Scholarly article on topic 'Design and control of a standalone PV water pumping system'

Design and control of a standalone PV water pumping system Academic research paper on "Electrical engineering, electronic engineering, information engineering"

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{"Photovoltaic (PV)" / "Brushless direct current (BLDC)" / "Perturb and observe (P&O)" / "Fuzzy logic controller (FLC)" / "Alternative current (AC)" / "State of charge (SOC)"}

Abstract of research paper on Electrical engineering, electronic engineering, information engineering, author of scientific article — Essam E. Aboul Zahab, Aziza M. Zaki, Mohamed M. El-sotouhy

Abstract Water resources are vital for satisfying human needs. However, almost one-fifth of the world’s population – about 1.2 billion people – live in areas where water is physically rare. One quarter of the global population also live in developing countries that face water shortages. This paper presents standalone PV water pumping system. Photovoltaic (PV) is the main power source, and lead acid batteries are used as energy storage system, to supply a water pump driven by a BLDC motor. The proposed control strategy consists of three control units. The first unit is to control the speed and hysteresis current controller for BLDC motor. The maximum power point tracking (MPPT) is the second control unit, and the battery charging/discharging system is controlled by the third controller. The simulation results show the effectiveness and the good efficiency of the proposed system.

Academic research paper on topic "Design and control of a standalone PV water pumping system"

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Journal of Electrical Systems and Information Technology xxx (2016) xxx-xxx

Design and control of a standalone PV water pumping system

Essam E. Aboul Zahaba, Aziza M. Zakib, Mohamed M. El-sotouhy

a Department of Electrical Power and Machines, Faculty of Engineering, Cairo University, Giza, Egypt b Department of Power Electronics and Energy Conversion, Electronics Research Institute, Dokki, Giza, Egypt

Received 5 October 2015; received in revised form 22 February 2016; accepted 6 March 2016

Abstract

Water resources are vital for satisfying human needs. However, almost one-fifth of the world's population - about 1.2 billion people - live in areas where water is physically rare. One quarter of the global population also live in developing countries that face water shortages. This paper presents standalone PV water pumping system. Photovoltaic (PV) is the main power source, and lead acid batteries are used as energy storage system, to supply a water pump driven by a BLDC motor. The proposed control strategy consists of three control units. The first unit is to control the speed and hysteresis current controller for BLDC motor. The maximum power point tracking (MPPT) is the second control unit, and the battery charging/discharging system is controlled by the third controller. The simulation results show the effectiveness and the good efficiency of the proposed system. © 2016 Production and hosting by Elsevier B.V. on behalf of Electronics Research Institute (ERI). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Photovoltaic (PV); Brushless direct current (BLDC); Perturb and observe (P&O); Fuzzy logic controller (FLC); Alternative current (AC); Direct current (DC); State of charge (SOC)

1. Introduction

Water resources are vital for satisfying human needs, protecting health, and guaranteeing food production energy

and the rebuilding of ecosystems, as well as for social and economic development and for sustainable development. However, according to UN World Water Development Report in 2015, almost one-fifth of the world's population

(about 1.2 billion people) live in zones where water is physically rare. One quarter of the global population also live in developing countries that face water shortages (The United Nations World Water Development Report, 2015). Remote water pumping systems are a basic choice in meeting this need. Installation of a new transmission line and a transformer to the remote areas is often very expensive (Hmidet et al., 2014). Also the costs of fossil fuels and their environmental impacts rise, the demand for renewable energy sources increases (Aashoor and Robinson, 2013; Sreekumar and Benny, 2013). If the source of water is 1/3 mile (app. 0.53 km) or more away from the power line,

* Corresponding author. E-mail address: mohamedmostafa_87@yahoo.com (M.M. El-sotouhy). Peer review under the responsibility of Electronics Research Institute (ERI).

http://dx.doi.org/10.10167j.jesit.2016.03.003

2314-7172/© 2016 Production and hosting by Elsevier B.V. on behalf of Electronics Research Institute (ERI). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

E.E. Aboul Zahab et al. / Journal of Electrical Systems and Information Technology xxx (2016) xxx-xxx

PV module

Fig. 1. The proposed system.

photovoltaic (PV) is preferred as an economic choice (Oi, 2005). There are many techniques for maximizing the output power from the PV modules either conventional techniques like perturb and observe (P&O) or intelligent techniques like fuzzy control (Sreekumar and Benny, 2013). The photovoltaic pumping has turn out to be one of the most favorable fields in photovoltaic applications. For a standalone PV system there are mainly two types of pumps: centrifugal and positive displacement pumps. In the centrifugal pump, the rotation of an impeller forces water into the tube. The water speed and pressure depend on the available mechanical power at the rotating impeller and the total head, but the displacement pump uses a piston or a screw to control the water flow. The positive displacement pump grants a better efficiency under low power conditions than the centrifugal pump. The water pumps may be driven by many types of driving systems. The more popular are direct current (DC) motors, alternative current (AC) motors or BLDC motors (Mohammedi et al., 2013). Brushless DC (BLDC) motor drives have received wide care as their performance is superior to those of conventional brushed DC motors and AC motors. In small units up to 5.0 kW, BLDC motors are preferable and have increased the request in water pumping systems operated by a photovoltaic array because of their higher operating efficiency and good starting torque (Putta Swamy et al., 1995). Hysteresis current control is one of the simple PWM current control techniques which are used for minimizing commutation torque ripple, and are easy to implement (Das and Chanda, 2014). As photovoltaic produces electricity only when the sunlight exists, so stand alone PV systems need a backup energy storage which makes it available through the bad weather or night conditions. In standalone PV systems, among many possible storage mediums, batteries are commonly used as a storage element. The lead-acid battery is most common used with standalone PV systems because it is quite cheap and broadly available (Jaycar Electronics Reference Data Sheet, 2016). This paper presents an efficient PV water pumping system. It provides theoretical studies and modeling for all the system components, comparison between (P&O) & fuzzy maximum power point techniques, and provides the motor performance (speed, torque, etc.) results.

2. The proposed system

The stand-alone PV water pumping system consists of a single PV module of 300 W rating, a maximum power point tracking, a battery bank with charging controller, BLDC motor driving a positive displacement pump, and BLDC motor controller as shown in Fig. 1.

2.1. PV module model

There are various sizes of PV module available in the market. Usually, a number of PV modules are combined as an array (either series or parallel connection) to meet different energy demands. The size of the PV module selected for the proposed system is 300 W module. The selected module is IS4000P 300 W multi-crystalline PV module. As shown in Fig. 2, the model of the PV module can be represented as shown in the equivalent circuit (Fig. 2) as a current source in parallel with a diode (Mahmoud et al., 2012).

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Fig. 2. The PV model and its simulated electrical characteristics.

60 61 62

Fig. 3. General model of battery.

The PV cell electrical characteristics are nonlinear and vary according to the solar insolation (G) and the cell Q5 temperature (T). The PV output current Ipv can be stated by Eq. (1).

Ipv = Iph - h(e[

where Ipv is the output current from the PV cell, Iph is the photo generated current, I0 is the saturation current in the dark, q is the electron charge =1.6 x 10-19 (C), Vpv is the output voltage of the PV cell, n is the diode ideality factor, K is Boltzmann constant= 1.38 x 10 -23 (J/K), Tis the cell temperature (°C). The PV module output current of a number of cells connected in series (Ns) and number of cells connected in parallel (Np) can be stated by Eq. (2).

qx Vpv

Ipv = NpIph - NpI0(e(NsxnxKxT) - 1)

The simulated electrical characteristics of the adopted PV module at standard conditions (irradi-ance= 1000 W/m2&temperature = 25 °C) are shown in Fig. 2.

2.2. Battery model

The batteries are used for store the excess power and supply it to the load in bad weather conditions or in night periods. In the standalone photovoltaic systems the commercial rechargeable batteries in the market are: lead-acid or (Pb-S), the nickel-cadmium or (NiCad), the nickel-metal hydride or (NiMH), and the lithium-ion or (Li-ion) types. Lead-acid batteries are the most popular, and widely used in renewable energy systems. The equivalent circuit of a general battery dynamic model parameterized to characterize most popular types of rechargeable batteries. This model can be represented by a simple controlled voltage source in series with a constant resistance, as shown in Fig. 3, and described by Eqs. (3) & (4) (Tremblay et al., 2007).

E = E0 - K-

Q -f idt

Vbat. = E -i x R

+ A exp(-B / idt)

where E is the no-load voltage (V), E0 is the battery constant voltage = 12 V, K is the polarization voltage (V), Q is the battery capacity (Ah), /idt is the actual battery charge (Ah), A is the exponential zone amplitude (V), B is the

4 E.E. Aboul Zahab et al. / Journal of Electrical Systems and Information Technology xxx (2016) xxx—xxx

Fig. 4. Circuit diagram of delta connected motor.

82 exponential zone time constant inverse (Ah) \ Vbat is the battery voltage (V), R is the internal resistance (ohm), i is

83 the battery current (A).

84 2.3. BLDC motor model

Three phase BLDC motor connection may be star or delta connected, the motor used in the simulation is delta connected as shown in Fig. 4.

The line to line voltages and BEMFs are the same as phase voltages and BEMFs (Pillay and Krishnan, 1989a,b). Suppose Lab = Lbc = Lca=L, and Rab = Rbc = Rca=R.

V1 = i1R + L ( \+ei

V2 = i2R + L

V3 = i3R + L ( — ) +e3

di2 ~dt

where L is the armature self-inductance [H], R is the armature resistance [Œ], Va, Vb, Vc are the terminal phases or lines voltages [V], ii, i2, i3 are motor input currents [A], and ei, e2, e3 are motor back-EMFs [V]. In the 3-phase BLDC motor, the back-EMF is related to a function of the rotor position and the back-EMF of each phase has 120° phase angle difference so the equation of each phase should be as follows:

ea = Kwf (0e)«

eb = Kwf(0e - 120)« ec = Kwf (0e - 240)« e1 = ea - eb e2 = eb - ec e3 = ec - ea

(8) (9) (10) (11) (12) (13)

where Kw is the back EMF constant of one phase [V/rad s-1], 0e is the electrical rotor angle [°el.], m is the rotor speed [rads-1], ea, eb, ec are trapezoidal functions with 120° flat top, the electrical rotor angle is equal to the mechanical rotor angle multiplied by the number of pole pairs p:

0e = ( p ) 0,

E.E. Aboul Zahab et al. / Journal of Electrical Systems and Information Technology xxx (2016) xxx-xxx

106 where 0m is the mechanical rotor angle [rad].

107 The function F (0e) gives the trapezoidal waveform of the back-emf. One period of this function can be written as:

f (0e) =

0 <0 < — ~ 3

< 0 < v

-1 -n <0< —

6 ( 2n\ 5n -1 + 6 (0e + I") T <0< 2-Total torque output can be represented as the summation of that of the 3 phases. Next equation represents the total output torque in [Nm]:

eaia + ebib + ecic

The mechanical equation of the motor is as follows: fdm\

Te -Tl J

So the motor speed can be calculated as:

f (Te -Tl - Pm) m = -

where Tl is the pump load torque [Nm], J is the inertia of rotor and coupled shaft [kg m2], P is the friction constant [Nmsrad-1]. Then the rotor position is calculated by integrating the speed of the motor, and then we could calculate the phase currents and line currents by applying Kirchhoff's law at three nodes (a-c). These line currents are feedback to the hysteresis current control to get the state of the transistors.

120 3. Control strategy

121 In this work the control strategy is divided into three main control units. (1) First control unit is responsible for

122 speed and hysteresis current control for the BLDC motor pump, (2) second control unit is responsible for MPPT, and

123 (3) third control unit is responsible for charging and discharging of the battery bank.

124 3.1. Speed and hysteresis current control for the BLDC motor pump

125 Fig. 5 describes the basic blocks of the PMBLDCM drive. The drive contains speed controller, reference current

126 generator, PWM current controller, position sensor, the motor and the inverter. The purpose of a motor speed controller

127 is to yield a signal representing the required speed, and to drive a motor at that speed. Speed controller calculates the

128 difference between the reference speed and the real speed producing an error, which is fed to the PI controller. The

129 parameters of the PI controllers are obtained by using trial and error method. PI controllers are used widely for motion

130 control systems. The controller tries to minimize the error by adjusting the process control inputs. Here the proposed

131 technique consists of the outer speed loop and one inner current loop as the double-loop control system is introduced,

132 shown in Fig. 5. In the double-loop control system, a PI controller is adopted in the speed loop and a hysteresis current

133 controller is adopted in the current loop on the principle of hysteresis current track current controlled voltage source

134 inverter (Sanita and Kuncheria, 2013). Hysteresis current control is one of the simple PWM current control techniques

135 which are used for minimizing commutation torque ripple, and it is easy to implement. This simple control strategy

136 will be presented at low cost and uses simple structure and requires minor memory or processing abilities. This type of

137 BLDC drive is very suitable for renewable applications. Although hysteresis control is insensitive to motor parameter

138 variations, the stability of its normal operations has to be confirmed (Das and Chanda, 2014).

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Fig. 5. The basic blocks of the BLDC motor drive.

Table 1

Q8 The reference current depending on rotor position.

Rotorposition ( e)

Referencecurrents

o <e < n/3 n/3 <e <2n/3 2n/3 <e < n n <e<4n/3 4n/3 <e <5n/3 5n/3 <e <2n

Is Is 0

-Is -Is 0

-Is 0 Is Is 0

-Is -Is 0 Is Is

Table 2

Hysteresis current control logic (Putta Swamy et al., 1995). AIx Switch

Q1 Q2 Q3 Q4 Q5 Q6

AIA>h ON OFF OFF OFF OFF OFF

AIA< -h OFF OFF OFF ON OFF OFF

AIB>h OFF OFF ON OFF OFF OFF

AIB< -h OFF OFF OFF OFF OFF ON

AIC>h OFF OFF OFF OFF ON OFF

AIC< -h OFF ON OFF OFF OFF OFF

139 3.3.1. Reference current generator

140 The input signals needed for generating the reference currents (I*a, I*b, I*c) are the rotor position (0), and the

141 magnitude of the three phase currents (I*) generated from Eq. (19):

142 I* = — (19)

143 where Kt is the torque constant, T* is the reference torque value generated from the PI speed controller. The reason that

144 this is called a hysteresis controller is that the phase voltage switches to retain the phase currents within the hysteresis

145 bands. The hysteresis band has a width equal 2 h. Hysteresis current control can be implemented by computing reference

146 current I*x as shown in Table 1 and measuring the actual current Ix where x=a, b, or c and then calculate error signal

147 A Ix = I*x — Ix and then apply the logic shown in Table 2, which activate the switches shown above in Fig. 5. The

148 reference current values can be created with respect to the rotor position as shown in Table 1 (Putta Swamy et al., 1995;

149 Pillay and Krishnan, 1989a).

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Fig. 6. Perturb and observe algorithm (P&O) (Oi, 2005).

150 The hysteresis current controller gives the switching of 6 switches to the inverter devices shown up in Fig. 4. The

151 switching logic is formulated as given in Table 2.

152 3.2. The MPPT control unit

153 The MPPT control unit is used to keep the output power of the PV as maximum as possible. This control unit is

154 implemented using two MPPT algorithms: (1) P&O and (2) FLC as follows:

155 3.2.1. P&O algorithm

156 In this method the PV output current and voltage are measured (/pv, Vpv), and the operating voltage (Vpv) is perturbed

157 (increased) by a small decrease in the duty cycle D by a rate (dD) of the boost converter and observing the change in

158 power and the change in voltage then calculate the (dP/dV) value. If (dP/dV) is positive the perturbation of the operating

159 voltage will be in the same way of increasing so should continue decreasing the duty cycle of the boost converter. If

160 dP/dV is negative the operating voltage should be perturbed in the reverse direction (decreased). The maximum power

161 point is obtained when dP/dV = 0. The flowchart of this algorithm is shown in Fig. 6. (Oi, 2005; Elgendy et al., 2012).

162 3.2.2. FLC based MPPT algorithm

163 Fuzzy logic control (FLC) is used largely in control engineering and is very important when there is no exact

164 mathematical model or while the controlled process is nonlinear (Rebhi et al., 2013). Fuzzy logic controller has been

165 largely used for industrial processes in the recent years due to its simplicity and effectiveness for both linear and non-

166 linear systems. It consists of three blocks: Fuzzification, Fuzzy rules and inference engine, and finally Defuzzification

167 (Aashoor and Robinson, 2013).

168 3.2.2.1. Fuzzification. In the fuzzification stage, numerical input variables are transformed into linguistic variables

169 based on subsets called membership function. The proposed fuzzy logic controller has two input variables dPpv &

170 dIpv,and one output variable dD:

171 dPpv = Ppv(k) - Ppv(k -1) (20)

172 d/pv = Ipv(k) -/Pv(k -1) (21)

173 dD = D(k) -D(k -1) (22)

174 where dP is the change in PV power, dl is the change in PV current and dD is the change in duty cycle. Fig. 7 shows

175 the membership functions of the two inputs and the output fuzzy sets. Each fuzzy set has four membership functions as follows: PB (Positive Big), PS (Positive Small), NS (Negative Small) and NB (Negative Big).

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Table 3 Fuzzy rules.

PB PS NS NB

PB PB PS NS NB

PS PB PS NS NB

NS NB NS PS PB

NB NB NS PS PB

CHARGE DISCHARGE

Fig. 8. Intermittent charging & discharging control.

176 3.3.2.2. Fuzzy rules and inference engine. Fuzzy rules include a set of rules in linguistic form which subordinate

177 the fuzzy inputs with the fuzzy output. These are created by an expert knowledge and understanding of the system

178 performance that is essential to realize the control objectives. The fuzzy control rules have been established using a

179 set of IF-THEN rules as defined in Table 3.

180 3.2.2.3. Defuzzification. In the defuzzification step the output of fuzzy logic control is converted from linguistic

181 variables to numerical variables, where in this process the crisp output of the FLC (dD) is calculated. There are

182 different approaches for defuzzifying a result fuzzy set. The method used in this paper is called the Center of Gravity

183 (Aashoor and Robinson, 2013). FLC can track the MPP rapidly with small oscillations around it (Mansour et al., 2015).

184 3.3. The battery bank charging and discharging control technique

185 There are many battery charging algorithms used to keep the battery at a high state of charge and increase the life

186 time of the battery. The intermittent charging control is the most generally used technique in commercial chargers

187 (Armstrong et al., 2008). In the charging mode, the battery is charged with maximum power point tracking (MPPT)

188 between two predefined voltage edges as shown in Fig. 8. When the battery reaches the upper voltage edge, at point

189 1, the charging is stopped, then the battery voltage is observed until it drops to the lower voltage edge, at point 2, then

190 the charging begins again. The same process is repeated at the discharging mode process to protect the battery bank

191 from deep discharging, when the battery voltage reaches the edge point 4 the discharge from battery bank should stop,

192 and repeat discharging from batteries after their voltage become larger than or equal to the voltage at edge point 3. So,

193 we could protect the battery bank from overcharging and deep discharging.

194 4. Simulation results

195 The system is simulated in the MATLAB/SIMULINK program with sample time equal to two micro seconds using

196 two MPPT techniques (P&O, and fuzzy) using the same motor control (outer speed control, inner hysteresis current

197 controller).

10 E.E. Aboul Zahab et al. / Journal of Electrical Systems and Information Technology xxx (2016) xxx-xxx

Fig. 9. Motor rated speed and rated torque.

"1-1-r

,+tï)l

r ' 1 1

0.04 0.05

time (sec)

Fig. 10. Motor three phase rated currents.

198 4.1. BLDC motor pump simulation results

199 Suppose the motor works at nominal speed and at nominal voltage as in Appendix A. The rated speed and rated

200 torque, the three phase currents, rotor position, and BEMFs are shown in Figs. 9-12 respectively.

201 4.2. MPPT simulation results using P&O technique

202 Suppose the module temperature is constant at 25 °C and the irradiance is variable and change through a day as (0.4,

203 0.6, 0.7, 0.8, 0.9, 1, 0.9, 0.8,0.7, and 0.3), and the step change in duty cycle is dD = 0.001. The results show the output

204 module power through a day (represented in simulation time by one second) in Fig. 13 using P&O MPPT technique.

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0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

time (sec)

Fig. 11. Motor rotor positions.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

time (sec)

Fig. 12. Motor three BEMFs.

205 4.3. MPPT simulation results using fuzzy technique

Suppose the module temperature is constant at 25 °C and the irradiance is variable and change through day as (0.4, 0.6, 0.7, 0.8, 0.9, 1, 0.9, 0.8, 0.7, and 0.3). The results show the output module power through a day (represented in simulation time by one second) with MPPT using fuzzy intelligent techniques shown in Fig. 14.

209 4.4. Simulation results of battery intermittent charging control algorithm

210 211 212

The intermittent charging control has two bands to protect the battery from over charging where batteries SOC supposed (98:100%) and lower band to protect the batteries from the deep discharging where batteries SOC supposed (25:20%).

Suppose the batteries initial state of charge (SOC) is 30%, and the weather is bad or at night, then the batteries discharge until the SOC becomes <20% (at point 4) here stop the discharge process by switching off a switch supposed it (s2). And when the battery recharges again, the supposed switch (s2) becomes ON but after the SOC reaches >25%

E.E. Aboul Zahab et al. / Journal of Electrical Systems and Information Technology xxx (2016) xxx-xxx 300 ^

0.4 0.5 0.6 time (sec)

Fig. 13. Module output power through a day with MPPT using P&O technique.

a- 150

_ 1 L J L

i i i

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

time(sec)

Fig. 14. Module output power through a day with MPPT using fuzzy intillegent technique.

216 (at point 3) which represent minimum allowable battery charging while driving the load. Also suppose the battery

217 continue charging till reaching the maximum charging value of the upper band 100% (at point 1). In this case we

218 should disconnect the current supplied to the batteries to protect them from overcharging by switching OFF a supposed

219 switch (S1), and reswitching it ON again if the SOC is lower or equal to the minimum upper band 98% (at point 2) as

220 shown in Fig. 15, and the supposed switch S2 is at ON state as shown in Fig. 16.

5. Discussion and conclusions

222 The standalone PV water pumping system used is described first. It consists of a PV array, a maximum power

223 point tracking (MPPT) system controlling a DC-DC boost converter which drives a BLDC motor driving a positive

224 displacement water pump. Two MPPT techniques are introduced P&O method and FLC method, and the two methods

225 are compared. From the simulation results it can be noticed that FLC is faster and has lower oscillation around the

226 maximum power point than the P&O. The battery backing system is also designed together with its control system

227 to satisfy system requirements all the time. Simulation results presenting the system performance were presented and 228Q6 discussed. By apply this system in multiple or large scale system could solve the problem of water in remote areas

100 90

S 20 10

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Switch (s1) status battery SOC(%)

time(sec)

Fig. 15. Battery SOC (%) with switch (S1) status.

Fig. 16. Battery SOC (%) with switch (S2) status.

229 and could extract it for one-fifth of the world's population (about 1.2 billion people) who live in zones where water is

230 physically rare.

Q7 Appendix A. BLDC motor parameters

Motor data Unit Value

Number of pair poles 1

Power rating W 101

Nominal voltage V 48

Rated speed rpm 10,000

Rated torque mNm 40

No load current A 0.109

Terminal resistance phase to phase Ohm 4.4

Terminal inductance phase to phase mH 0.678

Torque constant mNm/A 37.02

BEMF constant mV/rpm 3.877

Rotor inertia 2 gcm2 34

Friction torque mNm/rpm 2.4x10

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