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Photovoltaic pumping system in Bejaia climate with battery storage
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 5 ) 1 e1 1
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Photovoltaic pumping system in Bejaia climate with battery storage K. Rahrah a,*, D. Rekioua a, T. Rekioua a, S. Bacha b Laboratoire de Technologie Industrielle et de l'information (LTII), Faculte de Technologie, Universite de Bejaia, Algeria b G2lab INPG Grenoble, France a
article info
abstract
Article history:
The use of solar energy especially for water pumping is well adapted for most rural and
Received 8 February 2015
desert areas. The latter have a great solar energy potential and a shallow underground
Received in revised form
hydraulic potential. Solar pumping is an exception; it is in fact relatively easy to store
10 April 2015
potential energy of water with a more reliable elevated tank. In this paper, we present
Accepted 11 April 2015
modeling and sizing of photovoltaic pumping system with battery storage. To track
Available online xxx
maximum output power operating point of the photovoltaic generator, we use three optimization methods: Perturbation and Observation (P&O), Fuzzy Logic Controller (FLC)
Keywords:
and Neuro-Fuzzy algorithm (NF). Batteries are added for the purpose of ensuring contin-
Photovoltaic system
uous power flow. The storage battery model used is presented with obtained measurement
PV pumping
results. The obtained simulations developed under Matlab/simulink package, show the
Sizing
robustness and effectiveness of the Neuro fuzzy controller.
MPPT
Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Battery storage
Introduction In photovoltaic (PV) water pumping systems, maximum power point tracking (MPPT) is usually used as online control strategy to track maximum output power operating point of the photovoltaic generator (PVG) for different operating conditions of insolation and temperature of the PVG. Although, the use of the MPPT control do not mean a systematic optimization of the motor or the whole system efficiency [1,2]. Thus, several control strategies of optimization allowing the improvement of the photovoltaic water pumping system operation were applied to the efficiency maximization of the PVG. In these existing MPPT control methods, the P&O is used in many PV systems more widely than others. The P&O method can work well when the solar irradiance and the
temperature do not vary quickly with time. However, it cannot track the maximum power point (MPP) quickly and output power is oscillating around the MPP. But, relatively complex computations and decision require a more complex microcontroller with more memory. This makes the implementation of this method relatively difficult and the cost of MPPT controller high. Other intelligent based control schemes have been introduced (fuzzy logic, neural network) [5,11,13]. The inputs of the fuzzy logic controllers are an error and an error variation, the output is a duty cycle or its variation. The fuzzy controller introduced in Ref. [5] uses Ppv, Vpv and its variations as inputs, as output, it determines the optimal increment which must be added to the operating voltage for tracking the MPP in order to assure fast and fine tracking. The hybrid modeling methodology, exploiting the characteristics of fuzzy logic and neural networks theories, to deliver the maximum
* Corresponding author. Tel./fax: þ213 34215090. E-mail addresses: [email protected] (K. Rahrah), [email protected] (S. Bacha). http://dx.doi.org/10.1016/j.ijhydene.2015.04.048 0360-3199/Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
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Nomenclature A ideality factor of the junction constants aij hydraulic constant CH electromotive force, V Ebatt hours of radiation per day, H Ens solar radiation, W/m2 Es e0 electron charge, C battery current, A Ibatt diode current, A Id output-terminal current, A Ipv diode-current, A Ip shunt-leakage current, A Ish short circuit current, A Isc saturation current of the diode, A Isat maximum current at PPM, A Impp g acceleration of gravity, m/s2 H total head, m K Boltzmann constant, Joule/K K1, K2 and K3 adaptive gains maximum power point, W Pmpp photovoltaic power, W PPV Q water flow, m3/s Qdesired desired flow flow obtained with MPPT QMPPT internal resistance, U Rbatt series resistance, U Rs shunt resistance, U Rsh performance motor pump group Rmp temperature cells, K TJ reference temperature of the PV cell, K TJref battery voltage, V Vbatt maximum voltage at PPM, V Vmpp open circuit voltage, V Voc Greek letters temperature coefficient of short-current, A/K asc voltage temperature coefficient, V/K boc r mass density of water, Kg/m3 Abbreviations FLC fuzzy logic controller MPP maximum power point MPPT maximum power point tracking PV photovoltaic PVG photovoltaic generator P&O perturbation and observation NF neuro-fuzzy algorithm available power from the PV system under different weather conditions, presents much high computation [14,15]. The paper is organized as follows: in Presentation and modeling of the studied system mathematical model of the photovoltaic water pumping system (PVG, electrical motors and centrifugal pump), and the structure configuration of the (P&O), Fuzzy Logic Controller (FLC) and Neuro-Fuzzy algorithm (NF) applied to the global efficiency optimization of the PV water pumping system is presented. Sizing of the PV water pumping system study in Bejaia climate is presented in Sizing
of photovoltaic pumping system study, while in Applications of different MPPT for solar pumping system, the obtained simulations results, using Matlab/simulink package are given and interpreted. Finally,Conclusion concluded the work.
Presentation and modeling of the studied system Presentation Generally, the photovoltaic pumping systems consist of a photovoltaic generator and a pumping subsystem. These systems operate over the sun without electrochemical storage. The water pumped can be used directly or stored in a tank for later use. This type of water storage is the solution most widely adopted compared to electrochemical energy storage in batteries. Fig. 1 describes the system studied in this article. It consists of a photovoltaic field, a DC/DC converter, a DC/AC converter, asynchronous motor coupled to a centrifugal pump, and a tank for storage of pumped water. To ensure operation at maximum power point of the PV generator, three methods are applied to the system are (P&O, FLC and NF).
Modeling of the PV generator In the literature, there are several mathematical models that describe the operation and behavior of the PV generator [2]. The models differ in the calculation procedure, the precision and the number of parameters involved in the calculation of the currentevoltage characteristic. In this work, we used an analytical model that gives very good information. The equivalent diagram of this model is given in Fig. 2 [3]: The current generated by the module is given by: Ipv ¼ Ip Id Ish
(1)
Current Ip is directly dependent on solar radiation Es and cell temperature Tj. It is given by the following relation [4]: Ip ¼ P1 $Es 1 þ P2 Es Eref þ P3 TJ TJref
(2)
The diode current is given by: e0 $ðV þ Rs $IÞ 1 Id ¼ Isat $ exp A$ns $k$TJ
(3)
With Isat is the saturation current, highly dependent on the temperature, given by: Eg Isat ¼ P4 $T3J $exp k$TJ
(4)
The current of the shunt resistance is calculated by: Ish ¼
ðV þ Rs $IÞ Rsh
(5)
Ipv ¼ P1 $Es $ 1 þ P2 $ Es Eref þ P3 $ TJ Tref Eg ðV þ Rs $IÞ P4 $T3j $exp $ exp e0 $ 1 A$ns $k$TJ k$TJ Vpv þ Rs $I Rsh (6) with:
Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Fig. 1 e Description of the photovoltaic pumping system. Ip: current generated by the illumination (A), Id: the diode current (A), Ish: current of the shunt resistance (A), Isat: saturation current of the diode (A), Rs: series resistance (U), Rsh: shunt resistance (U), k: Boltzmann's constant (k ¼ 1.381023 (SI)), e0:electron charge (1.6021019 C), Tj: junction temperature (K), A: ideality factor of the junction. The parameters (P1, P2, P3, P4, A, Rsh and Rs) (Table 1) were obtained by numerical solution (Newton Raphson). Table 2 shows panel's data at STC for a SM 110-24 panel which is used in simulations.where: Vmpp: maximum power point voltage; Voc: open circuit voltage; Impp: maximum power point current; Isc: short circuit current; asc: current temperature coefficient; boc: voltage temperature. The curves currentevoltage Ipv (Vpv) and powerevoltage Ppv(Vpv) of the photovoltaic panel, are carried out by varying the load's resistance for three levels of irradiance and temperature (Figs.3 and 4). The experimental characteristics obtained are compared to the simulation characteristics for the same operating conditions (Es ¼ 530 Wm2, T ¼ 27,9 C; Es ¼ 620 Wm2, T ¼ 33 C; Es ¼ 750 Wm2, T ¼ 36 C). The PV panel SM 110-24 is located at the University of Bejaia. From the current and power characteristics (Fig.4.), the nonlinear nature of the PV array is apparent. Therefore, an MPPT algorithm must be incorporated to force the system to always operate at the maximum power point (MPP).
Modeling subsystem pumping Pump model Many different varieties of pumps are used with PV-pumping system. In our case, we use the model expresses the water
flow output (Q) directly as a function of the electrical power input (P) to the motor-pump, for different total heads. A polynomial fit of the third order expresses the relationship between the flow rate and power input, as described by the following equation [6,7]: PðQ; hÞ ¼ a0j ðhÞQ 3 þ a1j ðhÞQ 2 þ a2j ðhÞQ þ a3j ðhÞ
(7)
With: aij ðhÞ ¼
3 X
aij h
j
(8)
j¼0
where: aij are constants to be determined for each subsystem pumping.
Model of induction motor The mathematical model of asynchronous induction motor is given by the following equations [8,9]: - Stator voltage equations: 8 dFsd > > < Vsd ¼ Rs Isd þ dt > > : Vsq ¼ Rs Isq þ dFsq dt
(9)
- Rotor voltage equations: 8 dFrd dq > > Frq þ < 0 ¼ Vrd ¼ Rr Ird þ dt dt > > : 0 ¼ Vrq ¼ Rr Irq þ dFrq dq Frq dt dt
(10)
where: (Isd, Isq, Ird, Irq, Vsd, Vsq,Vrd, Vrq) and (Fsd, Fsq, Frd, Frq) are current, voltage and flux in the reference frame (d, q) of the stator and the rotor. Rs, Rr: are the stator the rotor resistances. - Mechanical equation:
Table 1 e Parameters of the PV panel SM 110-24 (Newton Raphson resolution). P1 Fig. 2 e Simplified equivalent circuit of solar cell.
0.00345
P2 0.58*10
P3 5
0.336*10
4
P4
A
Rsh
Rs
31.23
1
0.614
151.16
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Table 2 e Parameters simulations of the PV panel SM 110-24. Ppv 110 Ws
Impp
Vmpp
Isc
Voc
asc
boc
3.15 A
35 V
3.45 A
43.5 V
1.4 mA/ C
152 mV/ C
inverter. This will save one stage in the system and therefore will increase simplicity and efficiency. Many algorithms have been developed for tracking maximum power point of a solar cell [5,10]. In our work, we use the perturbation and observation (P&O), fuzzy logic (FLC) and Neuro Fuzzy (NF).
P&O method
Fig. 3 e Circuit measurement of Ipv (Vpv) and Ppv(Vpv) of the PV panel SM 110-24.
dur Tmot TLoad ¼ Jmot $ dt
(11)
ur: motor angular velocity, Jmot:moment of inertia.
This is the method most used saw its simplicity. A feedback loop and few measures are necessary. The voltage across the panel is deliberately disturbed (increased or decreased) with a constant (C) then the power is compared with that obtained before disruption. Specifically, if the power terminals of the panels are increased due to the perturbation, the following perturbation is made in the same direction. Conversely, if the power decreases, the new disturbance is performed in the opposite direction [11,12]. Easy to use and implement in a control system of a photovoltaic panel, the P&O but has drawbacks. Indeed, it happens that the P&O algorithm diverges from the maximum power point in case of very rapid fluctuation in the sunshine. Even if the operating conditions constant, variations of the voltage and current which generates oscillations around the point of maximum power is observed (Fig.5).
- Electromagnetic torque:
Method based on fuzzy logic (FLC)
Tmot
¼ P$ fsd $isq fsq $isd
(12)
P: is the pole pair number of the AC machine
MPPT controller Usually the MPPT controls a DC/DC converter that is generally placed between the PV array and the inverter, to maintain a constant DC voltage at the output of the generator. With an appropriate sizing of the PV array, the DC/DC converter can be avoided, due to the relatively small changes in the optimum voltage in operating condition and moving the MPPT to the
In recent years, fuzzy logic controllers (FLC) are widely used for the MPP search [5]. These are independent of the process model; they are characterized by their ability to understand the problems of nonlinearity and exhibit robust performance with respect to changes of weather and load. We present a method of MPPT using the theory of fuzzy logic to address the oscillation of the perturbation and observation (P&O) problem. The proposed fuzzy controller optimizes the amplitude of the oscillations to minimize disturbance and have a quick response and without oscillations. The fuzzy logic controller measures the PV array characteristics and then perturbs the operating voltage by an optimal increment (DVpv,ref) and the resulting PV power change. The power variation (DPpv) is either in the positive direction or in the negative one. From
Fig. 4 e Experimental and simulation curves Ipv(Vpv) and Ppv(Vpv). Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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5
Fig. 7 e Structure of MPPT Neuro Fuzzy controller.
Fig. 5 e Flowchart of Perturb and Observ method.
Table 3 e Fuzzy rule table. DPPV
VPV
BN N Z P BP
BN
N
Z
P
BP
BN BN N N Z
BN N N Z P
N N Z P P
N Z P P BP
Z P P BP BP
these inferences, the reference photovoltaic voltage variation (DVpv,ref) is increased or decreased in the direction which makes it possible to increase the power Ppv. The control rules are indicated in Table 3 with (DPpv) and (DVpv) as inputs, while (DVpv,ref) represents the output. These inputs and output variables are expressed in terms of linguistic variables (such as BN (big negative), N (negative), Z (zero), P(positive), and BP (big positive)). From these linguistic rules, the FLC proposes a variation of the reference voltage DVpv,ref according to Eqs. (12)e(14).
DPpv ¼ Ppv ½k Ppv ½k 1
(12a)
DVpv ¼ Vpv ½k Vpv ½k 1
(13)
Vpv;ref ½K ¼ Vpv ½k 1 þ DVpv;ref ½k
(14)
where: Ppv[k] and Vpv[k] are the power and voltage of the photovoltaic generator at sampled times (k), and Vpv,ref[k] the instant of reference voltage used to have the duty ratio. The fuzzy logic controller structure is show in Fig. 6. Where K1, K2 and K3 are adaptive gains. The bloc fuzzy logic controller includes three functional blocks: fuzzification, fuzzy rule algorithm, and defuzzification. The membership functions of inputs variables DPpv and DVpv are triangular and have five fuzzy subsets. Five fuzzy subsets are also considered for the output variable DVpv,ref. The fuzzy inference is carried out by using Mamdani's method, and the defuzzification uses the center of gravity to compute the output of this FLC.
Method Neuro Fuzzy (NF) [13e15] Neuro-fuzzy systems are fuzzy systems formed by a learning inspired by the theory of neural networks algorithm. The learning technique operates according to the local information and produces only local changes in the fuzzy system origin. The neuro-fuzzy system used here is the adaptive networkbased fuzzy inference system (ANFIS). The system is an adaptive network functionally equivalent to a first-order Sugeno fuzzy inference system. The neuro-fuzzy MPPT controller developed in this part includes two inputs DPpv and DVpv and a
Fig. 6 e Structure of MPPT fuzzy controller. Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Fig. 9 e Equivalent circuit model CIEMAT.
Layer 2: The nodes in this layer are fixed. Each node output represents a firing strength of a rule. Layer 2 implemented the fuzzy product operator. The output of the Layer 4 is comprised of a linear combination of the inputs multiplied by the normalized firing strength w. O1i ¼ mAi ðxÞ$mBi ðxÞ ¼ wi i ¼ 1; 2
(16)
Layer 3: In this layer, where the normalization process performed acts to scale the firing strengths, the nodes are fixed. The rule's firing strength to the sum of all rule's firing strength is calculated for the corresponding node. O3i ¼ wi ¼
wi þ w2 w1
(17)
Layer 4: The adaptive nodes in this layer operate as a function block whose variables are the input values and pi, qi, and ri is the consequent parameter set of the node. O4i ¼ wi $f i ¼ wi $ pi $x þ qi $y þ r Fig. 8 e a) Waveform of photovoltaic voltage under the standard test conditions (1000 W/m2, T ¼ 25 C). (b) Photovoltaic voltage at a transitional state (Zoom 1). (c) Photovoltaic voltage at a steady state (Zoom 2).
single output DVpv,ref which represent the variation of the photovoltaic power, photovoltaic voltage variation, and control. The two inputs variables generate action DVpv, ref to be applied to the chopper control, in order to adjust the input voltage thereof so as to ensure the adaptation of the power supplied by the photovoltaic generator. The equivalent ANFIS architecture of a first-order Sugeno fuzzy model with two rules shown in Fig. 7. The model has five layers and every node in a given layer has a similar function. The fuzzy IF-THEN rule set, in which the outputs are linear combinations of their inputs, is:
(18)
Layer 5: Simple summation of Layer 4 outputs. The adjustment of modifiable parameters is a two-step process. First, information is propagated forward in the network until Layer 4, where a least-squares estimator identifies the parameters. Then, the parameters in Layer 2 are modified using gradient descent. The only user specified information is the number of membership functions for each input and output as training. O5i ¼
X Wi $f i
(19)
i
Applications of different MPPT for PV generator As a first step, we simulate the panel part with and without MPPT for operation under constant conditions (1000 W/m2,
Rule 1 if x is A1 and y is B1 then f1 ¼ p1x þ q1x þ r1 Rule 2 if x is A2 and y is B2 then f2 ¼ p2x þ q2x þ r2 Layer 1: Consists of membership functions (A1, A2, B1, B2), where the fuzzification process takes place, and every node is adaptive. (15) O1i ¼ mAi ðxÞ where x is the input of node i, and Ai or (Bi) is a linguistic label (such as “small” or “large”) associated with this node.
Fig. 10 e Battery capacity variation for different temperatures.
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Fig. 13 e Variation of insolation for summer day in Bejaia. Fig. 11 e Variation of the state charge for different temperatures. method requires much time to track the MPP and presents oscillations around the MPP at a steady state. T ¼ 25 C). Fig. 8 a shows the shape of the voltage PV generator with and without MPPT. A zoom at a transitional and steady state is shown in Fig. 8 b and Fig. 8 c. We note on Fig. 8 b and Fig. 8 c that NF control has a faster response compared to other types of MPPT control and without MPPT. And the P&O
Battery storage We have opted for the CIEMAT model. It is characterized by setting a series of women with a variable resistor (Fig. 9.). The
Fig. 12 e a) Geographical situation of Bejaia. b) Solar radiation (Es) during a year in Bejaia. Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Table 4 e Steps Sizing of PV pumping system. Electric power required by the pump The peak power of the PV generator Number of panels series Number of panels parallel
Eele ¼ (CH*Q*HMT)/Rmp
Eele ¼ (2.725*25*10)/0.44
1548.29 Wh/day
Ppvtot ¼ Eele/[Ens*(1pertes)] Ns ¼ (Ppvtot/Ppv) Np ¼ Ipv/Ipvt
Ppvtot ¼ 1548.29/[2.2*(10.2)] Ns ¼ 880/110 Np ¼ 3.15/3.14
880 W 8 1
characteristics of the source voltage Ebatt and internal resistance Rbatt depend on temperature and battery charge state. For a number nbatt of cell, voltage equation is: Vbatt ¼ nbatt $Ebatt ±nbatt $Rbatt $Ibatt (20) with: Vbatt: battery voltage, Ibatt: battery current, Ebatt: electromotive force depending on the battery charge state, Rbatt: internal resistance which varies with the state of charge. The battery behaves as complex impedance Zbatt containing a resistance Rbatt and a reactance Xbatt to this disturbance. The battery behaves as complex impedance Zbatt with a resistance Rbatt and a reactance Xbatt to this disturbance. jZbatt j ¼
jVbatt j jIbatt j
Algeria (Fig. 12 a and b). PV generator is sized relative to the worst month. Fig. 13 shows that December is the month where solar radiation Es is the worst in Bejaia. The steps of sizing of the PV pumping system are summarized in the (Table 4).with:
(21)
The module of the complex impedance is thus well defined by the ratio of the absolute values of the two signals. We deduce the dephasing by the temporal difference between the two signals with the passage by zero. Knowing the module of Zbatt and dephasing, we can thus deduce the real part Rbatt and imaginary Xbatt of the impedance for his state of charge, these values changes according to the latter. We obtain: Rbatt ¼ 0.577U, Xbatt ¼ 0.15U and Cbatt ¼ 21.22 mF. We give some simulation results (Figs. 10e11). We note that when the temperature increases, the behavior of the battery is saved by increased as well. The value of the battery internal resistance decreases rapidly with increasing temperature, which is mainly due to the change of electrolyte resistance.
Sizing of photovoltaic pumping system study Estimated water needs The studied pumping system is sized to supply drinking water to families in a village in Bejaia whose water needs are estimated at 25 m3/h, the total head H ¼ 10 m, the thank is 100 m3.
Sizing the pump unit Due to the characteristics of the induction motor of the pump unit (Pn ¼ 746 W, In ¼ 3.4 A), the panels must be connected in series.
Sizing the PV generator Due to its geographical location, Algeria has one of the highest solar fields in the world. Regarding the region of Bejaia (36 430 N 5 040 E 2 m), which is a coastal city in the North East of
Fig. 14 e a) Profile of solar radiation. (b) Photovoltaic power. (c) Photovoltaic power at transitional state (Zoom 1). (d) Photovoltaic power at steady state (Zoom 2).
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Fig. 15 e a) Irradiation profile in a summer day at Bejaia. (b) Photovoltaic power. c Induction machine rotational speed. (d) Water flow. (e) Water flow (zoom). (f) Water flow in function of the irradiation Es.
CH: Hydraulic constant, CH ¼ r*g/3600, g: Acceleration of gravity (9.81 m/s2), H: Total head (m), Q: Water flow (m3/s), r: Mass density of water (1000 kg/m3), Rmp: Performance Motor pump group, Ens: hours of radiation per day (H). The total output voltage of the PV generator is: VPVtot ¼ NPV $VMPP ¼ 8*35 ¼ 280V The total output current of the PV generator is: IPVtot ¼
PPVtot 880 ¼ 3:14A ¼ VPVtot 280
Applications of different MPPT for solar pumping system
between the three methods P&O, FLC and NF will be presented for two operations.
Simulation under varying conditions To study the behavior of the system to changes in illumination we will make a quick decrease in the sunshine at a constant temperature (T ¼ 25 C). In starts with solar radiance Es ¼ 1000 W/m2 and at time t ¼ 5 s is decreased to Es ¼ 700 W/ m2, the results are presented in Fig. 14 a.
Table 5 e Comparison between the different MPPT. Performances
Different simulations were performed on the studied system. It is composed of eight (08) photovoltaic panel of 110 W connected in series. The various parts of the system (photovoltaic panel, DC/DC converter, DC/AC converter, induction motor and centrifugal pump) are modeled by separate block then related in a coherent way. In this section, in simulates the complete system with MPPT to make a comparative study
Response time(H) Speed(rad/s) Efficiency (%) DQ (m3/s) Error (%) DT (H)
Methods Without MPPT
P&O
FLC
NF
7 35 21 1.5$103 76.8 7
7 35 21 1.5$103 76.8 7
6.010 90 56.54 4.2$103 37 0.99
4.625 125 79 6$103 13 2.375
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For a quickly variation of solar radiation (1000 W/m2 to 700 W/m2), we note that the Neuro Fuzzy algorithm responds better than the two others.
hð%Þ ¼
Ph *100 Pmec
(23)
With:
Q desired ¼ 25m3 h ¼ 0:006m3 s
Simulation for a daily profile In this part, we have given a simulation for a daily profile to see the response of MPPT algorithms throughout the day, the results are given below (Fig. 15aef): It is clear in (Fig. 15b) that the system operation is improved by using the NF algorithm; we can extract a higher power compared to other types of MPPT control, and the operation without MPPT. One sees clearly that the speed and throughput are improved by the NF algorithm. It also allowed us to gain almost 1 h of time compared to other types of MPPT control, and 2 h compared to the system without MPPT (Fig.15e). We can also have a good pump operation for very low irradiation values (50 W/m2 in NF method, 100 W/m2 in FLC, 330 W/m2 in P&O, and 400 W/m2 in the case without MPPT). This shows the efficiency of these methods (Fig. 15f).
Calculation of errors and efficiency for different MPPT We make a calculation error with respect to the flow (Fig. 15d) and pump efficiency for the different MPPT methods and performance without MPPT (Table 5). Q Q MPPT εQ ð%Þ ¼ desired *100 Q desired
(22)
Ph ¼ r$g$H$Q MPPT
(24)
where: Qdesired the desired flow, QMPPT flow obtained with MPPT. Table 5 summarizes the results of a comparative study between the different MPPT methods.
Discussion of results Under Excel, we present the values of relative errors and efficiency. According to Fig. 16 and Table 5, we note that the photovoltaic pumping system with MPPT improves throughput compared to the system without MPPT. We can also see that the controller (NF) has a faster response compared to other methods [(P & O) and (FLC)], and this method made a small error. So with the Neuro Fuzzy Controller (NF), there will be closer to the desired flow rate.
Conclusion In this paper, we have presented a photovoltaic pumping system with battery storage. Three optimization MPPT methods (P&O, FLC and NF). have been applied to the studied system. . The simulation of the proposed system has been developed using Matlab/Simulink Package. The comparison between these three algorithms enable us to conclude that the NF algorithm presents better static (response time) and dynamic (error) performances. Batteries have been added for storage. We have used measurement results of the battery and used the values in the mathematical model. In perspective of this work, we are working in the implementation of the proposed system.
references
Fig. 16 e Performances under the different MPPT methods.
[1] Rekioua D, Matagne E. Optimization of photovoltaic power systems: modelization, simulation and control. Green Energy Technol 2012;102. [2] Chan DSH, Philips JR, Phang JCH. A comparative study of extraction methods for solar cell model parameters. Solid State Electron 1986;29(3):329e37. [3] Rekioua T, Rekioua D. Direct torque control strategy of permanent magnet synchronous machines. 2003 IEEE Bologna PowerTech e Conf Proc 2003:861e6. [4] Zhang L, FeiBai Y. Genetic algorithm-trained radial basis function neural networks for modeling photovoltaic panels. Eng Appl Artif Intell 2005;18(7):833e44. [5] Lalouni S, Rekioua D, Rekioua T, Matagne E. Fuzzy logic control of stand-alone photovoltaic system with battery storage. J Power Source 2009;193(2):899e907. [6] Mohammedi A, Rekioua D, Mezzai N. Experimental study of a PV water pumping system. J Electr Syst 2013;9(2):212e22.
Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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[7] Lalouni S, Rekioua D. Modeling and simulation of a photovoltaic system using fuzzy logic controller. Proceedings eInternational Conference on Developments in systems Engineering DeSE 2009;vol. 2009:23e8. [8] Makhlouf M, Messai F, Benalla H. Vectorial command of induction motor pumping system supplied by a photovoltaic generator. J Electr Eng 2011;62(1):3e10. [9] Abdelli R, Rekioua D, Rekioua T, Tounzi A. Improved direct torque control of an induction generator used in a wind conversion system connected to the grid. ISA Trans 2013;(4):525e38. [10] Valenciaga F, Puleston PF. Supervisor control for a standalone hybrid generation system using wind and photovoltaic energy. IEEE Trans Energy Convers 2005;20(2):389e405. [11] Rekioua D, Bensmail S, Bettar N. Development of hybrid photovoltaic-fuel cell system for stand-alone application. Int J Hydrogen Energy 2014;39(3):1604e11.
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[12] Rekioua D, Rekioua T. DSP-controlled direct torque control of induction machines based on modulated hysteresis control. In: Proceedings of the International Conference on Microelectronics, ICM; 2009. p. 378e81. ndez LM. Application [13] Torreglosa JP, Jurado F, Garcı´a P, Ferna of cascade and fuzzy logic based control in a model of a fuelcell hybrid tramway. Eng Appl Artif Intell 2011;24(1):1e11. [14] Rekioua D, Achour AY, Rekioua T. Tracking power photovoltaic system with sliding mode control strategy. Energy Proced 2013;36:219e30. [15] Bosque G, Del Campo I, Echanobe J. Fuzzy systems, neural networks and neuro-fuzzy systems: a vision on their hardware implementation and platforms over two decades. Eng Appl Artif Intell 2014;32:283e331.
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Photovoltaic pumping system in Bejaia climate with battery storage K. Rahrah a,*, D. Rekioua a, T. Rekioua a, S. Bacha b Laboratoire de Technologie Industrielle et de l'information (LTII), Faculte de Technologie, Universite de Bejaia, Algeria b G2lab INPG Grenoble, France a
article info
abstract
Article history:
The use of solar energy especially for water pumping is well adapted for most rural and
Received 8 February 2015
desert areas. The latter have a great solar energy potential and a shallow underground
Received in revised form
hydraulic potential. Solar pumping is an exception; it is in fact relatively easy to store
10 April 2015
potential energy of water with a more reliable elevated tank. In this paper, we present
Accepted 11 April 2015
modeling and sizing of photovoltaic pumping system with battery storage. To track
Available online xxx
maximum output power operating point of the photovoltaic generator, we use three optimization methods: Perturbation and Observation (P&O), Fuzzy Logic Controller (FLC)
Keywords:
and Neuro-Fuzzy algorithm (NF). Batteries are added for the purpose of ensuring contin-
Photovoltaic system
uous power flow. The storage battery model used is presented with obtained measurement
PV pumping
results. The obtained simulations developed under Matlab/simulink package, show the
Sizing
robustness and effectiveness of the Neuro fuzzy controller.
MPPT
Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Battery storage
Introduction In photovoltaic (PV) water pumping systems, maximum power point tracking (MPPT) is usually used as online control strategy to track maximum output power operating point of the photovoltaic generator (PVG) for different operating conditions of insolation and temperature of the PVG. Although, the use of the MPPT control do not mean a systematic optimization of the motor or the whole system efficiency [1,2]. Thus, several control strategies of optimization allowing the improvement of the photovoltaic water pumping system operation were applied to the efficiency maximization of the PVG. In these existing MPPT control methods, the P&O is used in many PV systems more widely than others. The P&O method can work well when the solar irradiance and the
temperature do not vary quickly with time. However, it cannot track the maximum power point (MPP) quickly and output power is oscillating around the MPP. But, relatively complex computations and decision require a more complex microcontroller with more memory. This makes the implementation of this method relatively difficult and the cost of MPPT controller high. Other intelligent based control schemes have been introduced (fuzzy logic, neural network) [5,11,13]. The inputs of the fuzzy logic controllers are an error and an error variation, the output is a duty cycle or its variation. The fuzzy controller introduced in Ref. [5] uses Ppv, Vpv and its variations as inputs, as output, it determines the optimal increment which must be added to the operating voltage for tracking the MPP in order to assure fast and fine tracking. The hybrid modeling methodology, exploiting the characteristics of fuzzy logic and neural networks theories, to deliver the maximum
* Corresponding author. Tel./fax: þ213 34215090. E-mail addresses: [email protected] (K. Rahrah), [email protected] (S. Bacha). http://dx.doi.org/10.1016/j.ijhydene.2015.04.048 0360-3199/Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
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Nomenclature A ideality factor of the junction constants aij hydraulic constant CH electromotive force, V Ebatt hours of radiation per day, H Ens solar radiation, W/m2 Es e0 electron charge, C battery current, A Ibatt diode current, A Id output-terminal current, A Ipv diode-current, A Ip shunt-leakage current, A Ish short circuit current, A Isc saturation current of the diode, A Isat maximum current at PPM, A Impp g acceleration of gravity, m/s2 H total head, m K Boltzmann constant, Joule/K K1, K2 and K3 adaptive gains maximum power point, W Pmpp photovoltaic power, W PPV Q water flow, m3/s Qdesired desired flow flow obtained with MPPT QMPPT internal resistance, U Rbatt series resistance, U Rs shunt resistance, U Rsh performance motor pump group Rmp temperature cells, K TJ reference temperature of the PV cell, K TJref battery voltage, V Vbatt maximum voltage at PPM, V Vmpp open circuit voltage, V Voc Greek letters temperature coefficient of short-current, A/K asc voltage temperature coefficient, V/K boc r mass density of water, Kg/m3 Abbreviations FLC fuzzy logic controller MPP maximum power point MPPT maximum power point tracking PV photovoltaic PVG photovoltaic generator P&O perturbation and observation NF neuro-fuzzy algorithm available power from the PV system under different weather conditions, presents much high computation [14,15]. The paper is organized as follows: in Presentation and modeling of the studied system mathematical model of the photovoltaic water pumping system (PVG, electrical motors and centrifugal pump), and the structure configuration of the (P&O), Fuzzy Logic Controller (FLC) and Neuro-Fuzzy algorithm (NF) applied to the global efficiency optimization of the PV water pumping system is presented. Sizing of the PV water pumping system study in Bejaia climate is presented in Sizing
of photovoltaic pumping system study, while in Applications of different MPPT for solar pumping system, the obtained simulations results, using Matlab/simulink package are given and interpreted. Finally,Conclusion concluded the work.
Presentation and modeling of the studied system Presentation Generally, the photovoltaic pumping systems consist of a photovoltaic generator and a pumping subsystem. These systems operate over the sun without electrochemical storage. The water pumped can be used directly or stored in a tank for later use. This type of water storage is the solution most widely adopted compared to electrochemical energy storage in batteries. Fig. 1 describes the system studied in this article. It consists of a photovoltaic field, a DC/DC converter, a DC/AC converter, asynchronous motor coupled to a centrifugal pump, and a tank for storage of pumped water. To ensure operation at maximum power point of the PV generator, three methods are applied to the system are (P&O, FLC and NF).
Modeling of the PV generator In the literature, there are several mathematical models that describe the operation and behavior of the PV generator [2]. The models differ in the calculation procedure, the precision and the number of parameters involved in the calculation of the currentevoltage characteristic. In this work, we used an analytical model that gives very good information. The equivalent diagram of this model is given in Fig. 2 [3]: The current generated by the module is given by: Ipv ¼ Ip Id Ish
(1)
Current Ip is directly dependent on solar radiation Es and cell temperature Tj. It is given by the following relation [4]: Ip ¼ P1 $Es 1 þ P2 Es Eref þ P3 TJ TJref
(2)
The diode current is given by: e0 $ðV þ Rs $IÞ 1 Id ¼ Isat $ exp A$ns $k$TJ
(3)
With Isat is the saturation current, highly dependent on the temperature, given by: Eg Isat ¼ P4 $T3J $exp k$TJ
(4)
The current of the shunt resistance is calculated by: Ish ¼
ðV þ Rs $IÞ Rsh
(5)
Ipv ¼ P1 $Es $ 1 þ P2 $ Es Eref þ P3 $ TJ Tref Eg ðV þ Rs $IÞ P4 $T3j $exp $ exp e0 $ 1 A$ns $k$TJ k$TJ Vpv þ Rs $I Rsh (6) with:
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Fig. 1 e Description of the photovoltaic pumping system. Ip: current generated by the illumination (A), Id: the diode current (A), Ish: current of the shunt resistance (A), Isat: saturation current of the diode (A), Rs: series resistance (U), Rsh: shunt resistance (U), k: Boltzmann's constant (k ¼ 1.381023 (SI)), e0:electron charge (1.6021019 C), Tj: junction temperature (K), A: ideality factor of the junction. The parameters (P1, P2, P3, P4, A, Rsh and Rs) (Table 1) were obtained by numerical solution (Newton Raphson). Table 2 shows panel's data at STC for a SM 110-24 panel which is used in simulations.where: Vmpp: maximum power point voltage; Voc: open circuit voltage; Impp: maximum power point current; Isc: short circuit current; asc: current temperature coefficient; boc: voltage temperature. The curves currentevoltage Ipv (Vpv) and powerevoltage Ppv(Vpv) of the photovoltaic panel, are carried out by varying the load's resistance for three levels of irradiance and temperature (Figs.3 and 4). The experimental characteristics obtained are compared to the simulation characteristics for the same operating conditions (Es ¼ 530 Wm2, T ¼ 27,9 C; Es ¼ 620 Wm2, T ¼ 33 C; Es ¼ 750 Wm2, T ¼ 36 C). The PV panel SM 110-24 is located at the University of Bejaia. From the current and power characteristics (Fig.4.), the nonlinear nature of the PV array is apparent. Therefore, an MPPT algorithm must be incorporated to force the system to always operate at the maximum power point (MPP).
Modeling subsystem pumping Pump model Many different varieties of pumps are used with PV-pumping system. In our case, we use the model expresses the water
flow output (Q) directly as a function of the electrical power input (P) to the motor-pump, for different total heads. A polynomial fit of the third order expresses the relationship between the flow rate and power input, as described by the following equation [6,7]: PðQ; hÞ ¼ a0j ðhÞQ 3 þ a1j ðhÞQ 2 þ a2j ðhÞQ þ a3j ðhÞ
(7)
With: aij ðhÞ ¼
3 X
aij h
j
(8)
j¼0
where: aij are constants to be determined for each subsystem pumping.
Model of induction motor The mathematical model of asynchronous induction motor is given by the following equations [8,9]: - Stator voltage equations: 8 dFsd > > < Vsd ¼ Rs Isd þ dt > > : Vsq ¼ Rs Isq þ dFsq dt
(9)
- Rotor voltage equations: 8 dFrd dq > > Frq þ < 0 ¼ Vrd ¼ Rr Ird þ dt dt > > : 0 ¼ Vrq ¼ Rr Irq þ dFrq dq Frq dt dt
(10)
where: (Isd, Isq, Ird, Irq, Vsd, Vsq,Vrd, Vrq) and (Fsd, Fsq, Frd, Frq) are current, voltage and flux in the reference frame (d, q) of the stator and the rotor. Rs, Rr: are the stator the rotor resistances. - Mechanical equation:
Table 1 e Parameters of the PV panel SM 110-24 (Newton Raphson resolution). P1 Fig. 2 e Simplified equivalent circuit of solar cell.
0.00345
P2 0.58*10
P3 5
0.336*10
4
P4
A
Rsh
Rs
31.23
1
0.614
151.16
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Table 2 e Parameters simulations of the PV panel SM 110-24. Ppv 110 Ws
Impp
Vmpp
Isc
Voc
asc
boc
3.15 A
35 V
3.45 A
43.5 V
1.4 mA/ C
152 mV/ C
inverter. This will save one stage in the system and therefore will increase simplicity and efficiency. Many algorithms have been developed for tracking maximum power point of a solar cell [5,10]. In our work, we use the perturbation and observation (P&O), fuzzy logic (FLC) and Neuro Fuzzy (NF).
P&O method
Fig. 3 e Circuit measurement of Ipv (Vpv) and Ppv(Vpv) of the PV panel SM 110-24.
dur Tmot TLoad ¼ Jmot $ dt
(11)
ur: motor angular velocity, Jmot:moment of inertia.
This is the method most used saw its simplicity. A feedback loop and few measures are necessary. The voltage across the panel is deliberately disturbed (increased or decreased) with a constant (C) then the power is compared with that obtained before disruption. Specifically, if the power terminals of the panels are increased due to the perturbation, the following perturbation is made in the same direction. Conversely, if the power decreases, the new disturbance is performed in the opposite direction [11,12]. Easy to use and implement in a control system of a photovoltaic panel, the P&O but has drawbacks. Indeed, it happens that the P&O algorithm diverges from the maximum power point in case of very rapid fluctuation in the sunshine. Even if the operating conditions constant, variations of the voltage and current which generates oscillations around the point of maximum power is observed (Fig.5).
- Electromagnetic torque:
Method based on fuzzy logic (FLC)
Tmot
¼ P$ fsd $isq fsq $isd
(12)
P: is the pole pair number of the AC machine
MPPT controller Usually the MPPT controls a DC/DC converter that is generally placed between the PV array and the inverter, to maintain a constant DC voltage at the output of the generator. With an appropriate sizing of the PV array, the DC/DC converter can be avoided, due to the relatively small changes in the optimum voltage in operating condition and moving the MPPT to the
In recent years, fuzzy logic controllers (FLC) are widely used for the MPP search [5]. These are independent of the process model; they are characterized by their ability to understand the problems of nonlinearity and exhibit robust performance with respect to changes of weather and load. We present a method of MPPT using the theory of fuzzy logic to address the oscillation of the perturbation and observation (P&O) problem. The proposed fuzzy controller optimizes the amplitude of the oscillations to minimize disturbance and have a quick response and without oscillations. The fuzzy logic controller measures the PV array characteristics and then perturbs the operating voltage by an optimal increment (DVpv,ref) and the resulting PV power change. The power variation (DPpv) is either in the positive direction or in the negative one. From
Fig. 4 e Experimental and simulation curves Ipv(Vpv) and Ppv(Vpv). Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Fig. 7 e Structure of MPPT Neuro Fuzzy controller.
Fig. 5 e Flowchart of Perturb and Observ method.
Table 3 e Fuzzy rule table. DPPV
VPV
BN N Z P BP
BN
N
Z
P
BP
BN BN N N Z
BN N N Z P
N N Z P P
N Z P P BP
Z P P BP BP
these inferences, the reference photovoltaic voltage variation (DVpv,ref) is increased or decreased in the direction which makes it possible to increase the power Ppv. The control rules are indicated in Table 3 with (DPpv) and (DVpv) as inputs, while (DVpv,ref) represents the output. These inputs and output variables are expressed in terms of linguistic variables (such as BN (big negative), N (negative), Z (zero), P(positive), and BP (big positive)). From these linguistic rules, the FLC proposes a variation of the reference voltage DVpv,ref according to Eqs. (12)e(14).
DPpv ¼ Ppv ½k Ppv ½k 1
(12a)
DVpv ¼ Vpv ½k Vpv ½k 1
(13)
Vpv;ref ½K ¼ Vpv ½k 1 þ DVpv;ref ½k
(14)
where: Ppv[k] and Vpv[k] are the power and voltage of the photovoltaic generator at sampled times (k), and Vpv,ref[k] the instant of reference voltage used to have the duty ratio. The fuzzy logic controller structure is show in Fig. 6. Where K1, K2 and K3 are adaptive gains. The bloc fuzzy logic controller includes three functional blocks: fuzzification, fuzzy rule algorithm, and defuzzification. The membership functions of inputs variables DPpv and DVpv are triangular and have five fuzzy subsets. Five fuzzy subsets are also considered for the output variable DVpv,ref. The fuzzy inference is carried out by using Mamdani's method, and the defuzzification uses the center of gravity to compute the output of this FLC.
Method Neuro Fuzzy (NF) [13e15] Neuro-fuzzy systems are fuzzy systems formed by a learning inspired by the theory of neural networks algorithm. The learning technique operates according to the local information and produces only local changes in the fuzzy system origin. The neuro-fuzzy system used here is the adaptive networkbased fuzzy inference system (ANFIS). The system is an adaptive network functionally equivalent to a first-order Sugeno fuzzy inference system. The neuro-fuzzy MPPT controller developed in this part includes two inputs DPpv and DVpv and a
Fig. 6 e Structure of MPPT fuzzy controller. Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Fig. 9 e Equivalent circuit model CIEMAT.
Layer 2: The nodes in this layer are fixed. Each node output represents a firing strength of a rule. Layer 2 implemented the fuzzy product operator. The output of the Layer 4 is comprised of a linear combination of the inputs multiplied by the normalized firing strength w. O1i ¼ mAi ðxÞ$mBi ðxÞ ¼ wi i ¼ 1; 2
(16)
Layer 3: In this layer, where the normalization process performed acts to scale the firing strengths, the nodes are fixed. The rule's firing strength to the sum of all rule's firing strength is calculated for the corresponding node. O3i ¼ wi ¼
wi þ w2 w1
(17)
Layer 4: The adaptive nodes in this layer operate as a function block whose variables are the input values and pi, qi, and ri is the consequent parameter set of the node. O4i ¼ wi $f i ¼ wi $ pi $x þ qi $y þ r Fig. 8 e a) Waveform of photovoltaic voltage under the standard test conditions (1000 W/m2, T ¼ 25 C). (b) Photovoltaic voltage at a transitional state (Zoom 1). (c) Photovoltaic voltage at a steady state (Zoom 2).
single output DVpv,ref which represent the variation of the photovoltaic power, photovoltaic voltage variation, and control. The two inputs variables generate action DVpv, ref to be applied to the chopper control, in order to adjust the input voltage thereof so as to ensure the adaptation of the power supplied by the photovoltaic generator. The equivalent ANFIS architecture of a first-order Sugeno fuzzy model with two rules shown in Fig. 7. The model has five layers and every node in a given layer has a similar function. The fuzzy IF-THEN rule set, in which the outputs are linear combinations of their inputs, is:
(18)
Layer 5: Simple summation of Layer 4 outputs. The adjustment of modifiable parameters is a two-step process. First, information is propagated forward in the network until Layer 4, where a least-squares estimator identifies the parameters. Then, the parameters in Layer 2 are modified using gradient descent. The only user specified information is the number of membership functions for each input and output as training. O5i ¼
X Wi $f i
(19)
i
Applications of different MPPT for PV generator As a first step, we simulate the panel part with and without MPPT for operation under constant conditions (1000 W/m2,
Rule 1 if x is A1 and y is B1 then f1 ¼ p1x þ q1x þ r1 Rule 2 if x is A2 and y is B2 then f2 ¼ p2x þ q2x þ r2 Layer 1: Consists of membership functions (A1, A2, B1, B2), where the fuzzification process takes place, and every node is adaptive. (15) O1i ¼ mAi ðxÞ where x is the input of node i, and Ai or (Bi) is a linguistic label (such as “small” or “large”) associated with this node.
Fig. 10 e Battery capacity variation for different temperatures.
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Fig. 13 e Variation of insolation for summer day in Bejaia. Fig. 11 e Variation of the state charge for different temperatures. method requires much time to track the MPP and presents oscillations around the MPP at a steady state. T ¼ 25 C). Fig. 8 a shows the shape of the voltage PV generator with and without MPPT. A zoom at a transitional and steady state is shown in Fig. 8 b and Fig. 8 c. We note on Fig. 8 b and Fig. 8 c that NF control has a faster response compared to other types of MPPT control and without MPPT. And the P&O
Battery storage We have opted for the CIEMAT model. It is characterized by setting a series of women with a variable resistor (Fig. 9.). The
Fig. 12 e a) Geographical situation of Bejaia. b) Solar radiation (Es) during a year in Bejaia. Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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Table 4 e Steps Sizing of PV pumping system. Electric power required by the pump The peak power of the PV generator Number of panels series Number of panels parallel
Eele ¼ (CH*Q*HMT)/Rmp
Eele ¼ (2.725*25*10)/0.44
1548.29 Wh/day
Ppvtot ¼ Eele/[Ens*(1pertes)] Ns ¼ (Ppvtot/Ppv) Np ¼ Ipv/Ipvt
Ppvtot ¼ 1548.29/[2.2*(10.2)] Ns ¼ 880/110 Np ¼ 3.15/3.14
880 W 8 1
characteristics of the source voltage Ebatt and internal resistance Rbatt depend on temperature and battery charge state. For a number nbatt of cell, voltage equation is: Vbatt ¼ nbatt $Ebatt ±nbatt $Rbatt $Ibatt (20) with: Vbatt: battery voltage, Ibatt: battery current, Ebatt: electromotive force depending on the battery charge state, Rbatt: internal resistance which varies with the state of charge. The battery behaves as complex impedance Zbatt containing a resistance Rbatt and a reactance Xbatt to this disturbance. The battery behaves as complex impedance Zbatt with a resistance Rbatt and a reactance Xbatt to this disturbance. jZbatt j ¼
jVbatt j jIbatt j
Algeria (Fig. 12 a and b). PV generator is sized relative to the worst month. Fig. 13 shows that December is the month where solar radiation Es is the worst in Bejaia. The steps of sizing of the PV pumping system are summarized in the (Table 4).with:
(21)
The module of the complex impedance is thus well defined by the ratio of the absolute values of the two signals. We deduce the dephasing by the temporal difference between the two signals with the passage by zero. Knowing the module of Zbatt and dephasing, we can thus deduce the real part Rbatt and imaginary Xbatt of the impedance for his state of charge, these values changes according to the latter. We obtain: Rbatt ¼ 0.577U, Xbatt ¼ 0.15U and Cbatt ¼ 21.22 mF. We give some simulation results (Figs. 10e11). We note that when the temperature increases, the behavior of the battery is saved by increased as well. The value of the battery internal resistance decreases rapidly with increasing temperature, which is mainly due to the change of electrolyte resistance.
Sizing of photovoltaic pumping system study Estimated water needs The studied pumping system is sized to supply drinking water to families in a village in Bejaia whose water needs are estimated at 25 m3/h, the total head H ¼ 10 m, the thank is 100 m3.
Sizing the pump unit Due to the characteristics of the induction motor of the pump unit (Pn ¼ 746 W, In ¼ 3.4 A), the panels must be connected in series.
Sizing the PV generator Due to its geographical location, Algeria has one of the highest solar fields in the world. Regarding the region of Bejaia (36 430 N 5 040 E 2 m), which is a coastal city in the North East of
Fig. 14 e a) Profile of solar radiation. (b) Photovoltaic power. (c) Photovoltaic power at transitional state (Zoom 1). (d) Photovoltaic power at steady state (Zoom 2).
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Fig. 15 e a) Irradiation profile in a summer day at Bejaia. (b) Photovoltaic power. c Induction machine rotational speed. (d) Water flow. (e) Water flow (zoom). (f) Water flow in function of the irradiation Es.
CH: Hydraulic constant, CH ¼ r*g/3600, g: Acceleration of gravity (9.81 m/s2), H: Total head (m), Q: Water flow (m3/s), r: Mass density of water (1000 kg/m3), Rmp: Performance Motor pump group, Ens: hours of radiation per day (H). The total output voltage of the PV generator is: VPVtot ¼ NPV $VMPP ¼ 8*35 ¼ 280V The total output current of the PV generator is: IPVtot ¼
PPVtot 880 ¼ 3:14A ¼ VPVtot 280
Applications of different MPPT for solar pumping system
between the three methods P&O, FLC and NF will be presented for two operations.
Simulation under varying conditions To study the behavior of the system to changes in illumination we will make a quick decrease in the sunshine at a constant temperature (T ¼ 25 C). In starts with solar radiance Es ¼ 1000 W/m2 and at time t ¼ 5 s is decreased to Es ¼ 700 W/ m2, the results are presented in Fig. 14 a.
Table 5 e Comparison between the different MPPT. Performances
Different simulations were performed on the studied system. It is composed of eight (08) photovoltaic panel of 110 W connected in series. The various parts of the system (photovoltaic panel, DC/DC converter, DC/AC converter, induction motor and centrifugal pump) are modeled by separate block then related in a coherent way. In this section, in simulates the complete system with MPPT to make a comparative study
Response time(H) Speed(rad/s) Efficiency (%) DQ (m3/s) Error (%) DT (H)
Methods Without MPPT
P&O
FLC
NF
7 35 21 1.5$103 76.8 7
7 35 21 1.5$103 76.8 7
6.010 90 56.54 4.2$103 37 0.99
4.625 125 79 6$103 13 2.375
Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048
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i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 5 ) 1 e1 1
For a quickly variation of solar radiation (1000 W/m2 to 700 W/m2), we note that the Neuro Fuzzy algorithm responds better than the two others.
hð%Þ ¼
Ph *100 Pmec
(23)
With:
Q desired ¼ 25m3 h ¼ 0:006m3 s
Simulation for a daily profile In this part, we have given a simulation for a daily profile to see the response of MPPT algorithms throughout the day, the results are given below (Fig. 15aef): It is clear in (Fig. 15b) that the system operation is improved by using the NF algorithm; we can extract a higher power compared to other types of MPPT control, and the operation without MPPT. One sees clearly that the speed and throughput are improved by the NF algorithm. It also allowed us to gain almost 1 h of time compared to other types of MPPT control, and 2 h compared to the system without MPPT (Fig.15e). We can also have a good pump operation for very low irradiation values (50 W/m2 in NF method, 100 W/m2 in FLC, 330 W/m2 in P&O, and 400 W/m2 in the case without MPPT). This shows the efficiency of these methods (Fig. 15f).
Calculation of errors and efficiency for different MPPT We make a calculation error with respect to the flow (Fig. 15d) and pump efficiency for the different MPPT methods and performance without MPPT (Table 5). Q Q MPPT εQ ð%Þ ¼ desired *100 Q desired
(22)
Ph ¼ r$g$H$Q MPPT
(24)
where: Qdesired the desired flow, QMPPT flow obtained with MPPT. Table 5 summarizes the results of a comparative study between the different MPPT methods.
Discussion of results Under Excel, we present the values of relative errors and efficiency. According to Fig. 16 and Table 5, we note that the photovoltaic pumping system with MPPT improves throughput compared to the system without MPPT. We can also see that the controller (NF) has a faster response compared to other methods [(P & O) and (FLC)], and this method made a small error. So with the Neuro Fuzzy Controller (NF), there will be closer to the desired flow rate.
Conclusion In this paper, we have presented a photovoltaic pumping system with battery storage. Three optimization MPPT methods (P&O, FLC and NF). have been applied to the studied system. . The simulation of the proposed system has been developed using Matlab/Simulink Package. The comparison between these three algorithms enable us to conclude that the NF algorithm presents better static (response time) and dynamic (error) performances. Batteries have been added for storage. We have used measurement results of the battery and used the values in the mathematical model. In perspective of this work, we are working in the implementation of the proposed system.
references
Fig. 16 e Performances under the different MPPT methods.
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Please cite this article in press as: Rahrah K, et al., Photovoltaic pumping system in Bejaia climate with battery storage, International Journal of Hydrogen Energy (2015), http://dx.doi.org/10.1016/j.ijhydene.2015.04.048