Maximum power point tracking of PV water pumping system using artificial neural based control

Fathi Aashoor, Francis Robinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • 3 Citations

Abstract

In photovoltaic (PV) water pumping systems, a maximum
power point tracking (MPPT) controller is extremely
important. Since PV generators exhibit nonlinear I-V
characteristics and their maximum power point varies with
solar insolation. Therefore, the MPPT controller optimises the
solar energy conversion by ensuring that the PV generator
runs at the maximum power point at all times under different
illumination conditions. In this paper, a new artificial neural
network (ANN) based searching algorithm is proposed for
maximum power point tracking (MPPT). The system is
composed of solar array, buck converter and centrifugal pump
load driven by a permanent magnet DC motor. The proposed
ANN controller uses the output power of the PV generator
and speed of the DC motor as input signals and generates the
pulse width modulation (PWM) control signal to adjust the
operating duty ratio of a buck converter to match the load
impedance to the internal impedance of the PV array; thus
maximizing the motor speed and the water discharge rate of a
centrifugal pump. A complete dynamic simulation of the
system is developed in MATLAB/SIMULINK to demonstrate
the feasibility of the ANN control scheme under different
sunlight insolation levels. The results obtained verify that the
proposed ANN controller shows a significant improvement in
the power extraction performance under different sunlight
conditions, when compared with a directly-connected PV generator
energized pumping system. Moreover, the
simulation results match the calculated improvement.
LanguageEnglish
Title of host publication3rd Renewable Power Generation Conference (RPG 2014)
Place of PublicationNaples
PublisherIET
DOIs
StatusPublished - 24 Sep 2014
Event3rd Renewable Power Generation Conference, RPG 2014 - Naples, UK United Kingdom
Duration: 24 Sep 201425 Sep 2014

Conference

Conference3rd Renewable Power Generation Conference, RPG 2014
CountryUK United Kingdom
CityNaples
Period24/09/1425/09/14

Fingerprint

Neural networks
Incident solar radiation
Controllers
DC motors
Centrifugal pumps
Water
Energy conversion
Pulse width modulation
Solar energy
Permanent magnets
MATLAB
Solar cells
Lighting
Computer simulation

Cite this

Maximum power point tracking of PV water pumping system using artificial neural based control. / Aashoor, Fathi; Robinson, Francis.

3rd Renewable Power Generation Conference (RPG 2014). Naples : IET, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aashoor, F & Robinson, F 2014, Maximum power point tracking of PV water pumping system using artificial neural based control. in 3rd Renewable Power Generation Conference (RPG 2014). IET, Naples, 3rd Renewable Power Generation Conference, RPG 2014, Naples, UK United Kingdom, 24/09/14. https://doi.org/10.1049/cp.2014.0884, https://doi.org/10.1049/cp.2014.0923
Aashoor, Fathi ; Robinson, Francis. / Maximum power point tracking of PV water pumping system using artificial neural based control. 3rd Renewable Power Generation Conference (RPG 2014). Naples : IET, 2014.
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abstract = "In photovoltaic (PV) water pumping systems, a maximumpower point tracking (MPPT) controller is extremelyimportant. Since PV generators exhibit nonlinear I-Vcharacteristics and their maximum power point varies withsolar insolation. Therefore, the MPPT controller optimises thesolar energy conversion by ensuring that the PV generatorruns at the maximum power point at all times under differentillumination conditions. In this paper, a new artificial neuralnetwork (ANN) based searching algorithm is proposed formaximum power point tracking (MPPT). The system iscomposed of solar array, buck converter and centrifugal pumpload driven by a permanent magnet DC motor. The proposedANN controller uses the output power of the PV generatorand speed of the DC motor as input signals and generates thepulse width modulation (PWM) control signal to adjust theoperating duty ratio of a buck converter to match the loadimpedance to the internal impedance of the PV array; thusmaximizing the motor speed and the water discharge rate of acentrifugal pump. A complete dynamic simulation of thesystem is developed in MATLAB/SIMULINK to demonstratethe feasibility of the ANN control scheme under differentsunlight insolation levels. The results obtained verify that theproposed ANN controller shows a significant improvement inthe power extraction performance under different sunlightconditions, when compared with a directly-connected PV generatorenergized pumping system. Moreover, thesimulation results match the calculated improvement.",
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N2 - In photovoltaic (PV) water pumping systems, a maximumpower point tracking (MPPT) controller is extremelyimportant. Since PV generators exhibit nonlinear I-Vcharacteristics and their maximum power point varies withsolar insolation. Therefore, the MPPT controller optimises thesolar energy conversion by ensuring that the PV generatorruns at the maximum power point at all times under differentillumination conditions. In this paper, a new artificial neuralnetwork (ANN) based searching algorithm is proposed formaximum power point tracking (MPPT). The system iscomposed of solar array, buck converter and centrifugal pumpload driven by a permanent magnet DC motor. The proposedANN controller uses the output power of the PV generatorand speed of the DC motor as input signals and generates thepulse width modulation (PWM) control signal to adjust theoperating duty ratio of a buck converter to match the loadimpedance to the internal impedance of the PV array; thusmaximizing the motor speed and the water discharge rate of acentrifugal pump. A complete dynamic simulation of thesystem is developed in MATLAB/SIMULINK to demonstratethe feasibility of the ANN control scheme under differentsunlight insolation levels. The results obtained verify that theproposed ANN controller shows a significant improvement inthe power extraction performance under different sunlightconditions, when compared with a directly-connected PV generatorenergized pumping system. Moreover, thesimulation results match the calculated improvement.

AB - In photovoltaic (PV) water pumping systems, a maximumpower point tracking (MPPT) controller is extremelyimportant. Since PV generators exhibit nonlinear I-Vcharacteristics and their maximum power point varies withsolar insolation. Therefore, the MPPT controller optimises thesolar energy conversion by ensuring that the PV generatorruns at the maximum power point at all times under differentillumination conditions. In this paper, a new artificial neuralnetwork (ANN) based searching algorithm is proposed formaximum power point tracking (MPPT). The system iscomposed of solar array, buck converter and centrifugal pumpload driven by a permanent magnet DC motor. The proposedANN controller uses the output power of the PV generatorand speed of the DC motor as input signals and generates thepulse width modulation (PWM) control signal to adjust theoperating duty ratio of a buck converter to match the loadimpedance to the internal impedance of the PV array; thusmaximizing the motor speed and the water discharge rate of acentrifugal pump. A complete dynamic simulation of thesystem is developed in MATLAB/SIMULINK to demonstratethe feasibility of the ANN control scheme under differentsunlight insolation levels. The results obtained verify that theproposed ANN controller shows a significant improvement inthe power extraction performance under different sunlightconditions, when compared with a directly-connected PV generatorenergized pumping system. Moreover, thesimulation results match the calculated improvement.

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DO - http://dx.doi.org/10.1049/cp.2014.0884

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PB - IET

CY - Naples

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