Neural Network Based MPPT Controller for Solar PV Connected Induction Motor

Shanthi, T.

doi:10.11591/ijra.v7i2.pp129-139

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The soft computing approach based on neural network (ANN) technique is a powerful tool for solving the nonlinear problems [7], [12]- [14], Indeed, the photo voltaic systems are often complex system with strong non-linearity in their operation, since the effect of the variation of the temperature and the irradiance, which causes highly nonlinear problems in the output power and voltage.…”

Section: Neural Network Based Mpptmentioning

confidence: 99%

Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks

Bouzidi

Harrouz

Mansouri

et al. 2022

AMM

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In this paper, we present a modeling of the photovoltaic array in order to tracking the maximum power point (MPPT) using a soft computing approach based on artificial neural network, The maximum power point tracking MPPT play a crucial role in photovoltaic systems for their ability to maximize the power output under varying conditions; The photovoltaic array modeled and implemented in matlab simulink environnement using the conventional perturb and observe algorithm for multiple ranges under varying temperatures and irradiances levels, a feed forward neural network collect the training data from the photovoltaic array simulink model, after the training process check, the neural network model tested with new temperatures and irradiance data to predict the maximum power point of the photovoltaic array, The developed neural network model shown an interesting results compared to simulink model based on classic perturb and observe algorithm.

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Section: Neural Network Based Mpptmentioning

confidence: 99%

Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks

Bouzidi

Harrouz

Mansouri

et al. 2022

AMM

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“…A recurrent fuzzy cerebellar model articulation neural network (RFCMANN)-based controller to regulate the active and reactive power of a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults is proposed in [29]. In [30], the speed of the induction motor is used as a feedback signal for a controller to generate the voltage, and the error and change in error obtained are used as inputs for the ANN to generate the switching signal to the SEPIC converter.…”

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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