frequency scheduling in a scalar control of induction ... of the currents, the rotor flux and the...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 22 (2016) pp. 10928-10938

© Research India Publications. http://www.ripublication.com

10928

Frequency Scheduling in a Scalar Control of Induction Motor Pump for

Photovoltaic Pumping System: Implementation and Measurement

D.Mezghani*1, H.Othmani*2 and *F.Sassi*, A.Mami*

*Tunis el Mana University, Science Faculty Tunis,

Campus Universitaire 2092 - El Manar Tunis, Tunisie.

Abstract

This work is about design and implementation of a fuzzy

optimizer for photovoltaic pumping system in order to exploit

all available energy related to climatic conditions. The studied

system is composed by a photovoltaic generator which is

coupled to induction motor pump through an adaptive system.

This adaptive system is a three-phase inverter which is

controlled by a scalar control law. As shown, this approach

revealed its efficiency and robustness against external

disturbances which is the climatic parameters change.

Keywords: Photovoltaic, Arduino, fuzzy logic, Scalar

control, centrifugal pump, Induction Motor.

INTRODUCTION

Nowadays, electrical energy request imposes the use of new

alternative sources such as green energy instead of actual

fossil sources. Photovoltaic energy is one of the most used

renewable energies. This has encouraged researchers for

developing the used techniques in this field over the years.

On another side, water-pumping is one of the most popular

applications of photovoltaic energy. In fact, there is a strong

relation between water needs and energy availability,

especially in hot weather. Hence, pumping water on sun wire

is a suitable solution for most rural and desert areas.

Several works have focused on photovoltaic water-pumping

systems. Works like [1,2] have been interested on sizing

photovoltaic water-pumping systems. Other works like [3,4]

treat the part of modelling of these systems. Many works have

shed light on extracting maximum energy from photovoltaic

sources like [5] where Maximum Power Point Tracker

(MPPT) algorithm were applied to a PV water-pumping

system. A cascade-sliding-mode control used the reference

trajectories of the currents, the rotor flux and the speed to

control an induction motor.

In this paper, we present the design of fuzzy logic control

used to improve the energy management related to the studied

system. Also we detail implementation of this controller on

Arduino Uno Board. At the beginning we should mention that

present work is a further work to [6] where an improved scalar

control is detailed. We will start with a description of the

studied system. Thereafter, we will focus on the fuzzy logic

controller and the structure used to improve the control law.

At the end we will present the obtained results and we will

interpret it.

SYSTEM OVERVIEW

The studied system is composed by a photovoltaic generator

(fig.1) which feeds an induction motor pump (element 2 in

fig.2) through a frequency converter (element 1 in fig.2). The

water is pumped from the left tank to the right tank. (element 6

in fig.2). The frequency converter is responsible of the pump’s

speed and consequently the flow of water.

Figure 1. Kaneka GSA60 photovoltaic field

Figure 2. Elements of the studied system

The studied system includes different sensors: pressure

(element 7), flow (element 5) and level (element 6). The pipe

(element 3) is connected by a valve assembly (element 4). For

this installation, we combine four photovoltaic panels in series.



10929

A. Photovoltaic panel

Photovoltaic generator can be modeled by:[7]

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Where Rs and Rp are the series and shunt resistors

respectively, Tref is the ambient temperature token as

reference, Irr is the irradiance, n is the ideality factor of the

diode, T is the temperature cell, K is the Boltzmann constant,

Eg is the gap energy, C is the number of cells (in series) per

module, Voc is the open circuit voltage of the module, Isc is the

short circuit current of the module, Ki is the coefficient of

temperature and q is the elementary charge in Coulomb.

The simulation of this model gives us the results shown in fig.3

fig.4 fig.5, and fig.6. From these results, the nonlinear nature

of the PV array is apparent. Therefore, we incorporate a fuzzy

controller to force the system to always operate at the

maximum power.

Table I shows panel's data at Standard Test Condition (STC)

for a Kaneka GSA 60 panel.

TABLE I. PARAMETERS OF THE PHOTOVOLTAIC

PANEL KANEKA GSA60

Pmax (W) 60

Vmpp (V) 67

Impp (A) 0.9

Voc (V) 92

Isc (A) 1.19

Figure 3. Curve Ppv(Vpv) with fixed Temperature.

Figure 4. Curve Ppv(Vpv) with fixed Irradiance.

Figure 5. Curve Ipv (Vpv) with fixed Temperature.

Figure 6. Curve Ipv(Vpv) with fixed Irradiance.

qTKVt.

ref

grefrss

TTnKqE

TTII 11

.

.exp

2

.

KCTnVq

II

oc

scrs

.

.exp

refiscrrph TTKIII

shdphpv IIII

1exp

t

pvspvsd

nCVIRV

II

p

pvspvsh

RIRV

I.



10930

B. Induction motor pump

The equation of the machine is then written in the following

form:

At the stator:

dtd

IRV

dtd

IRV

sqsqssq

sdsdssd

(8)

Where Rs is the stator resistances, Us(d,q) are the stator

voltages, is(d,q) are the stator currents and Φs(d,q) are magnetic

flow of the stator [8] .

At the rotor:

0

0

rqrq

rrq

rdrd

rrd

dtd

dtd

RV

dtd

dtd

RV

(9)

Where Rr is the rotor resistances, Ur(d,q) are the rotor voltages,

is(d,q) are the rotor currents and Φs(d,q) are magnetic flow of

the rotor.

The mechanical equation of the machine is:

remm TT

dtd

J

(10)

Ωm is the real speed, Tem is the electromagnetic torque, Tr is the

resistive torque and J is the moment of inertia.

The electromagnetic torque is given by:

)rqsdrdsqr

Mpem II

LLnT

(11)

The centrifugal pump is a rotary machine for communicating

to the pumped liquid sufficient energy to cause its movement

in a hydraulic network comprising in general a geometric

height level of elevation (Z), an increase in pressure (p) and

loss of loads. The calculation of centrifugal pumps is effected

by dimensional analysis and by Euler's theorem.

We define Q flow provided by a centrifugal pump as the

volume discharged during the time unit.

We define Hpompe , the energy supplied by the pump at the

weight unit of the liquid flowing through it. This height varies

with the flow rate and is represented by the characteristic

curve Hpompe = f (Q) for constant speed given by the

manufacturer.

Qb QΩbΩ b H m mpompe2

2102

(12)

The coefficients b0, b1 and b2 are based on the internal

geometry of the pump and independent of the speed of

rotation, can be determined experimentally by meeting three

points of the Hpompe characteristic = f (Q) given by the

manufacturer.

The centrifugal pump has a characteristic of the resistant

torque Cr() proportional to the square of its rotational speed

m given by the following equation aerodynamic[11]:

Cr() = C2 m2 (13)

C2 is the torque constant of the pump.

The useful mechanical power Pm supplied by the drive motor

to the pump is:

Pm = C2 m3 (14)

Mechanical losses applied to the shaft of the pump are

represented by CfV (), it is described by the following

expression in which C1 is the viscous friction coefficient.

Cfv = C1 m (15)

Couples presented above is added acceleration torque

J.dm / dt, where J is the total inertia of the mechanical system

and t is time, thus electromagnetic torque is described by the

following expression:

Cem = C2 m2 + C1 m + J

dtdΩm

(16)

To determine the operating point we began with the structure

below



10931

Figure 7. Synoptic of Hydraulic system.

With the generalized Bernoulli relationship, we can explain the

manometric height Hpompe of the pump by:

ipompe hi Z

ρgp

g H Z

ρgp

gv v

222

2

111

2

22

(17)

Hp is the total geometric height, p1 is the inlet pressure, p2 is

the outlet pressure, v1 is the suction velocity, v2 is the

discharge speed, ρgp

pressure height, g

v2

2

is the dynamic

height, Z is the position height and Σhi are the losses in the

suction and discharge.

Neglecting terms of speed and assuming p1 = p2, the term of

the height of the hydraulic system is given by:

Hcircuit = Hp + Q2 (18)

The operating point of the installation is the intersection of

Hpompe (Q) at constant speed and Hcircuit (Q) defined by the flow

rate Q on which the pump is automatically adjusted.

This intersection is expressed by the following relationship:

Q Ψ H Qb QΩbΩ b p m m22

2102 (19)

pmm H Ωb Q Ω b Q-Ψb 0201

22

(20)

This is a second degree equation Q, its resolution is used to

determine the water flow generated by the pump for a given

speed of rotation. The pump begins to generate a flow rate

from a minimum speed defined by [8]:

02

21

2min

4

4

bΨb b HXb Ω p

(21)

Particular way, we will discuss three possible cases following

the recorded value of the speed:

If Ωm < Ωmin no flow is generated by the pump, it means:

02

21

2min

4

4

bΨb b HXb Ω p

(22)

If Ωm = Ωmin the pump starts delivering water is:

Ψb Ωb QQ

2

min1min

2

(23)

If Ωm> Ωmin expression rate is given by:

Ψb

HΩb Ψb Ωb - Ω- b Q

pm mm

2

202

211

2

4 (24)

In the case where tanks are constantly connected to each other

and on the same plane (case of our application), there will then

2 21 1 0 2

2

4

2

nomm m

nom

b b b b X QQ Ω Ωb X Ω

(25)

Electrical specifications of induction motor-Pump are

described by table II.

TABLE II. SPECIFICATIONS OF EBARA INDUCTION

MOTOR-PUMP

Nominal output power (W) 370

Nominal electrical power(W) 550

Max Flow Rate (l/min) 35.60.9

Max head (m) 7

Statoric resistor() 24.6

Rotoric resistor () 16.1

Mutual self(H) 1.46

Rotoric self (H) 1.48

Statoric self(H) 1.49

C1(kg.m-2.s-1) 1.75. 10-3

C2(Kg.m-4.s-2) 7.5. 10-6

J(Kg.m-1) 6.5. 10-3

b0 (min2.m.tr-2) 4.52. 10-4

b1(m.min2.tr-1.L-1) -1.966.10-3

b2(min2.m.L-2) -0.012

Ψ(min2.m.L-2) 4.0816 10-

3



10932

PROPOSED CONTROL LAW

To operate our system at maximum efficiency, we developed a

new law which is able to find the speed reference applied to

the motor related to the irradiance and temperature.

First we take different points of irradiance, temperature and

corresponding speeds. These measures will help us to design

the fuzzy controller which will determine the appropriate

speed for each irradiance and temperature.

This new approach allows overcoming the conventional

methods using electronic devices such as the DC-DC

converter and its MPPT (Maximum power point tracking)

controller.

Figure 8. Synoptic of Hydraulic system.

The scalar control was detailed in our previous work [6]. We

focus on fuzzy controller.

The diagram of the fuzzy optimizer is explained by fig.9. The

design of a FL controller requires the passage through the steps

of fuzzification, find the inference rules and the step of

deffuzification.

Figure 9. Basic configuration of a fuzzy logic controller.

The design of fuzzy contrloller begin with fuzzification. This

step allows setting degrees of fuzzy variable membership

according to the real value. In our work, we use the triangular

and trapezoidal functions for input variables (Fig.10, fig.11 and

fig 12). They allow easy implementation and fuzzification step

then requires little computation time when evaluated in real

time.

Setting membership functions is made after many

measurements done on the system in open loop.

Figure 10. Membership functions of Irradiance.

Figure 11. Membership functions of Temperature.

Figure 12. Membership functions of frequency.

The second step is finding adequate inference rules. At this

level, we can determine the behavior of the fuzzy controller.

Rules are expressed as "IF THEN". In the fuzzy rules operators

"AND" and "OR" are involved. The operator "AND" refers to

variables within a rule, while the "OR" operator binds the

different rules. We use the method of MAMDANI [10] to

interpret these two operators.

The design of the table below (Table .III) was based on the

principles of a basic control system. We explain the operation

of fuzzy controller by the three blue rules for example.

If Irradiance is 1, and Temperature 1, then fuzzy controller

should set frequency at 2. In other word, when the irradiance is

very weak and Temperature is cold the frequency should be

small.

If Irradiance and Temperature are medium, then fuzzy

controller should set frequency at medium value.

Similarly if we have a high Irradiance and Temperature, then

fuzzy controller should set a high frequency. But the exact

values of the controller constants were found after performing

experiences on the studied system which helped us to define

each membership functions.

Photovoltaic

Generator

Three Phase’s

Inverter

Induction

Motor-Pump

Fuzzy

controllor

Scalar control

Mesured Three

Phases current

Optimal Speed

reference

Signal

Control

Temperature

Irradiance



10933

TABLE III. FUZZY INFRENCE RULES

Rule N° Irradiance Temperature Frequency

1 1 1 2

2 2 1 3

3 3 1 4

4 4 1 5

5 5 1 6

6 6 1 7

7 7 1 7

8 1 2 1

9 2 2 2

10 3 2 3

11 4 2 4

12 5 2 5

13 6 2 6

14 7 2 7

15 1 3 1

16 2 3 1

17 3 3 2

18 4 3 3

19 5 3 4

20 6 3 5

21 7 3 6

The Defuzzification converts fuzzy sets output in suitable real

variable such a process. Several Defuzzification strategies

exist; we used the method of '' gravity center ''. The gravity

center abscissa of the membership function resulting from the

inference is the output value of the controller.

This is the method which gives generally better results. The

results are stable relationships to changes in fuzzy set solution,

and therefore the system inputs.

FUZZY IMPLEMENTATION AND DISCUSSION

RESULTS

To highlight the purposed control, we have implemented the

fuzzy controller on Arduino Uno Board.

Fig.13. shows connection between the different elements of the

studied system. Climatic conditions (irradiance and

temperature) are acquired through a photo-resistance and

LM35 sensor (Fig.14). These two sensors are connected to the

ADC (Analog digital converter) of Arduino. The optimal

frequency is transmitted to the inverter through PWM (Pulse

width modulation).

Figure 13. Connection of Arduino uno Board to photovoltaic

pumping system.

The Arduino Uno board is a small microcontroller board [9].

This Microcontroller is based on the ATmega328. It is armed

by 14 digital input/output pins (of which 6 can be used as

PWM outputs), 6 analog inputs, a 16 MHz ceramic

resonator,32k Flash Memory, a USB connection, a power

jack, and a reset button.

We have used Arduino IDE environment, to implement the

software acquisition of climatic conditions and to set the

optimal frequency by the designed fuzzy controller. We use

several libraries such as libraries related to ADC and PWM.

Figure 14. Irradiation and temperature sensors connection to

Arduino.



10934

Figure 15. Input/output of the DV51-322-2K2 inverter.

The frequency inverter DV51, can be controlled by the control

unit DEX-KEY-61 or by external signals. In the first case, the

control unit allows the input and the display of several control

changes, monitoring the output of the current, and the specific

current magnitudes operation. In this case, different modes

can be selected and configured in staggered levels. The

second case presents our particular application. Indeed, the

fuzzy logic allows controlling the speed of the pump, through

the Arduino board which responsible of sending the desired

frequency to DV51-322-2K2 via its analog input O (0Volt to

10Volt corresponding to a frequency calibration variant 0Htz

to 50Htz), as shown in the following figures.

Figure 16. Cabling analog input o of DV51 with variable

external voltage.



10935

Figure 17. Process flowchart of the implemented algorithm.

Figure 18. Cabling analog input o of DV51 with variable

external voltage.

In order to construe different variables that characterize the

studied system, we present a detailed operating data acquired

for three typical days of March 2016 . Indeed, we were able to

study and evaluate the variation curves of:

Daily temperature Ta and irradiance Ec

Optimal frequency seted by fuzzy logic controller

Daily photovoltaic voltage Vpv

Daily speed of induction motorm

Quantity of pumped water during a mounth

Total efficiency nt

Fig.20, fig.21and fig 22 show respectively the daily changes in

ambient temperature Ta, photovoltaic panel temperature Tp and

solar irradiance Ec recorded during the months of July and

October 2016. We note that the temperature Tp roughly follows

the evolution of irradiance Ec with meadow coefficient.

For these climatic conditions, the generated statoric frequency

is linearly increasing according to irradiance and photovoltaic

temperature. However a 5% decrease of temperature causes a

decrease of 15% of frequency.

In addition to that, the stator frequency variation causes an

immediately variation of the photovoltaic variables such as

voltage Vpv.

Fig.23. shows that fluctuations in the photovoltaic voltage

range from 200 to 280V for Ta temperatures above 30 ° C

(July) and it range from 220 to 240V when temperatures is

below 30° C (October) and Vp voltage stays close to its

optimum value Vpopt. According to Vpv measurements, we note

that an increase on Tp brings a decrease in photovoltaic power.

Indeed 5% increase on temperature T produces a 2.78%

decrease on Vpv voltage. However, a 5% increase in irradiation

generates a low increase Vpv of (around 0.01%).

Fig.24 and fig.25 respectively show the daily variation in the

speed of induction motor m and the flow rate of pumped

water per month. Induction motor-pump starts to pump water

when irradiance exceeds 200 W/m2 which correspond to an

operating frequency about 22Hz. The maximum speed is about

280 rad/s which can give us about 32.1 l/min of pumped water.

This maximum speed is achieved at 44Hz.

The rate peaked about 280 rad / s corresponding to a flow of

water pumped in the order of 32 l/min to a maximum

frequency of about 44Hz. These values are acquired when

photovoltaic temperature is about 70 °C and irradiance equal to

1100 W/m2. These values decrease at autumn with a value

about 21%. In results, we can say that the quantity of pumped

water reaches 28 l/min in July and 22 l/min in October on

average.

We note that the total average efficiency nt is around 30%

(Fig.26). Therefore, we consider that the designed fuzzy

controller has allowed us to obtain optimal results against

climatic conditions changes and mostly for pumping system

which operates to sun wire.

Start routine of fuzzy

controller

ADC channel 1 configuration

ADC channel 2

configuration

Temperature Acquisition Irradiance Acquisition

Fuzzification Step

Fuzzy inference

Defuzzification Step

PWM pin9 configuration

Output corresponding voltage

(and then optimal frequency)



10936

Figure 19. Daily variation of irradiance

Figure 20. Daily variation of ambiante temperature

Figure 21. Daily variation of photovoltaic temperature

Figure 22. Frequency behavior

Figure 23. Photovoltaic voltage evoltion

Figure 24. Speed behavior of induction motor pump

Figure 25. Monthly flow rate average of pumped water.

Figure 26. Efficiency behavior.



10937

Figure 27. Hm(Q) characteristic for different speed value.

Figure 28. Pmec(Q) characteristic for different speed value.

Figure 29. np(Q) characteristic for different speed value.



10938

With the acquired data, we can determine the characteristic of

the manometric height Hm(Q), mechanic power Pmec(Q), and

np (Q) at different speed values of induction motor pump as

shown in fig.27, fig.28 and fig.29.

Overall, intersection between instant characteristic of

considered variables (brown curve) and curve that gives

constant frequency will determine the operating point of the

system composed by induction motor-pump and hydraulic

network. For example, with a frequency equal to 41.7 Hz, the

speed of induction motor-pump is about 2400 rpm (Cyan

curve). Thus, the operating point coordinates deducted are Q =

29 l/min, Hm = 3.27 m, Pmec = 219.8 W, np = 21.4%.

With a few mistakes meadows due to the measures we have

seen that the measured values are almost located on the

measured operating characteristic and issued by the

manufacturer.

CONCLUSION

In this document, we designed a fuzzy logic controller in

order to improve efficiency of the studied photovoltaic

pumping system. Then we have implemented this controller

on Arduino Uno Board. Finally, some measurements were

carried out on the experimental device; they allowed us to

validate the adopted control. This tests show that embedded

system such Arduino uno can be used for controlling complex

systems like photovoltaic pumping station. On the other hand,

we have shown the robustness of the designed controller

against climatic condition variations.

REFERENCES

[1] Ben Salah Ch, Ouali M. Energy management of

hybrid photovoltaic systems. Int J Energy Res

2012;36:130–8.

[2] Acakpovi A, Xavier FF, Awuah-Baffour R. Analytical

method of sizing photovoltaic water pumping system.

In: 2012 IEEE 4th international conference on

adaptive science & technology. ICAST; 25–27

October, 2012. p. 65–9,

[3] Bakelli Y, Arab AH, Azoui B. Optimal sizing of

photovoltaic pumping system with water tank storage

using LPSP concept. Sol Energy 2011;85:288–94.

[4] Ould-Amrouche S, Rekioua D, Hamidat A.

Modelling photovoltaic water pumping systems and

evaluation of their CO2 emissions mitigation

potential. Appl Energy 2010;87:3451–9.

[5] Marouani R, Sellami MA, Mami A. Cascade sliding

mode control applied to a photovoltaic water pumping

system with maximum power point tracker. In: 1st

IEEE international conference on advanced

technologies for signal and image processing (ATSIP

2014); 17–19 March, 2014. p. 328–33.

[6] Sassi F, Othmani.H, Mezghani D, Mami A.

“improved scalar control of an induction motor

pump”. International Journal of Applied Engineering

Research Volume 11, Number 15 (2016) pp 8728-

8732

[7] Othmani H, Mzghanni D, Belaid A, Mami A. “New

Approach of incremental Inductance Algorithm for

maximum Power Point Tracking Based on Fuzzy

Logic”. International Journal of Grid and Distributed

Computing Vol. 9, No. 7 (2016), pp.121-132

[8] Makhlouf M, Messai F , Benalla H, “Vectorial

command of induction motor pumping system

supplied by a photovoltaic generator”, J Electr Eng,

vol. 62, pp. 3–10, 2011.

[9] Banzi M, Getting Started with Arduino, 2 nd

Edition,Copyright, ©Massimo Banzi. All rights

reservedPrinted in the U.S.A, 2011.

[10] Mamdani EH, "Application of Fuzzy Logic to

Approximate Reasoning Using Linguistic Synthesis",

IEEE Trans. Computer, Vol. 26, N°.12, pp.1182-

1191,December1977.

[11] Betka A, Moussi A, Performance optimization of a

photovoltaic induction motor pumping system,

Renewable Energy, vol 29,pp 2167–2181, 2004.

frequency scheduling in a scalar control of induction ... of the currents, the rotor flux and the...

Documents