T panel to track optimum thermal and electrical power

Energy Conversion and Management 65 (2013) 372–380

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Artificial Neural Network based control for PV/T panel to track optimum thermal and electrical power Majed Ben Ammar, Maher Chaabene ⇑, Zied Chtourou University of Sfax, Research Unit on Control of Electrical Machines and Power Networks, National School of Engineers of Sfax, BP W, 3038 Sfax, Tunisia

a r t i c l e

i n f o

Article history: Received 13 June 2012 Received in revised form 13 July 2012 Accepted 3 August 2012 Available online 17 October 2012 Keywords: PV/T Modeling State equation Simulation Optimization ANN

a b s t r a c t As solar energy is intermittent, many algorithms and electronics have been developed to track the maximum power generation from photovoltaic and thermal panels. Following technological advances, these panels are gathered into one unit: PV/T system. PV/T delivers simultaneously two kinds of power: electrical power and thermal power. Nevertheless, no control systems have been developed in order to track maximum power generation from PV/T system. This paper suggests a PV/T control algorithm based on Artificial Neural Network (ANN) to detect the optimal power operating point (OPOP) by considering PV/T model behavior. The OPOP computes the optimum mass flow rate of PV/T for a considered irradiation and ambient temperature. Simulation results demonstrate great concordance between OPOP model based calculation and ANN outputs. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The effectiveness of solar energy converters is intermittent following the fast variation of solar radiation, ambient temperature and wind speed. Hence, Maximum Power Point Tracking (MPPT) from photovoltaic (PV) array is essential since it maximizes the PV generated power for a given meteorological condition [1]. Consequently, various efficient MPPT techniques have been developed in literature [2,3]. The most used tracking techniques are: the perturb and observe, the constant voltage, the short-current pulse, the Artificial Neural Network, the fuzzy logic, etc. [4–6]. For solar water and air heaters, the instantaneous efficiency test for measuring thermal performance is characterized by flow rate factor FR(sa) and FR(UL)under steady state condition [7]. Similarly, for thermal testing of parabolic concentrator solar cooker, the design parameters FUL and Fg0 are proposed under load conditions [8]. Recently, thermal tests on solar dryers with no load and load have been reported in literature identifying two parameters namely the overall heat loss coefficient, UL and the drying efficiency, gd respectively. The determination of UL is based on the maximum temperature of various components such as absorber plate, glass cover, and fluid, attained inside the dryer under stagnation condition [8,9]. In 1978, the first photovoltaic and thermal (PV/T) was conceived [10]. Consequently, physical models of flat-plate solar ⇑ Corresponding author. E-mail addresses: [email protected] (M.B. Ammar), maher.chaabene@ cmerp.net (M. Chaabene). 0196-8904/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enconman.2012.08.003

heat collector with integrated solar cells, and algorithms for making quantitative predictions regarding the performance of the system were presented [11]. Many investigations have been presented in order to improve PV/T efficiency. These studies have maximized either the photovoltaic [12] or the thermal efficiency [13,14]. It has been demonstrated that the mass flow rate has the major effect on the thermal and the electrical efficiency [15,16]. A previous published work has introduced explicit dynamic model suitable for PV/T system characterization [17]. This paper presents the effect of mass flow rate on produced thermal energy and electrical power. Then by using established model, a calculation of the optimum power operating point (OPOP) is determined. The algorithm applies the ANN method on the PV/T model in order to compute the optimum flow rate that allows optimum operating point for a given solar radiation and ambient temperature. 2. PV/T panel modeling Fig. 1 shows the architecture of the PV/T based water pumping and heating system. It includes a water storage tank connected to PV/T collector. A water pump supplied by the PV/T generated electric power ensures the water pumping from a well. The state representation model of a PV/T solar collector is given by Fig. 2. The input vector is T ¼ ½ T g T c T p T f 0 , where Tg, Tc, Tp, Tf are respectively the temperatures of glazing, solar cell, absorber plate and water circulation. The perturbations of PV/T panel are w the wind speed, gh the solar irradiation and Ta the ambient temperature. _ the mass flow rate of fluid. The output vector The control vector is m

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Nomenclature A Ae c d di g gh h hi hr k k1 Isc,sTc

asTc m mg _ m Nu P Q T t y w

area, m2 edge area, m2 specific heat capacity, J/(kg K) internal diameter of the tube, m inside tube diameter, m gravitational constant, 9.81 m/s2 solar radiation, W/m2 conduction heat transfer coefficient, W/m2 K heat transfer coefficient, W/m2 K radiation heat transfer coefficient, W/m2 insulation thermal conductivity, W/m K plate thermal conductivity, W/m K current in standard conditions, A temperature coefficient of short-circuit, mA/°C. mass, kg mass of the front glazing, kg mass flow rate, kg/s Nusselt number, – electrical power, W thermal power, W temperature, K time, s traveling distance, m wind sped, m/s

Greeks

a

absorptivity, – vertical orientation angle, ° PVG temperature coefficient, °C1 emissivity, – efficiency, – kinematic viscosity, m2/s thermal diffusivity, m2/s constant of Stefan, W/m2 K4 transmissivity, –

b bg

e g t k

r s

Superscripts + critical []0 transpose Subscripts a ambient air c solar cell f fluid g glass cover p absorber plate

Hot water supply

Use

PV/T Collector Heat collecting plate

PV module

Converter

Control of mass flow rate

Pump Cold water supply

Electrical supply Well Fig. 1. Water heating and pumping system.

is ½ P Q 0 , where P is the electric power output and Q the thermal profit. The energy balances for each component of the PV/T collector are:  Glass cover sub-model:

mg cg

dT g ¼ ag g h Ag þ Ag ðhwind þ hrga ÞðT a  T g Þ þ Ag hrcg ðT c dt  T g Þ þ Ag hcg ðT c  T g Þ

ð1Þ

 Solar cell sub-model:

mc cc Fig. 2. Synoptic schema of PV/T panel.

 dT c  ¼ ac sg Ac ð1  gr Þ g h  Ag hrcg ðT g  T c Þ  Ag hcg ðT c dt  T g Þ  Ac hcp ðT c  T p Þ

ð2Þ

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 Absorber plat sub-model:

In the same way, the photovoltaic module voltage with NS serial is:

mp cp

dT p ¼ Ac hcp ðT c  T p Þ  Ac hpa ðT p  T a Þ  Af hfa ðT f  T a Þ dt _ f DT f  mc

V pv ¼ Ns  V T ln ð3Þ

 ISC;STC  Ipv I0

ð15Þ

The electrical power is given by:

 Output fluid temperature sub-model:

P ¼ V pv  I pv dT f DT f _ mf cf ¼ Af hpf ðT p  T f Þ  cf m dt Dy

ð4Þ

where hcg is the convective heat transfer coefficient between glass cover and solar cell. The convective heat transfer coefficient under is given by [18]:

hwind ¼ 2:8 þ 3w

ð5Þ

The radiation heat transfer coefficient between the front cover and the environment is:

  hrga ¼ eg r T 2g þ T 2a ðT g þ T a Þ

ð6Þ

 Thermal output power: The thermal output power of PV/T system is given by:

_ f ðT f  T fi Þ Q ¼ mc



hrcg ¼



1

eg

ð7Þ

þ e1c  1

where hcp is the conduction heat transfer between cell and absorber plate:

hcp ¼ Nu

k1 L

ð8Þ

(

"

1708ðsin 1:8bÞ1:6 Nu ¼ 1 þ 1:44 1  Ra cos b " # 1=3 Ra cos b 1 þ 5830

#

 1708 1 Ra cos b ð9Þ

Ra is the Reynolds number. It is expressed as:

Ra ¼

g DTL3i tkT a

ð10Þ

where DT is the difference in temperature of the cell/collector surface and temperature of the plate (bottom) is:

DT ¼ ðT p  T c Þ

ð11Þ

While computing, the variation of the fluid temperature by the variation of the tube length is considered constant [18].  Electrical output power: The electric output power depends on the instantaneous operating temperature Tc of the PV cell module, and can be expressed as a function of the electrical current of the PV module:

    V pv þ Rs  I pv V pv þ Rs  Ipv 1  Ipv ¼ Np Iph  I0 exp VT Rsh

ð12Þ

The thermodynamic potential is given by:

VT ¼

KB  Tc  n q

ð13Þ

where KB = 1.3806  1023 [J/K], the Boltzmann constant q = 1.6  1019 [C] and the factor of non ideality n = 1, 62. The cell temperature is given by Eq. (2). The photoelectric current is given by:



g Iph ¼ ISC;STC þ aSTC ðT c  T a;ref Þ  h 1000 I0 is the opposite current of saturation of the diode.

T_ ¼ AT þ BU þ DW Y ¼ CT þ EU

ð14Þ

ð18Þ

T ¼ ½ T g T c T p T f 0 is the vector containing the temperatures at the 4 nodes of the PV/T system, A is the state matrix which contains the heat exchange coefficients between the system elements, B is the control matrix which encloses commands applied on the mass _ D is the perturbation matrix acting on the perturbaflow rate (m), tion inputs vector (W ¼ ½ g h T a 0 ), Y is the output vector containing the electrical powers and the thermal profit gain (Y ¼ ½ P Q 0 ). The detailed form of the state equation is expressed by: 80 1 0 > T_ g a11 > > > B C B > > C B B _ > > B T c C B a21 > > B C¼B > > _ C B > >B @ T p A @ a31 > > > > < T_ a41 f

Nu is the Nussle number. It is given by:

ð17Þ

where cf is the specific heat at the average fluid temperature, Tfi is the inlet temperature of the fluid, and Tf is the outlet temperature of the fluid at instant t given by Eq. (4). By gathering the system components sub-models given by Eqs. (1)–(17), the state equation of the energy balance is:

and that between the front cover and the collector plate is:

r T 2g þ T 2a ðT g þ T a Þ

ð16Þ

> > > > > > > > > > > > > > > > > > :

P Q

! ¼

a12

a13

a22

a23

a32

a33

a42

a43

1 0 1 0 Tg b11 d11 CB C B C B B T c C B b21 C B d21 a24 C CB C B C_ B CB C þ B Cm þ B C B C B B d31 a34 C A@ T p A @ b31 A @ a14

10

Tf 1

a44

0

c11

c12

c13

c21

c22

c23

Tg !B C B Tc C B C B Cþ C c24 B @ Tp A c14

b41

e11 e21

d41

d12

1

C ! d22 C C gh C d32 C A Ta d42

! _ m

Tf

ð19Þ

The parameters aij, bij, cij, dij and eij represent PV/T model coefficients. They are determined from Eqs. (1)–(17). Table 1 Design parameters of photovoltaic thermal (PV/T) collector. Front glazing (low-iron glass) Area of glass: 1.64 m2 Mass of glass: 7.2 kg Specific heat capacity of glass: 810 J/kg K Emissivity of glass: 0.88 Transmissivity of glass:0.95

Solar cell (polycrystalline silicon) Area of module: 0.87 m2 Mass of module 5.4 kg Specific heat capacity of cell: 903 J/kg K Emissivity of cell 0.35 PVG reference efficiency: 0.178 Kinematic viscosity: 1.88  105 m2/s PVG reference temperature: 25 °C Vertical orientation angle: 45° thermal diffusivity 2.69  105 m2/s Connection resistance of panel: 0.8 X Current in standard conditions: 3.35 A Temperature coefficient of short-circuit: 1.4 mA/°C

Number of modules in parallel: 33 Number cell: 1 Thermal absorber (aluminum alloy) Area of plate absorber: 2 m2 Mass of plate absorber: 9.03 kg Specific heat capacity of absorber: 900 J/kg K Thermal conductivity: 385 W/m K Water in channel Area of channel fluid flow: 0.165 m2 Mass of water: 45 kg Tube spacing: 0.15 m Specific heat capacity of fluid: 4190 J/kg K Heat transfer coefficient hpf: 100 W/m2 hcp: 5.7 W/m2 hrcp: 1000 W/m2 hi: 300 W/m2 K

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2100

155

gh = 1000 W/m2 Ta = 25 °C

153

P= 148.76 Watt Q= 1690.72 Watt

1800

151

1700

149

1600

147

1500

electrical power (Watt)

thermal power (Watt)

1900

145 Optimum mass flow rate

1400 0.013

0.014

0.015

0.016

0.017

0.018

0.019

0.020

0.021

143 0.022

mass flow rate (Kg/s) Fig. 3. Variation of thermal power and electrical power by varying the mass flow rate.

0.251

Ta = 1 5°C

gh = 700 W/m2

0.250

gh = 500 W/m2

0.249

gh = 400 W/m2

P.Q (kW 2)

0.248 0.247 0.246 0.245 OPOP (400 W/m 2, 15°C)

0.244

OPOP (500 W/m2, 15°C)

0.243 0

2

OPOP (700 W/m , 15°C)

0

0.006

0.012

0.018

0.024

0.030

mass flow rates (kg/s) Fig. 4. PV/T power by varying mass flow rate at constant ambient temperature Ta = 25 °C and different values of solar radiation.

0.254 g h = 1 0 0 0 W/m

2

Ta = 35°C

0.253

Ta = 30°C 0.252

Ta = 25°C

2

P.Q (kW )

0.251 0.250 0.249 0.248 OPOP (25°C, 1000 W/m 2)

0.247

OPOP (30°C, 1000 W/m2)

0.246 0

2

OPOP (35°C, 1000 W/m )

0

0.006

0.012

0.018

0.024

0.030

mass flow rates (kg/s) Fig. 5. PV/T power by varying mass flow rate at constant solar radiation gh = 1000 W/m2 and different values of ambient temperature.

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Fig. 6. Architecture of the neural network for obtaining the OPOP.

3. PV/T control: OPOP tracking

3.2. OPOP tracking

The performance of PV/T collector is depicted by the output powers: thermal (Q) and electrical (P). These outputs are influenced by the operating conditions which are the solar radiation, the ambient temperature and the mass flow rate. In fact, by increasing the mass flow rate, the cell temperature Tc decreases (Eqs. (2) and (3)), wich increases the electrical generated power (Eqs. (12)–(16)) and decreases the output thermal power (Eq. (17)). We therefore introduce the optimal power operation point OPOP as the optimal flow rate that allows for both optimal thermal and electrical power generation. _ opt ) The OPOP consists of the calculation of the control law (m _ opt must be which ensures the PV/T optimum outputs P and Q. m calculated on the basis of the PV/T model by considering the climatic parameters (gh, Ta) as stochastic perturbations. Nevertheless the models that represent the PV/T system (Eq. (19)) is nonlinear and composed of two sub systems: One provides fast output (P) and a second delivers an output with time delay. This makes hard the use of conventional control methods for OPOP tracking.

The OPOP detection is based on the variation of the mass flow rate then the determination of the common point of the two curves P and Q. This procedure cannot be applied while the system is under operation because the mass flow rate variation may cause dysfunction, and makes time delay following mass flow rate calibration. In order to track the optimum mass flow rate for different climatic parameters, several methods based on Artificial intelligence can be used. These methods are a combination of fuzzy logic, genetic algorithm and Artificial Neural Networks. The use of fuzzy logic needs the development of an expert who is able to make decision for any circumstance, the use of genetic algorithm is based on a behavior model which is not investigated, the Artificial Neural Network (ANN) needs a data base for the training phase. Hence, ANN is suitable in our case as the data base may be built. In fact, different solar radiation and ambient temperature, the OPOPs are determined by varying the mass flow rate and detecting the crossing point of the max (P  Q)curves (Figs. 4 and 5.). The optimum mass flow rates determined for fixed steps of solar radiations (100 W/m2) starting from 300 W/m2 and ambient temperatures (10 °C) starting 5 °C are gathered in order to from learning data

3.1. OPOP determination The OPOP may be determined by a graphical approach. This approach consists to determine the common point given by the curves of P and Q function of the mass flow rate variation. State representation equations (Eq. (19)) is converted to numerical format and then simulated for different mass flow rate, ambient temperature and solar radiation. In order to concretize the simulation, the PV/T specifications given by Table 1 are used to plot the variation of thermal and electrical powers with respect to mass flow rate changes at given ambient temperature and solar radiation (Fig. 3). This figure shows that when the mass flow rate increases, the cell temperature decreases and the electrical power increases while when the mass flow rate increases, the water temperature decreases and the thermal power decreases. The OPOP at a given ambient temperature and solar radiation is determined by computing the common operating point given by two generated power curves, this corresponds to the maximum of P  Q curve. The OPOP varies function of solar radiation and ambient temperature. Fig. 4 shows the variation of P  Q function of mass flow rate at constant ambient temperature and different values of solar radiation. As for, Fig. 5 shows the variation of P  Q function of mass flow rate at constant solar radiation and different values of ambient temperature.

Fig. 7. Training, validation and testing over learning iterations (12 epochs) for PV/T system.

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Mass flow rate (Kg/s) Mass flow rate (Kg/s)

0.016

Mass flow rate (Kg/s)

Neural Network

0.015

0.018

PxQ determination

Ta = 15°C

0.013 0.011 0.009 0.007 350

450

550

650

750

850

950

550

650

750

850

950

650

750

850

950

Ta = 25°C

0.014 0.012 0.010 350

450

Ta = 35°C

0.016 0.014 0.012 0.010 350

450

550

Solar radiation (W/m2)

Mass flow rate (Kg/s)

Mass flow rate (Kg/s)

Mass flow rate (Kg/s)

Fig. 8. ANN estimated and determined OPOPs for different solar radiations at constant ambient temperature.

Neural Network

PxQ determination

0.016 gh = 650 W/m2

0.014 0.012 0.010

5

10

15

20

25

30

35

15

20

25

30

35

15

20

25

30

35

0.017 gh = 750 W/m2

0.015 0.013 0.011

5

10

0.017 gh = 850 W/m2

0.016 0.014 0.012

5

10

Ambient temperature (°C) Fig. 9. ANN estimated and determined OPOPs for different ambient temperature at constant solar radiation.

base for Artificial Neural Network (Fig. 6). Following the training from the established data base, the ANN controller is used to estimate the optimum mass flow rate for a given solar radiation gh and ambient temperature Ta. The architecture of the adopted Neural Network is composed of three layers. The input layer contains two neurons as it disposes of two inputs (solar radiation and ambient temperature). The hidden layer includes five neurons, this number is selected following the execution of empirical rules which start with a high number of neurons and eliminate the unnecessary ones on condition to reach network stability and

output accuracy. The output layer contains one neuron that corresponds to the optimum mass flow rate. The activation function of neurons is defined by the hyperbolic tangent function [19]:

Y ¼ f ðUÞ ¼ W 0  tan hðW 1  U þ b1 Þ þ b0

ð20Þ

The model is based on multi-layer perceptron (MLP) feed-forward architecture, where Y is the outputs neural network given by the _ opt , U is a column vector including the optimum mass flow rate m solar radiation gh and the ambient temperature Ta.

Optimum mass flow rate (Kg/s)

1000

28 26

800

gh Ta

24

600

22

400

20

200

18

6

8

10

12

14

16

0 18

6

8

10

12

14

16

18

Solar radiation (w/m2)

M.B. Ammar et al. / Energy Conversion and Management 65 (2013) 372–380

Ambient temperature (°C)

378

0.020

0.015

0.010

0.005

Optimum mass flow rate (Kg/s)

28

1000

26

800 600

24 22

400

Ta gh

20 18 12

12.5

13

200 0 14

13.5

Solar radiation (w/m 2 )

Ambient temperature (°C)

Real solar time (hours)

0.020 estimated

0.018

detrminated

0.016 0.014 0.012

12

12.5

13

13.5

14

Real solar time (hours)

Fig. 10. Estimated and determined curves of optimum mass flow rate (January 22th 2011 as a representative day of the cold season).

The weights and biases of the ANN are determined by a training algorithm with the historical input–output data using back propagation. The available data is divided into two parts: one part is used for training the net whereas the other usually smaller part is used to test the performance of the ANN.

4. Results and discussion ANN model consists of a training algorithm based on data base established in advance. This data base contains different values of ambient temperature Ta, solar radiation gh, and computed mass flow rate using the OPOP determination given by Figs. 4 and 5. The obtained ANN model is consequently used during function _ opt for a given climatic to determine the optimum mass flow rate m parameters (Eq. (20)). One way of avoiding over fitting is by using a part of the initial training set as validation set on which the network is tested occasionally without learning. As long as the model learns the general pattern in the data, results with the validation data set will improve with training. However, if the validation results deteriorate, the network has learned particulars of the data

points (over fitting) and learning is stopped. In the presented study input data set is split into 60% for training, 20% for validation and 20 for testing. Fig. 7 illustrates such a learning process for PV/T optimum mass flow rate. The obtained performance factor is less than107. Following the ANN training the corresponding numerical values of the net weights and biases as given in Eq. (20) are:

2

2:4443 6 6 1:1877 6 W1 ¼ 6 6 2:4494 6 4 2:7374

0:7564

3

7 1:5055 7 7 1:0513 7 7 7 0:3755 5 2:6247 0:5367 W 0 ¼ ½ 0:4513 0:2342 0:1674 0:8596 0:7737  3 2 3:7772 7 6 6 1:0711 7 7 6 7 b1 ¼ 6 6 0:7564 7and b0 ¼ 0:7606 7 6 4 1:8943 5 2:6865

379

Optimum mass flow rate (Kg/s)

1000

gh

800

Ta

40

2

45

600

35 400

30 25

200 6

8

10

6

8

10

12

14

16

0 18

12

14

16

18

Solar radiation (w/m )

Ambient temperature (°C)

M.B. Ammar et al. / Energy Conversion and Management 65 (2013) 372–380

0.022 0.018 0.014 0.010 0.006 0.002

Optimum mass flow rate (Kg/s)

45

1000

40

800 600

35

400

Ta 30 25 14

gh 14.5

15

200 0 16

15.5

2 Solar radiation (w/m )

Ambient temperature (°C)

Real solar time (hours)

0.022 estimated 0.018

determinated

0.014 0.010 14

14.5

15

15.5

16

Real solar time (hours)

Fig. 11. Estimated and determined curves of optimum mass flow rate (August 22th 2011 as a representative day of the hot season).

In order to validate the approach, a new data base containing values of solar radiation starting from 350 W/m2 to 950 W/m2 and ambient temperature Ta starting from 5 °C to 35 °C, is established. Computed values using the determination method (Figs. 4 and 5) and those given by ANN are plotted. _ opt for different Figs. 8 and 9 show the optimum mass flow rate m solar radiations and for different ambient temperatures. The ANN estimated mass flow rates which represent the OPOPs are validated by the determined OPOPs from P  Q curves. It is clear that the ANN controller accurately estimates optimum mass flow rates for any solar radiation and ambient temperature. In order to evaluate the approach performance, estimated and determined optimum mass flow rates are analyzed by computing the Normal Mean Bias Error (NMBE) calculated as follows [20]:

NMBE% ¼

1 M

PT a ¼35:5 g h ¼950:100 X _ opt  detemined ðestimated m T a ¼5 PT a ¼35:5 _ opt determined m g h ¼350 T a ¼5

 100 ¼ 13:05%

_ opt Þ m ð21Þ

where M is the number of reading points, The NMBE provides information on the ANN performance. The value of NMBE for different values of ambient temperature (5–35 °C) and solar radiation (350–950 W/m2) is 13.05%. In the aim to concretize the approach, the OPOP is computed during two typical days: cold season day and hot season day (Figs. 10 and 11). For each day, the instantaneous variation of climatic parameters and the determinated and the estimated values of OPOP using a time step of 5 min, are gathered in the same figure. Furthermore, a zoom-in is performed for Figs. 10 and 11, so as to focus and compare finely short term estimated and determinated data for optimum mass flow rate. The cold season is characterized by a disturbed weather during which the estimated value of OPOP is little far from the calculated OPOP due to perturbation of climatic parameters (solar radiation and ambient temperature). This appears by the zoom-in of Fig. 9 where determinated and estimated data are not quite close due to weather instability. On the contrary the weather is stable during the hot season, a great concordance is observed between the two values of optimum mass flow rate (Fig. 11).

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5. Conclusion As solar energy converters must be adjusted according to meteorological variation, they require control algorithms in order to track the optimum generated power. This paper presents an algorithm that determines the Optimum Power Operating Point (OPOP) of a Photovoltaic Thermal (PV/T) function of the variation of solar radiation and ambient temperature. The OPOP computes the optimum flow rate for the PV/T in order to track simultaneously maximum thermal and electrical powers. As the PV/T is characterized by a multivariable and nonlinear model, an Artificial Neural Network (ANN) has been used to calculate OPOP. Obtained results confirm that the approach delivers fast and accurate PV/T flow rate control. A novel PV/T control is actually under development in order to track maximum generated electrical and thermal powers by taking into account the user need. References [1] Algazar Mohamed M, AL-monier Hamdy, Abd EL-halima Hamdy, El Kotb Salem Mohamed Ezzat. Maximum power point tracking using fuzzy logic control. Electr Power Energy Syst 2012;39:21–8. [2] Messai A, Mellit A, Guessoum A, Kalogirou SA. Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation. Sol Energy 2011;85:265–77. [3] Syafaruddin, Karatepe Engin, Hiyama Takashi. Performance enhancement of photovoltaic array through string and central based MPPT system under nonuniform irradiance conditions. Energy Convers Manage 2012;62:131–40. [4] Welch Richard L, Venayagamoorthy Ganesh Kumar. Energy dispatch fuzzy controller for a grid-independent photovoltaic system. Energy Convers Manage 2010;51:928–37. [5] Ammar Mohsen Ben, Chaabene Maher, Elhajjaji Ahmed. Daily energy planning of a household photovoltaic panel. Appl Energy 2010;87:2340–51. [6] Chekired F, Larbes C, Rekioua D, Haddad F. Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit. Energy Proc 2011;6:541–9.

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