Electrical degine of solar water pump run automatic1).pptx

Paper ID - 1570890546
Comparative Study of ANN and
Incremental Conductance MPPT for
Solar Water Pump
Authors
Kanchan Bharti Sarvendra Kumar Singh Shyama Kant Jha
M.Tech Student Research Scholar Professor
EE Department ICE Department ICE Department
Recent Advances in Electrical, Electronics & Digital Healthcare Technologies, 2023 (REEDCON-2023)
Presenter
Kanchan Bharti
Electrical Engineering Department
Netaji Subhash University of Technology, New Delhi

Paper ID - 1570890546
Outline of Presentation
1. Abstract
2. Introduction
3. Previous Work
4. Incremental Conductance (INC) MPPT
5. Artificial neural network (ANN) MPPT
6. Simulation Model
7. Result and Discussion
8. Comparison Study
9. Conclusion

Paper ID - 1570890546
Abstract
The world continues to need new energy sources to reduce its dependency on fossil fuels, and PV electricity appears
to be a realistic solution. When it comes to renewable energy, the sun's energy is the greenest and cleanest source
available.
A photovoltaic system's efficiency depends on both temperature and sun irradiation, making photovoltaic energy
unrestricted, clean, and less expensive, making it ideal for a variety of remote applications like telecommunications,
battery charging, meteorological measurement equipment, satellite feeding, and water pumping.
As a result, the output of a solar PV array depends on the sun's irradiance and temperature. So, enhancing the
efficiency of a solar PV array is basically dependent on the solar MPPT (Maximum Power Point Tracking System).
For measuring the peak power output of PV panels, the maximum power point tracking (MPPT) method based on
artificial neural networks (ANN) and incremental conductance (INC) is described in this paper.
It is precise to use MPPT for the measurement of the maximum possible power point as weather variables such as
temperature and sun irradiation change.
The research design and simulation of a PV array with an MPPT controller are presented in this paper.

Paper ID - 1570890546
Introduction
PV power seems to be a workable solution to the world's ongoing demand for fresh energy sources to lessen its
reliance on fossil fuels.
The sun's energy is green, clean, and the best energy application in the context of renewable energy. Photovoltaic
energy is unrestricted, clean, and less costly, and a PV system's efficiency depends on both temperature and sun
irradiation.
The maximum power from a solar PV array is extracted using the MPPT approach.
A primitive MPPT circuit has only simple DC converters between the output and input energy sources, which may be
any renewable source.
Presently, different types of methods have been proposed for extracting maximum power, such as perturb & observe
(P&O), incremental conductance (INC), fractional open-circuit voltage (FOCV), fractional short-circuit current
(FOSC), and hill climbing (HC).
These methods, however, suffer from a number of shortcomings, including a slower response time, large steady-state
fluctuations, and poor efficiency under rapid shifts in solar irradiance.
The main benefits of using PV systems that use ANN technology are their capacity to identify nonlinear correlations
involving both independent and dependent variables and the fact that a full understanding of the core system
characteristics is not necessary.

Paper ID - 1570890546
Pervious Work
S.No. Year Reference No. Type of Technique Application
1. 2022 [1] ANN 1. MPPT are commonly employed as a robust,
fast and efficient technique.
2. Quick reaction for maximum power point
tracking and less steady state inaccuracy.
2. 2018 [2] P&O algorithm 1. Simple structure and easy to implement on
stand-alone and grid connected system.
2. Can be used for high efficiency.
2019 [3]
3. 2017 [4] Golden section algorithm 1. Flexible output voltage.
2. Low loss of energy.
4. 2017 [5] Incremental conductance
algorithm
1. Highly efficient.
2. Automatic adjustment of module operating
voltage without oscillation.
2019 [6]
2020 [7]
5. 2013 [8] SEPIC algorithm 1. Easy to implement.
2. Based on high frequency transformer and
freewheeling diode.

Paper ID - 1570890546
Incremental Conductance (INC) MPPT
This technique may track shifting conditions more quickly than P&O, despite requiring the controller to complete more
calculations.
The incremental conductance approach is used to compare incremental conductance (I/V) to array conductance (I/V) in
order to estimate MPP.
The INC approach is based on the discoveries that P = IV and dp/dv = 0 at MPP. The MPP voltage is the resultant output
voltage if I/V = I/V.
MPP is therefore obtained when the incremental conductance matches the instantaneous conductance's negative value.
The INC MPPT algorithm operates with two values drawn from the characteristic curve depicted.
MPPT tracked the peak value of power at a certain irradiance.
In this MPPT algorithm, the instantaneous conductance values of ‘I/V’ and ‘dI/dV’ are used to obtain the maximum
value of power at this instance of irradiance.

Paper ID - 1570890546
Artificial neural network (ANN) MPPT
An ANN MPPT is basically an example of an AI technique. Compared to other standard MPPT approaches, this MPPT
provides additional benefits.
The main disadvantage of conventional MPPT is its slow response when sudden changes in weather variables occur
during PV array application. Conventional MPPT frequently fails to tackle MPP of PV arrays at certain values of
temperature and irradiance.
When the sun's temperature and irradiance fluctuate, NN will offer a precise duty ratio number to get MPPT.
In this research work, the study data set for New Delhi is taken from NASA to train the ANN.
Input data is provided by the user from the data set, and then the train and hidden layers are calculated on the basis of
the input data.
Starting with the ANN, a basic if-else programme written on the MATLAB workspace calculates training and target
data. The input data is divided into testing data, training data, and target data.
Levenberg-training Marquardt's algorithm is used to create the neural network. There are 10 hidden layers that allow
the user to generalise the data and weight of the neural network, and ANN will calculate mean square error (MSE) and
regression (R).

Paper ID - 1570890546
Simulation Model
A user-defined solar PV array of 3 strings in which 10 modules are connected in parallel is used, whose parameters are
shown in Table 1.
Table1: Parameters of PV Array
Parameter One Module value 3 Module Value
Maximum Power (Pmax) 200.143 W 600.429 W
Open Circuit Voltage (Voc) 32.9 V 98.7 V
Voltage at maximum Power (Vmap) 26.3 V 78.9 V
Short Circuit Currrent (Isc) 8.21 A 24.63 A
Current at Maximum Power (Imap) 7.61 A 22.83 A
Cell per Module 54 162
Temperature coefficient of Voc (α) -(0.35502) ( %/°C)
Temperature coefficient of Isc (β) 0.06 (%/°C)
The output of a PV array depends on solar irradiance and temperature. At every value of irradiance, the values of
power, current, and voltage are different, as shown in Table 2.

Paper ID - 1570890546
Table 2: Value of Maximum Power at Different Irradiances at 25°C for One Module
Irradiance (W/m2
) Maximum Power (W) Voltage at Max. Power (V) Current at Max. Power (A)
1000 200.143 26.3 7.61
800 161.50 26.45 6.10
700 414.8 26.52 5.35
600 121.8 26.58 4.58
500 101.6 26.58 3.52
400 81.16 26.51 3.06
One module draws the maximum current and voltage at different irradiances, as shown in the above table 2, but the
power changes with time and voltage, which is clearly shown by the characteristics of power voltage and current voltage
in fig. 1:

Paper ID - 1570890546
Fig. 1: P-V and V-I characteristics of one module at 25°C
The output of the PV array is applied to the DC-DC boost converter.
A boost converter with a specified input capacitor (Cin) of 5 mF, an inductor coil (L) of 2.5 mH, and an output
capacitor (Cout) of 250 mF is connected to obtain the maximum output voltage from a PV array.
In this, IGBT is used by two different terminals, and values are recorded for comparing their efficiency and power
rating. The methodology used for tuning the IGBT to obtain maximum power is called MPPT.
Maximum Power Point Tracking (MPPT) is a method used for obtaining the maximum voltage from solar, wind, or
any other source.

Paper ID - 1570890546
Result and Discussion
The output result waveform of PV cells without MPPT, PV array output with INC MPPT, and PV array output with
ANN MPPT
It is found that ANN-based MPPT gives a quick response as compared to conventional MPPT.
Without MPPT, the output of a solar PV array is changing with time and solar irradiance value, but it is not
constant at any point of operation and does not attain any stable point, as shown in Fig. 2.
Fig. 2: Output of a Solar Array Without MPPT
In Fig. 2, it is clearly observed that power and voltage are not constant at any point of operation, and maximum power is not
obtained at any point. So, for obtaining maximum power from a solar PV array, different conditions of MPPT are used.

Paper ID - 1570890546
Fig. 3: Output of the Solar Array with INC MPPT Fig. 4: Output of the Solar Array with ANN MPPT
In comparison to all other typical manipulation techniques, ANN-based MPPT tracking is more accurate. There are no
steady-state oscillations, less settling time, and higher output power, as seen in Fig. 4. Hence, when compared to previous
MPPT techniques, the ANN-based MPPT algorithm performs better. The maximum power is drawn from the solar PV array,
and this maximum power is constant for a certain duration of irradiance and temperature. When the values of irradiance and
temperature are changed, the maximum power value will also change, as shown in the characteristic diagram. An ANN-
based MPPT can then make a faster decision to settle the constant with a quick response.

Paper ID - 1570890546
Comparison Study
Table 3: Performance Comparison of INC and ANN MPPT
MPPT Irradiance (W/m2
) Pout(W) Pin (W) Efficiency (%) Avg. Power (W) Settling time (S)
ANN MPPT
1000 642.19 648.37 99.04
98.76% 0.013s
800 587.27 594.01 98.94
700 492.64 497.63 98.99
600 410.40 414.89 98.91
500 388.80 397.64 97.77
400 260.50 263.26 98.95
INC MPPT
1000 604.10 625.70 96.54
94.90%
There is no any
exact settling
time.
800 388.50 409.10 94.96
700 296.80 311.20 95.37
600 217.40 229.80 94.60
500 150.8 160.20 94.13
400 96.35 102.70 93.82

Paper ID - 1570890546
Conclusion
In this article, the MATLAB simulation design of the ANN and INC MPPT for obtaining maximum output power and
voltage is discussed in detail and coupled with a boost converter.
ANN MPPT gives a constant voltage, power, and current at every instant, and a small change in irradiance or
temperature has an effect on the output voltage and power, whereas INC-based MPPT does not give a quick response to
a small change in irradiance or temperature.
It was also observed that the settling time of ANN is about 0.013 s, and after that, it gives a constant maximum power
value, whereas the settling time of INC MPPT is about 0.035 s, but after this time, it does not give a constant maximum
power value.
The average efficiency of ANN is about 98.76%, whereas the average efficiency of INC MPPT is about 94.90%. It
means that the efficiency and overall performance of ANN MPPT are much better than those of INC MPPT.

Paper ID - 1570890546
References
1. Y.E. Idrissi, K. Assalaou, L. Elmahni, E. Aitiaz, “New improved MPPT based on artificial neural network and PI controller for
photovoltaic applications”, International Journal of Power Electronics and Drive Systems Vol. 13, Issue 3, pp 1791-1801, 1 Sept 2022.
2. G. Jain, A. Shankar V.K. and S. Umashankar , “Modelling and simulation of solar photovoltaic fed induction motor for water pumping
application used perturb and observer MPPT”, IEEE international conference on energy efficient technologies for sustainability, 7-8
April 2016, pp, Nagercoil, India.
3. B. Ali, Q.A. Khan, Mod. Adeel, Dr.A.A. Khan and T. Yamin, “Portable Solar Powered DC Pumping System Using Pump jack and
MPPT”, IEEE International Conference on Computing, Mathematics and Engineering Technologies, 30-31 Jan. 2019, Sukkur,
Pakistan.
4. S. Djeriou, A. Kheldoun and A. Mellit “Efﬁciency Improvement in Induction Motor-Driven Solar Water Pumping System Using
Golden Section Search Algorithm,” Springer, pp. 3199–3211, 29 November 2017.
5. N. Daniel and J. J. Daniel, “modelling of BLDC Motor Drive Solar Water Pump with INC-MPPT in MATLAB”, IEEE international
conference on circuit power and computing technologies, 20-21 April 2017, pp, Kollam, India.
6. H.K.V. Gadiraju, V.R. Barry and I. Maraka, “Dynamic Photovoltaic Array Reconfiguration for Standalone PV Water Pumping
System”, IEEE 8th
International Conference on Power System, 20-22 Dec. 2019, Jaipur, India.
7. A.U. Selokar and Prof. V.B. Waghmare, “Solar photovoltaic array and Zeta converter based water pumping system based on BLDC
motor with torque ripple compensation”, IEEE International Conference on Emerging Trends in Information Technology and
Engineering, 24-25 Feb 2020, Vellore, India.
8. R. Kumar and B. Singh, “Solar PV Array Fed Water Pumping System Using SEPIC Converter Based BLDC Motor Drive”, national
power system conference, pp., 18-20 December 2014, Guwahati, India.

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