Photovoltaic model with MPP tracker for standalone / grid connected applications

Natsheh, Emad; Albarbar, Alhussein

doi:10.1049/cp.2011.0205

Cited by 17 publications

(28 citation statements)

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“…High agreement between the outcomes of the model and the experimental data was achieved. Results and validation of the module can be found in the author previous work published in [50].…”

Section: Dataset Collectionmentioning

confidence: 99%

“…Anomalies in data due to a broken or unfunctional device/sensor were eliminated. The targeted output voltage (Vr) and current (Ir) are collected by running a MATLAB code that analyses the validated PV model output characteristics [47][48][49][50]. The dynamic behaviour of the proposed PV model has been validated previously by the author for different types of PV modules under variety of solar radiation and temperature conditions.…”

Section: Dataset Collectionmentioning

confidence: 99%

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Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Natsheh

Samara

2019

Applied Sciences

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Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.

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“…High agreement between the outcomes of the model and the experimental data was achieved. Results and validation of the module can be found in the author previous work published in [50].…”

Section: Dataset Collectionmentioning

confidence: 99%

Section: Dataset Collectionmentioning

confidence: 99%

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Natsheh

Samara

2019

Applied Sciences

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“…The general mathematical model for the solar cell has been studied over the past three decades [10]. The model of PV power plant used in this study is based on the dynamic PV model developed and validated in our previous work [11]. The following equation model the current of the PV cell [11]:…”

Section: The Photovoltaic Modulementioning

confidence: 99%

“…The model of PV power plant used in this study is based on the dynamic PV model developed and validated in our previous work [11]. The following equation model the current of the PV cell [11]:…”

Section: The Photovoltaic Modulementioning

confidence: 99%

“…As such, many maximum power point tracking (MPPT) techniques have been developed and implemented. Among these techniques, hill-climbing MPPT such as perturb and observe (P&O), which is a simple algorithm that does not require previous knowledge of the PV generator characteristics and is easy to implement with analogue and digital circuits [11]. However, the main problem of this technique can be seen when solar radiation rapidly change.…”

Section: Pv Mppt Based On Neural Networkmentioning

confidence: 99%

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Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

Natsheh¹,

Albarbar²

2013

SGRE

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This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications

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Simulation and Analysis of Hybrid PEMFC and Solar PV Cell Model for Commercial Load Profile System

Thoudam

Guchhait

Banerjee

2020

Lecture Notes in Electrical Engineering

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Photovoltaic model with MPP tracker for standalone / grid connected applications

Cited by 17 publications

References 0 publications

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

Simulation and Analysis of Hybrid PEMFC and Solar PV Cell Model for Commercial Load Profile System

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