power of the photo voltaic generator and a single output which is the cyclic ratio' D', we collected these data from the solar panel simulation with perturb and observe control. The network we built has an input layer with two neurons, a hidden layer with 15 neurons and an output layer with one neuron, we performed a learning on 100 data using Matlab software, we trained and tested this neural network until we obtained a very small quadratic error compared with similar researches. Compared with a P&O method , the maximum power was riched. Findings: The deep learning model exhibited enhanced efficiency in extracting the maximum power point ;particularly ; in the presence of climatic variations. It successfully captured the complex relationships between weather conditions and the MPP, leading to improved power generation and minimized energy losses. The maximum power reached with perturb and observe method (P&O) is 95 kw for a response time of 0.1752 s, on the other hand the neural method is faster with a response time of 0.1567 s and at the same time the maximum power of 100 kw has been reached. Novelty: This study is unique in a way that it employed an artificial neural network for MPP extraction in photovoltaic installations. The neural network we used to simulate our photocoltaic installation produced excellent results: we can clearly see that the PV output voltage remained constant and PV maximum power reached 100 kw despite climatic variations.

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Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area

Cited by 8 publications

References 27 publications

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network

Maximum Power Point Tracking in A Photovoltaic System Based on Artificial Neurons

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