Comparative Evaluation Of Single Sensor Based Maximum Power Point Tracking Techniques For Photovoltaic Water Pumping Systems

Daoud, Ahmed A.; Scholar,; Midoun, A.

doi:10.26634/jee.3.3.1123

Cited by 5 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output power of the solar panels are affected by the light intensity and the outside temperature, therefore, we must establish a photovoltaic panel simulation model to let the maximum solar panels in the UAV to play its photoelectric conversion efficiency. It can more clearly grasp the impact of the external environment on its output power [13]- [17]. The battery's output characteristic equation is as follow.…”

Section: Solar Cell Characteristic a Solar Cell Model And Characteristicsmentioning

confidence: 99%

An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Wang

Shen

2019

IEEE Access

View full text Add to dashboard Cite

Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.INDEX TERMS Simulation, photovoltaic maximum power tracking, artificial neural networks, mind evolutionary algorithm.

show abstract

Section: Solar Cell Characteristic a Solar Cell Model And Characteristicsmentioning

confidence: 99%

An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Wang

Shen

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Fuzzy controller is improved by developing artificial intelligent methods by using neural network . Neural networks have the ability to adapt so that it can handle [12] non-linearity, uncertainty and parameter variations that occur in a controlled plant. One example of non-linear feed forward networks is back propagation network and radial basis function network (RBF) [19].…”

Section: Neural Network Peak Power Point Tracking Controllermentioning

confidence: 99%

Neuro Fuzzy based Peak Power Point Tracking for Solar Photo Voltaic System

Kalika¹,

Rajaji²,

Gupta³

2013

IJCA

View full text Add to dashboard Cite

Photovoltaic systems are so versatile that it can supply any electric power need and are used for numerous applications. Recent advancements in efficiency and cost reduction have made photovoltaic systems economically competitive with traditional power sources. This paper presents an intelligent method of peak power point tracking for photovoltaic systems based on tracking the peak power point by measuring the voltage and current of the solar array to control a buck-boost DC to DC converter. Result analysis shows that the neuro-fuzzy controller can deal with different load and weather conditions and deliver more power from the photovoltaic systems. To increase the efficiency of PV panels, it must operate around the peak power point which is influenced by cell temperature and sun irradiation. A controller therefore is needed to find the peak power point and control PV output voltage according to peak power point voltage. The aim of the paper is to design and analyze neural fuzzy controller for controlling the PV system output voltage.

show abstract

“…These techniques differ in many aspects such as required sensors, complexity, cost, range of effectiveness, convergence speed, correct tracking when irradiation and/or temperature change, hardware needed for the implementation or popularity, their advantages and their necessity to control the operating point to extract the maximum power from the PV array but they all require the sensing of the PV current and voltage using the off-the-shelf hardware to generate the duty cycle  used to control converters, such as choppers. [17,27].…”

Section: Introductionmentioning

confidence: 99%

An investigation of incremental conductance based maximum power point tracking for photovoltaic water pumping system performances

Kodji,

Houdji,

Djongyang

et al. 2018

J. Ren. Energies

View full text Add to dashboard Cite

In this work, each components of the photovoltaic water pumping system have been modeled in the Matlab/Simulink platform using meteorological data obtained from the field for the specific month corresponding to the hottest one of the year and for different total heads or pump loads. Optimization is done using the incremental conductance MPPT techniques which help to track the maximum power available in the photovoltaic module at each time and then feed the pump with optimal power. The results of this simulations show the effectiveness of the optimized system compare to the direct-coupled one. For the considered water pumps, daily flow rates are predicted and permit to calculate monthly total volume of water which is in the range of 122 m3 to 200 m3 for the total heads in the range of 60 m to 24 m with optimization and in the range of 12 m3 to 43 m3 for the same range of total heads without optimization. This study also shows the relevance of the optimized system compare to the direct-coupled system for providing water in rural areas of soudano-sahelian zone of Cameroon.

show abstract

Comparative Evaluation Of Single Sensor Based Maximum Power Point Tracking Techniques For Photovoltaic Water Pumping Systems

Cited by 5 publications

References 10 publications

An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Neuro Fuzzy based Peak Power Point Tracking for Solar Photo Voltaic System

An investigation of incremental conductance based maximum power point tracking for photovoltaic water pumping system performances

Contact Info

Product

Resources

About