modification design of river capacity as flood mitigation …

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BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY (BJET) VOL.13 NO.1, FEBRUARY, 2018

75

MODIFICATION DESIGN OF RIVER CAPACITY AS FLOOD MITIGATION STRATEGY

IN THE KAFIN HAUSA FLOOD PLAIN AREA, JIGAWA STATE-NIGERIA

Abdulwahab, M. and Maina, M. M*

Department of Agricultural and Environmental Engineering, Bayero University, Kano

*Correspondence Author: [email protected]. +2349092205217

ABSTRACT

Floods occur repeatedly in Kafin Hausa flood plain areas and they cause tremendous losses in terms of property and

life. The study was carried out to determine the causes, effect and suggest measures in tackling such disaster. In-situ

methods were used in carrying out some of the tests mainly infiltration and ground water fluctuation test. Rainfall and Dam discharge data were collected; GPS, GIS, and Google earth application/software were used to locate,

delineate and determine hydrological features of the study area. The soil have low water infiltration rates of approximately 72.1 mm/hr, 59.1 mm/hr, 48.47 mm/hr, and 37.7 mm/hr, for sand, silt- loam, sandy-loam, and clay-

loam soil respectively. A shallow ground water level of minimum and maximum value of 8.03 m and 10.30 m

respectively, with an estimated fluctuation value ranges between 0.15m - 0.02m. Twenty (20) years Return Period was used to estimate the generated runoff over the study area (16.225 km

2) to be 1,587.205 m

3/s. The river geometry

was modified to have a capacity of 3,986.890 m3/s which highly exceeds initial river capacity of 2,087.286 m

3/s. It is

recommended that the present river dimension be dredged to the new dimensions which will accommodate any future floods.

Keywords: Design, flood, Floodplain, Kafin Hausa, infiltration, geospatial and rainfall.

1.0 INTRODUCTION

Floodplain is an area of land adjacent to a stream or

river that stretches from the banks of its channel to the

base of the enclosing valley walls and experiences

flooding during periods of high discharge (Goudie,

2004).

A flood that rises rapidly, with little or no advance

warning, is called a flash flood. Flash floods usually

result from intense rainfall over a relatively small

area, or if the area was already saturated from

previous precipitation (Henry, 2006).

Floods are caused by many factors: heavy rainfall,

highly accelerated snowmelt, severe winds over water,

unusual high tides, tsunamis, or failure of dams,

levees, retention ponds, or other structures that retain

water. Flooding can be exacerbated by increased

amounts of impervious surface or by other natural

hazards such as wildfires, which reduce the supply of

vegetation that can absorb rainfall (Welch et al.,

1977).

The high flood risk areas in Nigeria are predicted over

Sokoto-Rima Basin (Sokoto, Zamfara States),

Komadugu Yobe Basin (Yobe, Bauchi, and Jigawa

States), some parts of Niger-Benue Basin (Kebbi,

Nasarawa, Taraba, Adamawa States) and the Ogun-

Osun Basin (Oyo and Osun States (NIHSA, 2014).

The aim of this study was to delineate total area

covered by the floodplain using satellite imagery,

study the effect of flash flood and to design a drainage

system that can accommodate the excess runoff

generated to avoid flood at Kafin Hausa floodplain.

mailto:[email protected]

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2.0 MATERIALS AND METHODS

2.1 Site Description

The study area (Kafin Hausa catchment) is located in

Kafin Hausa, Jigawa State, Nigeria, its geographical

coordinates are 12.034246oN, 9.921866

oE and

12.215122oN, 9.991223

oE and elevation of

approximately 360 m with a mean minimum

temperature of (12°C) and an approximate annual

rainfall of 600 mm.

2.2 Climatic and Hydrological data

Discharge from Tiga dam was collected from Hadejia-

Jamare River Basin Development Authority

(HJRBDA), the climatic data for twenty one years

record was obtained from Hadejia office of HJRBDA

and Biniyaminu Usman College of Agriculture,

Hadejia. The data consist of temperature (min, max),

Rainfall, Relative humidity, wind speed, radiation,

cloud cover.

The study area has a soil type ranging from sandy

loam, silt and clay which are the most dominant in the

site. Figure 1 shows the area affected by flood and the

nearby villages which are affected by the floods.

Figure 1: Map of Kafin Hausa Flood Plain

2.3 Infiltration Test Considering the vast area to be analyzed in this

research, the plots were chosen based on the

predominately soil type in the area, mainly; sandy,

silt-loam, sandy-loam and clay-loam. Double Ring

Infiltrometer was used for the experiment, the result

were analyzed using Kostiakov equation.

Kostiakov = I = Kta(1932) …. (1)

Where: K = intercept, and a = slope.

2.4 Ground Water Level Investigation

Four wells were selected at different locations based

on the proximity of these wells to the River Kafin

Hausa, the wells were levelled ‘A’, ‘B’, ‘C’, ‘D’ with

elevations of 372.8 m, 366.8 m, 371.4 m and 367.8 m

respectively. The water level was taken at a week

interval (Lin et.al., 1998) using steel measuring tape.

2.5 Design of Drainage System

The initial river dimension was measured and

catchment area was estimated using Google earth and

ArcGIS 10.3 software. Figure 2 below shows the flow

direction of the floods and the target area of

inundation.

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77

Figure 2. Direction of flow contributing to river Kafin Hausa The estimated contributing catchment area was

approximately165,047.31 ℎ𝑎. The river discharge

was computed using Manning’s formula (eq. 2 and 3.)

Q = AV ….…. (2)

V= 1

𝑛𝑅ℎ

2 3⁄ 𝑆1 2⁄ …….... (3)

Where: V = average velocity (m/s); Rh = hydraulic

radius (m) and is equal to A/Pw; n = Manning’s

roughness coefficient; s = Slope (m/m).

The run off was calculated using rational formula

𝑄 =𝐶𝐼𝐴

360 …. (4)

Where: Q = Discharge (m3/s); C = runoff coefficient; I

= rainfall intensity (mm/hr); A = catchment area (ha).

3.0 RESULTS AND DISCUSSION

3.1 Infiltration rate

Figure 3. Shows how water moves through the soil

column over time, the slow movement of water is

attributed to the high moisture level, soil type, and a

rainfall of 142 mm which was recorded in the month

of August 2015 at the time of the study.

The infiltration rate after 216 min (3.6 hrs) for the

sandy, silt loam, sandy loam and clay loam soils were

72.1, 59.1, 48.4, and 37.7 mm respectively. The

infiltration rate was considered to be low when

compared to the infiltration rates of some studies (Lin

et al., 1998). Because of slow infiltration rate, runoff

is fast generated which resulted in flood.

Figure 3. Average Infiltration rate curve.

27.0

47.0

67.0

87.0

107.0

127.0

147.0

0 50 100 150 200 250

i (m

m/h

r)

t (min)

sandy soil silt loam soil

sandy loam clay loam

Direction of flow

Auyo

Taura

a

Jahun

Kafin Hausa

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78

3.1 Rainfall Analysis

According to Gwary (2008) and Adeoti et al., (2010),

the cause of flood in Nigeria is mainly due to excess

rainfall and poor waste disposal, which is not always

the case especially in the agrarian areas. And this be

ascertained in Figure 4, with exception of 2015 with

record of 330.5 mm which did not result into any

flood. Therefore; based on this study torrential rain

was considered to be the major cause of flood in the

study area, this conforms to the Hydrological Services

Agency report by Gwary (2008) and Adeoti et al.

(2010).

Therefore heavy down pour coupled with discharge

from Tiga and Challawa Dams can be said to be the

major cause of flood over the years from the flood-

rainfall analysis.

But the study exonerates dam discharge as the sole

causes of flood in the area due to its insignificance

changes over the years.

Figure 4. Trends of Annual Rainfall Record for the

Years 1995-2015.

Figure 5. Water fluctuation between the

wells “A”, “B”, “C”, and “D”

3.2 Ground Water Test

The graphs show that low water fluctuates over time;

this can be as a result of the amount rain recorded

within the week intervals and the overall extraction of

water from the wells.

The fluctuation was high between the 1st and 9th

weeks and then later stabilized as rainfall frequency

drops over time which was as a result of variation in

the pumping rate. Though the ground water level was

shallow in all the wells, which is due to the proximity

of the wells to the river and the rainfall event.

In order to understand the level of variability in the

water level fluctuation between and within the wells,

the Analysis of Variance (ANOVA) was used. The

variability in water level between the observation

wells was significant at 1% confidence level. This was

the result of the difference in the location and

proximity to the river. The well grouping using

Duncan’s multiple test shows, Well B and C are not

significantly different followed by Well A and Well D

with a mean value of 9.79 m.

3.3 River Bathymetry and Design

In order to come up with a remedy to mitigate the

effect of flash flood in the floodplain, the initial river

0

200

400

600

800

1000

1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5

RA

INFA

LL (

MM

)

YEARS

7.50

8.00

8.50

9.00

9.50

10.00

10.50

1 3 5 7 9 11 13 15 17 19 21 23

WA

TER

LEV

EL IN

MET

ERS

TIME INTERVAL IN WEEKS

A

B

C

D

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79

geometry was measured and computing the velocity

and discharge of the river.

Infiltration, precipitation and the estimated catchment

area to the river were considered in the new design

geometry of the river, which when dredged to the

specification given will accommodate the excess

runoff that might be generated in the event of high

storm. Table 1 presents the initial and the new design

of Kafin Hausa river dimensions.

The new designed river geometry was achieved by

considering a generated runoff over the catchment

area of 1,650,473,100 m2 and highest storm return

period of 20 years. The effect of climate change which

may lead to unexpected increase in precipitation was

also considered. According to Agbonkhese et al.

(2014), in more recent years, 2011 and 2012 appear to

be the worst incidence of flooding in Nigeria, which

revealed that, most of the affected states recorded over

60% increase of rainfall in the period which may be as

a result of climate change.

Based on these facts, the new river geometry was

estimated and a new discharge capacity of 3,986.890

m3/s; flowing at a velocity of 22.704 m/s was

obtained, depth and width of 5.44 m and 21.4 m

respectively. Therefore, dredging of River Kafin-

Hausa to an increase depth of 1.47 m will modify the

capacity of the river to 3,986.890 m3/s, thereby

carrying virtually all the water along its watercourse

without spilling the river bank.

Table 1: Initial and Designed River Dimension

Design

Parameter

Initial

Dimension

New

Designed

Dimension

Depth (m) 3.97 5.44

Bottom Width

(m)

21.4 21.4

Area (m2) 116.478 175.603

Velocity of

flow (m/s)

17.92 22.704

Flow Discharge

(m3/s)

2,087.286 3,986.890

4. CONCLUSION

Natural disaster such as floods and associated hazards

may be inevitable, but they can be minimized and

turned in to an opportunity in life. People tend to live

near floodplain and farm due to its high fertility and

easy access to water even though it involves many

risks.

The research considered many factors ranging from

infiltration, discharge from Tiga and Challawa Gorge

Dams, ground water level and rainfall to determine the

causes of flood in Kafin Hausa floodplain. After the

investigation, it was revealed that torrential rain over

the catchment generated high runoff and small size of

the river led to overflow the river bank. It was

observed that, rain recorded in 2015 happened to be

the least when compared to the previous years’

records. The infiltration rate was low and the ground

water fluctuation and the dam discharges was nearly

constant over the years.

It can be concluded by saying; low rainfall of 330.5

mm experienced during the year of the study resulted

in no flood in the area. Twenty (20) years return

period was used to take care of the effects of climate

change. Dredging process was recommended thereby

increasing the depth of the river by 1.47 m. The

process will increase the discharge capacity of the

river from 2,087.286 m3/s to 3,986.890 m

3/s. This will

reduce the flood effect as all the water will be

contained in the watercourse.

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80

REFERENCE

Adeoti, A. I.; Olayide, O. E.; and Coaster, A. S.

(2010). Flooding and fishes’ welfare in Lagos state,

Nigeria. Journal of human ecology Vol. 32(3) 161-

167.

Agbonkhese, O., Agbonkhese, E.G., Aka, E.O., Joe-

Abaya, J., Ocholi, M., and Adekunle, A. (2014).

Flood Menace in Nigeria: Impacts, Remedial and

Management Strategies. Journal of Civil and

Environmental Research, ISSN 2224-5790, Vol.6, No.

4.

Goudie, A.S., (2004). Encyclopedia of

Geomorphology, Vol. 1. Routledge, New York. ISBN

0-415-32737-7.

Gwary, D. (2008). Climate change, food security and

Nigeria agriculture. Paper presented at the workshop

on challenges of climate for Nigeria. NISER 19th –

20th May, 2008.

Henry, P. (2006). Levees and other raised Ground.

Am. Sci., 94(1): 7 – 11

Kostiakov, (1932). On the Dynamics of the

Coefficient of Water Percolation in Soils and on the

Necessity for Studying it from a Dynamic Point of

View for Purpose of Amelioration, Trans. Int. Congr.

Soil Sci. 17-21.

Lin, H.S., Mclnnes, K.J., Wilding, L.P., and Hallmark,

C.T. (1998). Macroporosity and initial moisture

effects on infiltration rates in vertisols and vertice

integrades. Soil Sci.163:28

Logsdon, S.D and D.B, Jaynes. 1996. Spatial variabili

ty of hydraulic conductivity in a cultivated field at

diffrernt times. Soil Sci. Soc. Am. J. 60:703-709.

Manning, R. (1891). On the flow of water in open

channel and pipes. Transections of the institutes of

civil engineers of Ireland. 20:161-207.

NIHSA (2014). Nigeria Hydrological Service Agency

Flood Outlook, 2nd Edition 2014.

Welch, H., Symons, P., & Narver, D. (1977). Some

Effects of Potato Farming and Forest Clear Cutting on

New Brunswick Streams, Fisheries and Marine

Service. Environ. Can Technical Report No. 745, St.

Andrew’s New Brunswick.Wetland in Jigawa state.

An ecological survey of the wetlands in Jigawa State

report compiled by HNWCP for Jigawa State

Government (2001).

ISSN: 2449 – 0539 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY (BJET) VOL.13 NO.1, FEBRUARY, 2018

81

EFFECT OF COOLING ON PARASITIC RESISTANCES OF SOLAR PHOTOVOLTAIC

MODULES IN AN UNCONTROLLED ENVIRONMENT

Mawoli, M.1 and Anene, E.C.

2

1 Sokoto Energy Research Centre, Usmanu Danfodiyo University, Sokoto Nigeria.

2 Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University Bauchi Nigeria.

Corresponding author e-mail: [email protected]

ABSTRACT

The current study investigated the effect of cooling on parasitic resistances of solar PV modules in an uncontrolled

environment. An experimental setup comprising of two solar PV modules were subjected to the same external

conditions and the data were collected between the hours of 09:00 to 16:00 with one of the solar modules been

subjected to cooling process using a DC submersible pump. The DC submersible pump was consistently controlled

(power ON/OFF) by module's surface temperature. The results have shown that solar PV module series resistance RS

tends to decrease as module's temperature decreases through the process of cooling from 4.49Ω to 2.53Ω, whereas

its shunt resistance RSH increases with decrease in module's temperature from 92.83Ω to 335.89Ω. The relationship

between modules' output power, Pmax and series resistances, RS were established to be exponential functions.

Modules' output power, Pmax and equivalent resistances, Req were observed to be exponentially related. Under initial

power condition of RS=0, the solar module maximum power with cooling and without cooling unit were found to be

132.49W and 92.45W respectively. Under ideal solar module condition of Rs→0 and RSH→∞, there was 8.6%

improvement in maximum power resulting from effect of cooling. Short circuit current and output power were also

observed to be linearly related with negative gradients. Hence, minimising solar PV module temperature through the

process of cooling caused equivalent, and series resistances to decreases but shunt resistance increases; the

cumulative effect was exponential increase in solar module maximum power.

Keywords: series resistance; shunt resistance; solar cells; module's cooling; uncontrolled environment.

1. INTRODUCTION

Electrical behaviour of solar photovoltaic cells are

governed by some parameters. These parameters can

be categorized into internal and external parameters.

Series resistance RS; shunt resistance Rsh; and

saturation current IS are categorized under internal

parameters. External parameters include irradiance, G

and temperature, T.

The fill factors (FF) of commercial solar cells are

lower than ideal primarily due to series resistance

(RS), which will become larger as substrate size

increases. However, in both laboratory and production

cells, the fill factor is not solely limited by the RS but

also by effects such as low shunt resistance and non-

ideal diode parameters (Bowden and Rohatgi, 2001).

The series and shunt resistances were determined

using analytical five point method (Cotfas, 2010), as

thus given in equations 1 and 2.

IscI

SdI

dVR

... (1)

VocV

SdI

dVR

.... (2)

Solar cells' series parasitic resistances rS is defined as

the ratio between solar cells' series resistance to

characteristic resistance of the solar cells. However,

solar cell characteristic resistance is defined as the

ratio between voltage to current at maximum power

operating point. That is,

CH

SS

R

Rr ... (3)


82

CH

SHSH

R

Rr ... (4)

Where characteristic resistance, RCH is

max

max

I

VRCH ... (5)

Series and shunt resistances affect the fill factor of

solar photovoltaic modules as expressed in the

equations below

SS rFFFF 10 ... (6)

SH

shr

FFFF1

10 ... (7)

Where: RCH is the characteristic resistance, FFs and

FFsh are fill factors in the absence of RS and Rsh, and

FF0 is the fill factor.

Problems associated with series resistance stems from

high current densities under concentrated light. The

shunt resistance is due to the leakage current at the

junction and depends on the method of junction

construction. The voltage-current characteristic

equation of a non-ideal solar cell is given in equation

8, while the equivalent circuit model is shown in

Figure 1 (Massoud et al., 2012),

Figure 1. Equivalent circuit model for a non-ideal solar cell

SH

SS

SphR

RIV

TkA

RIVqIII 1

)(exp

... (8)

Where: I is the current to the load, Iph is the photon-

generated current, Is is the diode reverse saturation

current, q is the electron charge, V is the voltage

across the diode, k is the Boltzmann’s constant, T is

the junction temperature, A is the ideality factor of the

diode, and Rs and Rsh are the series and shunt

resistances of the cell respectively.

Bensalem and Chegaar (2013) investigated thermal

behaviour of parasitic resistances of polycrystalline

silicon solar cells in a controlled environment under

constant illumination of 1000 Wm-2

and found out that

the series resistance increases with temperature

whereas shunt resistance decreases with temperature.

El-Shaer et al. (2014) carried out a study on the effect

of light intensity and temperature on crystalline silicon

solar modules parameters and reported that the series

resistance of each module decreases with increasing

light intensity due to the increase in conductivity of

the active layer. The parallel resistance also decreases

with increase in light intensity.

Ghoneim et al. (2011) analysed the performance

parameters of amorphous photovoltaic modules under

different environmental conditions and reported that

series resistance Rs exhibits a dramatic change with

illumination intensity, that is, it increases from

approximately 6.5 to 12.3Ω within a narrow range of

irradiance. As the intensity is increased, Rs starts to

decrease slowly in the medium range of irradiance

then quickly in the high range (≥800 W/m²).

Dieme and Sane (2016) investigated the impact of

parasitic resistances on the output power of a parallel

vertical junction silicon solar cell and reported that the

power increases along with shunt resistance but

decreases with increase in series resistance. In other

words, a solar cell is considered powerful when the

series resistance is low and shunt resistance is high.

A study carried out by Tobnaghi et al. (2013) on the

effect of temperature on electrical parameters of solar

cells have shown that the most significant change by

temperature is voltage which decreases with

increasing temperature while output current slightly

increase by temperature. It was also reported that

reduction in the open-circuit voltage for silicon solar

cells is about 2 mV/oC (Tobnaghi et al., 2013). In

addition, increase in temperature causes reduction in

maximum power output by 0.005 mW/oC. The best

performance of solar panels in sunny and cold days

has been suggested.


83

A study carried out by Ike (2013) on the effect of

temperature on the performance of a photovoltaic

solar system in eastern Nigeria have shown that there

is an indirect proportionality between the power

output performance of the system and the ambient

temperature. It was concluded that due to

unfavourable condition resulting from very high

ambient temperature compared to the low ambient

temperature in converting solar energy into electricity

using solar modules, the solar photovoltaic panels

should be installed at a place where they receive more

air current so that the temperature remains low for

enhanced power output. In other words, effective

cooling by air can reduce modules temperature

thereby improving on its power output.

Temperature among other factors contributes in

affecting the performance of the solar modules.

Reviewed literatures on the effect of temperature on

parasitic resistances limited the studies under

controlled environments. However, this study was

carried out in an uncontrolled environment.

2. EXPERIMENTAL PROCEDURE

The experiment was setup at Sokoto Energy Research

Centre located in Usmanu Danfodiyo University,

Sokoto Nigeria. In the setup, two (2) polycrystalline

silicon solar modules manufactured by JOYSOLAR

with total area of 1.08 m2 were deployed; the solar

modules were oriented to face true South supported by

structures that were designed to provide tilt angles of

15°. The fluid circulation by a DC submersible pump

was regulated at a module's preset temperature of

45oC.

The experiment was conducted under the same

outdoor conditions of solar radiation, ambient

temperature, wind speed and humidity. Solar

radiation, ambient temperature, modules surface

temperature, wind speed and humidity were measured

and recorded between the hours of 09:00 to 16:00

daily for a period of three (3) months (May - July) at

an interval of 30 minute. The experimental setup and

the equivalent circuit diagram are shown in Figures 2

and 3 respectively. At seven different positions of

slider on rheostat, currents and voltages were

measured and tabulated for the two solar modules for

every interval of 30minute. In addition, short circuit

current, ISC and open circuit voltage, VOC were

measured and recorded.

Figure 2. Experimental setup


84

Figure 3. Equivalent circuit diagram for cooling system

3. RESULTS AND DISCUSSION

The relationship between output power and series

resistance for solar modules with and without cooling

is shown in Figure 4. Modules' output power varies

exponentially with series resistance for both solar

module with and without cooling. Therefore, the Pmax

and Rs for the two solar modules are related as thus:

SRpvt

m eP15.0

49.132

... (9)

SRpv

m eP04.0

45.92

... (10)

Exponential coefficients 132.49 and 92.45 in

equations 8 and 9 are the vertical stretching factors,

otherwise referred to as initial maximum power

conditions at RS equals zero (i.e., RS→0). Therefore,

at RS≈0,

WPandWP pv

m

pvt

m 45.9249.132

Figure 4: Output power-Vs-series resistance relationship for PV modules with & without cooling

Pmax = 132.49e-0.15Rs

R² = 0.640

Pmax = 92.45e-0.04Rs

R² = 0.771

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0.00 5.00 10.00 15.00 20.00

Outp

ut

po

wer

, P

max

(W

)

Series resistance, Rs (Ω)

PV/T PV


85

In terms of the equivalent resistance, Req, modules'

output power for PV module with and without cooling

are exponential functions of Req, as thus shown in

Figure 5.

Figure 5: Output power-Vs-equivalent resistance relationship for PV module with and without cooling

The output power for modules with and without

cooling systems are given in equations 10 and 11

respectively.

eqRpvt

m eP148.0

14.132

... (11)

eqRpv

m eP146.0

01.111

... (12)

For an ideal solar module, Rs is extremely small and

Rsh high, that is, Rs≈0 and Rsh≈∞; therefore, Req=0.

WP pvt

m 14.132, and

WP pv

m 01.111

Therefore, the percentage difference in terms of

maximum power between solar module with cooling

and without cooling unit as a function of module

equivalent is 8.6%.

Series and shunt resistances are pictorially compared

in Figures 6 and 7. This enables proper analysis of

effect of cooling on the solar modules parasitic

resistances.

Figure 6: Effect of cooling on module series resistances

Pmax = 132.1e-0.14Req

R² = 0.628

Pmax = 93.96e-0.04Req

R² = 0.781

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00

Mo

dule

outp

ut

po

wer

, P

max

(W

)

Module equivalent resistance, Req (Ω)

PV/T PV

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00

Mo

dule

ser

ies

resi

stan

ce,

Rs

(Ω)

Time, t (hr)

PV/T PV


86

It can therefore be concluded that, the lower the

temperature, the smaller the series resistance. The

average series resistances recorded for modules with

and without cooling system are 2.53Ω and 4.46Ω

respectively.

Figure 7: Effect of cooling on modules' shunt resistances

It can be observed from Figure 6 that minimizing solar

cells' temperature resulted in an increased shunt

resistance values. The mean shunt resistances for PV

modules with and without cooling were 335.89Ω and

92.83Ω respectively. It is an indication that as solar

PV module temperature appreciates, the PV module

shunt resistance decreases.

The result in Figure 8 is a relationship between solar

cells characteristic resistances and short circuit

current.

Figure 8: Rch - Isc characteristic for modules with and without cooling system.

Rch and Isc are linearly related for both modules. The

output power and characteristic resistances for both

modules are also linearly related as thus presented in

Figure 9.

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

Mo

dule

shunt

resi

stan

ce,

Rsh

(Ω

)

Time, t (hr)

PV/T PV

Isc,pvt = -0.265Rch + 6.319

R² = 0.846

Isc,pv= -0.254Rch + 6.320

R² = 0.958

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.00 7.00 9.00 11.00 13.00 15.00 17.00 19.00

Sho

rt c

ircu

it r

esis

tance

, Is

c (A

)

Characteristic resistance, Rch (Ω)

PVT PV


87

Figure 9: Pmax - Rch characteristic for modules with and without cooling system.

Power losses per 1Ω increase in characteristic

resistance for PV module with and without cooling

system are 6.32W and 5.36W respectively.

Furthermore, the effect of cooling on PV modules'

parasitic resistances (rs and rsh) is shown in Figure 10.

Figure 10: Effect of cooling on module parasitic resistances

The result presented in Figure 10 has clearly shown

that the module parasitic series resistances for PV

modules with and without cooling system (rs,pvt and

rs,pv) are negligible as compared to parasitic shunt

resistances rsh,pvt and rsh,pv. However, it can be

observed from Figure10 that parasitic shunt

resistances, rsh,pvt for PV module with cooling system

is greater than that of PV module without cooling

system, except at 09:00. It can therefore be deduced

that as module's temperature reduces through the

process of cooling, the module parasitic shunt

resistance increases thereby improving the module

efficiency.

Pmax,pvt = -6.321Rch + 156.5

R² = 0.877 Pmax,pv = -5.358Rch + 138.9

R² = 0.940

40.00

50.00

60.00

70.00

80.00

90.00

100.00

110.00

120.00

6.00 8.00 10.00 12.00 14.00 16.00 18.00

Outp

ut

po

wer

, P

max

(W

)

Characteristic resistance, Rch (Ω)

PVT PV

0

10

20

30

40

50

60

70

80

90

9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00

Module

par

asit

ic r

esis

tance

s, r

s,sh

(Ω

)

Time, t (hr)

rs,pvt rs,pv rsh,pvt rsh,pv


88

4. CONCLUSION

The results presented have clearly shown that the

cooling process has effect on the module maximum

power both in the case of series resistance and

equivalent parasitic resistance with initial maximum

power as 132.49W and 92.45W for PV module with

cooling and without cooling. The maximum power

under ideal PV module conditions of Rs and Rsh for

PV module with cooling unit recorded a maximum

power of 132.14W whereas the module without

cooling recorded power of 111.01W, with percentage

difference of 8.6%.

It could also be concluded that the cooling process

resulted in lowering the PV module temperature

thereby causing PV modules' (with and without

cooling system) Rs and Rsh to be 2.53 Ω & 335.89Ω,

and 4.49 Ω & 92.83 Ω respectively. In other words,

since PV/T fill factor is greater than PV module fill

factor, it can therefore be deduced that a decrease and

an increase in series and shunt resistances cause fill

factor to improve. The reverse is the case for Rs>>0

and Rsh<<∞; that is, the fill factor tends to decrease.

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Bowden, S. and Rohatgi, A. (2001) Rapid and Accurate

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