solar power plant reliability incorporating insolation availability · 2018-08-07 · ter –...

Solar Power Plant Reliability Incorporating Insolation Availability

Elezabeth Paul

Govt.Model Engineering College

Thrikkakara, Cochin, Kerala, India

Shouri P V

Govt.Model Engineering College

Thrikkakara, Cochin, Kerala, India

Abstract— Solar power generation has been growing drasti-

cally over the recent years owing to increasing energy demandsas well as growing concerns of fossil fuel consumption. The reliability estimation of the solar power plants has been receiving increasing attention. This is largely because of ongoing changes in generation investments and environmental constraints. Many of the researches for reliability estimation of solar system considered the reliability and availability of hardware components such as solar panels, inverters etc. only. But input source or solar insolation is an important element on which the reliability of hardware depends. Hence this work aims at determining the reliability of solar power plant that captures effects of input variability and failures of system components. Modelling is based on preparing a control chart using measured values of power and solar insolation for the given locality at different points of time. A model is developed for evaluation of solar insolation reliability beyond the lower limit of insolation using stream flow model hence reliability variation over time can be determined.

Keywords— Reliability, input variability, insolation, stream flow

model.

I. INTRODUCTION

Solar power generation is one of the most efficient and

popular means of utilizing renewable energy owing to in-creasing energy demand and rising cost of alternate energy sources. Recently there is an increasing attention to estimation of solar system reliability which is mainly due to ongoing changes in generation investments and environment con-straints.[1],[2] A grid connected photovoltaic system consist of many components such as solar panels, solar inverters etc. Many of the researches done so far in reliability estimation have considered only the reliability and availability of hard-ware components while neglecting the availability of input source. Input source or solar insolation is an important element which affects the operations and functions of the hardware components. Hence while estimating the system reliability, individual reliabilities of both solar insolation and

hardware needs to be considered.[8]

The main objective of this work is to find the reliability

of a solar power plant incorporating both solar insolation

availability and hardware reliability and hence find the pe-

riods during which power plant is unreliable where alternate

energy sources may be required.[7]

II. MODEL APPLIED TO PRACTICAL

SITUATION

First, Reliability modeling is assessed using the practical

data collected from grid connected PV system located at

Govt.Model Engineering College, Thrikkakara, Ernakulam

(latitude : 10.02860 north, longitude : 76.3290 east). This

30kW system operates from July 2017. The power

generated by the solar plant is utilized at the consumer

point, excess power is fed into the grid and deficient power

is fed from the grid.

TABLE .1 Main components of plant

Sl.No

Item Specification Make

1 PV Module 300 Wp, 72 Cell

Polycrystalline

Australian

Premium

Solar

2 String inverter 15KW with inbuilt

logger

Fronius

3 AC energy meter 3phase 4 wire,

230V, 10-60A,

direct

connected

L&T

4 Tri-Vector

Meter ER300P

Frequency 50Hz

+/-10%

L&T, ER300P

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 3 (2018) Spl. © Research India Publications. http://www.ripublication.com

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This 30 kW rated system has an output DC voltage of

200-800 V and an output AC voltage of 220 V. It comprises

three strings of which two strings consist of 32 modules and

one string of 36 APSP6-300/72 (300 W) PV modules . One

15 kW solar PV inverter (Fronius Symo 15.0-3-M) is con-

nected with 300w modules, 3 strings. L&T Tri-Vector Me-

ter – ER300P data logger acquires the data through a con-

nection to the local grid through an inverter, a safety control

box and a solar energy meter.

III. MODELING OF PV SYSTEM USING

CONTROL CHART

As mentioned earlier, objective of work is to arrive at

solar power plant reliability and involves calculation of

power outputs from solar insolation. A control chart is pre-

pared using the measured values of power and solar insola-

tion for the given locality at different points in time. The

upper limit and lower limit corresponds to 3σ (standard

deviation) limits and solar insolation below 3σ value corre-

sponds to zero reliability.

Since the plant is operated from July 2017, the unknown

values of power can be calculated from the following equa-

tions :

Pout = ηcell * Pin (1)

Pin= (average solar insolation) * (area of panel) (2)

Including the calculated values with the measured values,

the monthly power outputs over the entire year can be ob-

tained. Following steps can be used for obtaining the con-

trol chart.

1. Calculate the monthly average value of power or X ̅

for the year (kW) as follows

TABLE 2. Monthly average value or X̅

January February March April May June 16.17 17.18 21.51 21.28 18.09 12.16

July August September October November December 13.11 14.6 17.57 15.82 16.34 15.72

2. Calculate X̿ for the above value

X̿ = ∑X̅/n,

where n is the number of samples.

3. Calculate range or R values as follows

January February March April May June

7.25 11.25 5.09 3.19 3.97 7.09

July August September October November December

6.08 10.52 7.87 6.89 11.38 11.71

4. Calculate R̅ value

R̅ = ∑R/n,

where n is the number of samples.

5. Calculate control limits for the chart, given by

UCL (Upper Control Limit) = X̿+ R̅

LCL (Lower Control Limit) = X̿- R̅,

where = 0.308 (constant value when

number of samples is above 10)

Figure 1.UCL and LCL for power

From the control chart we get LCL=14.284kW. This value

is taken as the minimum acceptable value of power output and

solar insolation values less than this corresponds to zero re-

liability. From the equations mentioned earlier value of

minimum solar insolation = 4.8kWh/ .

IV. RELIABILITY ESTIMATION OF

THE SYSTEM

As mentioned earlier this work captures the effect of both

solar insolation availability and hardware component

reliability. In order to build such a generation model, two

modeling steps are taken for PV system, (1) modeling the

dependency of output on variability of solar insolation and

(2) dependency of failure rate of components on reliability.

A. Reliability of Solar Insolation

The monthly average data for solar insolation at the loca-

tion is analyzed by using stream flow model to obtain the

reliability of solar insolation. From the resultant graph the

relation between solar insolation, power and reliability can

be modeled. Following table gives the monthly average

solar insolation (kWh/ ) data for the location.


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Table.3 Monthly average solar insolation

January February March April May June July August September October November December

5.63 6.18 6.58 6.03 5.39 3.96 4.17 4.46 5.29 4.8 4.87 5.17

The above data is analyzed using stream flow modeling to obtain the reliability using pre-defined class intervals as the ta-

ble below. Here the first column

Table.4 Reliability modeling of solar insolation

Class interval with

respect to mean Insolation(kWh/ ),I

Frequency

n

Cumulative frequen-

cy N

Failure probability

F(I)

Reliability

R(I)

I 0 0 0.0000 1.0000

I̅ - S 4.4683 2 2 0.1666 0.8334

I̅ - S/2 4.846 2 4 0.3334 0.6666

I̅ 5.225 2 6 0.5 0.5

I̅ + S/2 5.603 2 8 0.6667 0.3333

I̅ + S 5.9817 1 9 0.75 0.25

I̅ + 3S/2 6.36 2 11 0.9166 0.0834

I̅ + 2S 6.7384 1 12 1 0

I̅ + 5S/2 7.116 0 12 1 0

I̅ + 3S 7.4951 0 12 1 0

I̅ + 7S/2 7.873 0 12 1 0

In Table.4, I̅ refers to average of insolation (kWh/ )

and S is the variance. Column 1 gives just the intervals to

which the data is grouped.

B. Relation between Solar Insolation and Relia-

bility

Figure 2. Reliability as a function of Solar Insolation

Figure 3.System reliability Versus Insolation

Figure.2 indicates the probability or chance of having a

given solar insolation. But higher the solar insolation, high

er will be the power. Therefore system reliability or proba-

bility that system will deliver the required power will be

compliment of this, given in Figure.3.


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From the graph an equation relating reliability and solar

insolation is modeled given by,

R(I) = 0.0009 - 0.0131 + 0.0575 - 0.0844 – 0.0101i + 1.0029,

R(I) is reliability when insolation is I (kWh/ ).

C. Relation between Power and Reliability

Using (1) and (2) relation between power and reliability

can be modeled.

Figure 4. Reliability Versus Power

From the graph an equation relating reliability and solar

insolation is modeled given by,

R(P) = 1E-06 - 7E - 05 + 0.0012 - 0.0064 - 0.0028p + 1.0029, where R(P) is reliability when power is p (kw).

D. Reliability of System Components

Reliability of system components (Solar panels, solar

inverter, energy meter, tri vector meter ) is determined to

find the overall reliability of system. Probability that the

component will function satisfactorily for at least t units of

time is given by

R(t) =

The overall system reliability can be obtained the equa-

tion

= * * * * , where is solar in-

solation reliability and to refers to system compo-

nent reliability.

The product of reliability of components and solar inso-

lation gives the system reliability. The reliability of the sys-

tem for different months for 20 years are shown in the fol-

lowing figure. Here reliability of system is highest during

March when solar insolation is highest (Table.1) and is

lowest for June, when insolation is lowest.

Figure 5.Reliability Versus Time

V. RESULTS AND DISCUSSION

Figure.5 gives system reliability versus time graph for 20

years. With the passage of time, the reliability of solar inso-

lation also varies with season and month. It can be observed

that reliability is highest for the month of March (having

highest solar insolation) and lowest for June (having the

lowest solar insolation). The figure conceals for a particular

location (latitude : 10.02860 north, longitude : 76.3290

east). Hence the total system reliability is also a function of

season.

VI. CONCLUSION

Solar-energy-based photovoltaic (PV) systems are in-

creasingly gaining worldwide attention due to the high elec-

tricity consumption in combination with the desired envi-

ronmental friendly solutions for power production devel-

opment. Indeed, PV systems are continuously exposed to

many factors that significantly degrade their performances

and efficiency. This paper develops a model for PV system

reliability which captures the effect of both input insolation

variability and hardware component reliability hence esti-

mating the overall reliability of system which is important

for when considering especially initial investment and en-

vironmental constraints.


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Acknowledgements

We would like to thank the reviewers of this article for

their insightful comments, which helped us to greatly im-

prove its quality. The authors would like to thank Govt. Model Engineering college for the support during the

study.

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solar power plant reliability incorporating insolation availability · 2018-08-07 · ter –...

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