A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller

Petchjatuporn, P.; Ngamkham, Wannaya; Khaehintung, N.; Sirisuk, P.; Kiranon, W.; Kunakorn, A.

doi:10.1109/tencon.2005.301032

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…ANN‐based PV MPPT techniques commonly utilise electric signals as inputs [11–19, 29, 33–38]. Those techniques can benefit from the available electric signals in the system which are: PV array voltage ( V PV ), current ( I PV ) and power ( P PV ).…”

Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

“…Several MPPT techniques use two signals for tracking, such as [12, 14] which utilise the array current ( I PV ) and array power ( P PV ), whereas others, such as [15, 17, 29, 33, 37, 38] use the array current ( I PV ) and array voltage ( V PV ). Other techniques utilise only one electric signal as array power ( P PV ) [13, 18] or array voltage ( V PV ) [19] or open‐circuit voltage [34, 35] for cost reduction purposes. More accurate techniques offering better performance benefits from all the available electric signals in the system [11].…”

Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

“…Hybrid controllers offer integration and assistance between ANN with either classical or artificial based MPPT techniques for performance improvement. Classical MPPT techniques can be adapted with ANN assistant or integration for enhanced performance [13, 17, 29]. Other artificial intelligence techniques can be utilised with ANN offering superior performance on the expense of complexity [12, 14–16, 18, 19, 22, 26, 27, 30, 31, 33, 39, 40, 44, 45].…”

Section: Ann‐based Pv Mppt Techniques Classification According To Tmentioning

confidence: 99%

“…Artificial neural networks (ANNs) feature several advantages, such as robust operation, fast tracking, non‐linear systems tolerant and off‐line training. Therefore various ANN‐based PV MPPT techniques elaborate recently [11–48].…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Elobaid

Abdelsalam

Zakzouk

2015

IET Renewable Power Generation

261

118

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Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence-based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple-variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN-based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.

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Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

Section: Ann‐based Pv Mppt Techniques Classification According To Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Elobaid

Abdelsalam

Zakzouk

2015

IET Renewable Power Generation

261

118

View full text Add to dashboard Cite

show abstract

“…Therefore, modeling approaches based on empirical and ANN models capable of approximating complex non‐linear behavior could be highly motivated as these are capable of describing the relationships between different mechanisms not clearly understood . ANN models are highly flexible and have been applied to several different aspects of battery research, for example, estimating capacity , state‐of‐charge (SOC) , state‐of‐health , and to control battery charging .…”

Section: Overview Of Related Battery Modelsmentioning

confidence: 99%

A hybrid lithium-ion battery model for system-level analyses

Lindgren

Asghar

Lund

2016

Int. J. Energy Res.

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Summary We describe an advanced lithium‐ion battery model for system‐level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi‐parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt‐hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account. Copyright © 2016 John Wiley & Sons, Ltd.

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Maximum Power Point Tracking Techniques for Photovoltaic Systems—A Comprehensive Review From Real‐Time Implementation Perspective

Sudarshan¹,

Chitra²,

Sultana³

et al. 2022

Smart Grids and Green Energy Systems

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A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller

Cited by 12 publications

References 8 publications

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

A hybrid lithium-ion battery model for system-level analyses

Maximum Power Point Tracking Techniques for Photovoltaic Systems—A Comprehensive Review From Real‐Time Implementation Perspective

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