Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

Bahgat, A.; Helwa, N. H.; Ahamd, G.E; Shenawy, E. T. El

doi:10.1016/s0960-1481(03)00126-5

Cited by 51 publications

(21 citation statements)

References 9 publications

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“…Inaccuracy of the maximum power estimation always exists to some extend due to the prediction errors [41]. Sometimes the predicted value is larger than practical, sometimes smaller.…”

Section: Algorithm Implementationmentioning

confidence: 99%

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Zhang

Liu

et al. 2014

IEEE Trans. Power Syst.

103

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Abstract-For a microgrid with high renewable energy penetration to work autonomously, it must maintain its own supply-demand balance of active power. Maximum peak power tracking algorithms, which emphasize high renewable energy utilization, may cause a supply-demand imbalance when the available renewable generation is more than demanded, especially for autonomous microgrids. Currently, droop control is one of the most popular decentralized methods for sharing active and reactive loads among the distributed generators. However, conventional droop control methods suffer from slow and oscillating dynamic response and steady state deviations. To overcome these problems, this paper proposes a distributed subgradient-based solution to coordinate the operations of different types of distributed renewable generators in a microgrid. By controlling the utilization levels of renewable generators, the supply-demand balance can be well maintained and the system dynamic performance can be significantly improved. Simulation results demonstrate the effectiveness of the proposed control solution.

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“…Inaccuracy of the maximum power estimation always exists to some extend due to the prediction errors [41]. Sometimes the predicted value is larger than practical, sometimes smaller.…”

Section: Algorithm Implementationmentioning

confidence: 99%

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Zhang

Liu

et al. 2014

IEEE Trans. Power Syst.

103

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“…Several MPP methods, such as perturbation, fuzzy control, power-voltage differentiation and on-line method have been reported (Dufo-Lopez and Bernal-Agustin, 2005;Bahgat et al, 2004;Yu et al, 2004). These control methods have drawbacks in stability and response time in the case when solar illumination changes abruptly.…”

Section: Determination Of the Maximum Power Pointmentioning

confidence: 99%

“…For this reason, the model parameters identification provides a powerful tool in the optimization of solar cell performance. The algorithms for determining model parameters in solar cells, are of two types: those that make use of selected parts of the characteristic (Chan et al, 1987;Charles et al, 1981;Charles et al, 1985;Dufo-Lopez and Bernal-Agustin, 2005;Enrique et al, 2007) and those that employ the whole characteristic (Haupt and Haupt, 1998;Bahgat et al, 2004;Easwarakhanthan et al, 1986). The first group of algorithms involves the solution of five equations derived from considering select points of an current-voltage (I-V) characteristic, e.g.…”

Section: Introductionmentioning

confidence: 99%

Application of the Genetic Algorithms for Identifying the Electrical Parameters of PV Solar Generators

Sellami¹,

Bouaïcha²

2011

Solar Cells - Silicon Wafer-Based Technologies

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“…• estimate and predict solar radiation data [10][11][12][13][14], • estimate the maximum power (P m ) and normal operating power of a flat PV module [15,16], • size, model, and simulate both stand-alone PV systems [17,18] and PV systems with a maximum power-point tracking controller [19,20], • predict the equivalent circuits parameters of a flat PV module [21],…”

Section: Introductionmentioning

confidence: 99%

Applications of ANNs in the Field of the HCPV Technology

Almonacid

Mellit

Kalogirou

2015

High Concentrator Photovoltaics

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High-concentrator photovoltaic (HCPV) devices are based on the use of multijunctions solar cells and optical devices. Therefore, the electrical modelling of an HCPV device presents a great level of complexity. Several artificial neural network (ANN)-based models have been developed to try to address this issue. In this chapter, a review of the developed ANN-based models developed to try to address some issues related with the field of high concentrator PV technology is reported. In addition, the results obtained from the application of some of these models to estimate the electrical parameters of an HCPV module-such as maximum power, short-circuit current, and open-circuit voltage-are presented. The results show that the ANNs are a useful tool for modelling HCPV applications.

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Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

Cited by 51 publications

References 9 publications

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Application of the Genetic Algorithms for Identifying the Electrical Parameters of PV Solar Generators

Applications of ANNs in the Field of the HCPV Technology

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