A Malfunction Detection Method for Individual Photovoltaic Modules

Masaya Iwata, Yuji Kasai, Eiichi Takahashi and Masahiro Murakawa

National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono,

Tsukuba, Ibaraki, 305-8568 Japan

Keywords: Photovoltaic Modules, Monitoring, Malfunction Detection, Maintenance, Power Line Communications.

Abstract: Although photovoltaic (PV) modules occasionally fail, it is difficult to identify which module is

malfunctioning. In order to detect malfunctioning PV modules, we have developed a malfunction detection

method for individual PV modules by continuously monitoring their data. This method can automatically

identify a malfunctioning module where output power declines at an early stage. Thus, the method provides

faster and more accurate detection of malfunctions. Moreover, the method considerably reduces workloads

for maintenance personnel because it eliminates the need for conventional inspection procedures to identify

a malfunctioning module. A feature of the method is the utilization of two kinds of information among the

PV modules, namely, spatial and temporal correlations, to distinguish between generation declines due to

some malfunction and those due to climate conditions. To confirm the effectiveness of the method, we

conducted a malfunction-detection experiment with actual data from our PV module monitoring system

which we have already implemented. The experiment used 24 PV modules installed within the monitoring

system, and simulated a malfunction by covering 10% of a module. The system was able to detect the

period of the simulated malfunction, which confirms the effectiveness of the method.

1 INTRODUCTION

Photovoltaic (PV) modules are generally believed to

be maintenance-free and to last for more than 20

years, but, in reality, they occasionally fail because

they are industrial products. Accordingly, PV

modules need to be maintained in terms of their

proper timing. However, it is difficult to identify

which module has a malfunction because existing

PV module systems are only capable of displaying

the overall level of power generation from a system

on a power conditioner. In order to detect

malfunctioning modules at an early stage, we have

developed a malfunction detection method for

individual PV modules by continuously monitoring

their data.

The maintenance of PV module systems is

conventionally carried out by checking the level of

solar energy at a power conditioner, or by checking

for abnormalities with the modules or with the cable

connections, etc. at an installation (Tan and Seng,

2011). However, this method results in delays in

detecting malfunctions, and, additionally, imposes

heavy workloads on maintenance personnel to check

each PV module in turn. Thus, recently, awareness

of the need for malfunction detection techniques has

been growing. For example, some studies consider

automatic fault detection by continually monitoring

output power data (Chouder and Silvestre, 2010;

Polo et al., 2010; Stettler et al., 2006). Moreover,

other studies have been developing a fault detection

technique for each string (Phoenix Contact, 2012;

Onamba, 2010) or a technique for monitoring and

maximizing the output power from PV modules

(Tigo Energy, 2009).

With the method proposed in this paper, each

module is continuously monitored in order to

identify as soon as possible abnormal decreases in

output power that would indicate the emergence of

some kind of malfunction. Thus, detecting declines

in output power is fast and accurate with this

method, and the workloads for maintenance

personnel are considerably reduced because the

method eliminates the need for conventional

inspection procedures to identify a malfunctioning

module. A technical feature of the method is that it

can distinguish between generation declines due to

some malfunction and those due to overcast or

climate conditions. This is achieved by utilizing

correlation information from the PV modules and

179

Iwata M., Kasai Y., Takahashi E. and Murakawa M..

A Malfunction Detection Method for Individual Photovoltaic Modules.

DOI: 10.5220/0004378601790184

In Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems (SMARTGREENS-2013), pages 179-184

ISBN: 978-989-8565-55-6

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

power output data is transformed in to a form that

makes it easy to detect malfunctions. We employ

two kinds of correlation information; namely, spatial

correlations involving comparisons among

neighbour PV modules and temporal correlations

involving comparisons with prior power output data.

The method utilizes a monitoring system for

individual PV modules that we have already

implemented (Nosato et al., 2012; Nosato et al.,

2013). The system can monitor the status of each

module through the installation of inexpensive

dedicated transmitters and a receiver that has been

developed with our original technology.

Furthermore, a merit of the system is that no

additional cables are required for communication

purposes because the method uses the DC power

lines from the PV modules as communication lines.

In the present study, we conduct a detection

experiment for a simulated malfunction with actual

data for the PV module monitoring system, which

confirms the effectiveness of the method.

2 MONITORING SYSTEM FOR

INDIVIDUAL PV MODULES

The method requires a PV module system that is

capable of monitoring the power generated by each

module. We have already developed such a system

(Figure 1) (Nosato et al., 2012; Nosato et al., 2013).

This section provides an outline of the system.

The system monitors the status of power

generation for each module through the installation

of dedicated transmitters and a receiver (Figure 2).

An important advantage of the transceivers is that

the system does not require additional cables for

communication, because the transceivers use the

power lines from the PV modules as communication

lines.

A transmitter (Figure 3) is so small that it can be

installed within a junction box, and a receiver

(Figure 3) is installed on the front of a power

conditioner. For communication, we have developed

a new method that is robust to noise by applying

CDMA (Code Division Multiple Access) technology

which is widely used in wireless communication,

such as mobile phones. Each transmitter measures

the voltage, current, and temperature levels for each

PV module and transmits these data. The receiver

monitors the output power, voltage, current, and

temperature for all the PV modules from the data

received from the transmitters. The receiver is

connected to a PC, where monitoring data are

displayed and saved. This system can monitor data

for up to 50 modules at approximately every 18

seconds. Moreover, the transmitter can be

manufactured with inexpensive commercial parts,

and the manufacturing cost is estimated to be 2 to 3

U.S. dollars when mass produced.

In this study, we conduct a malfunction detection

experiment for individual modules with the

monitoring system.

Figure 1: Photograph of the monitoring system for

individual PV modules (with modules #19 and #21, used

in this paper, highlighted).

Figure 2: Overview of the monitoring system for

individual PV modules.

Figure 3: Transmitter (left, 197 x 140 x 75 mm) and

receiver (right, 14.5 x 42.0 mm) for the PV module

monitoring system.

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3 BASIC CONCEPT OF

MALFUNCTION DETECTION

3.1 Target Malfunction for Detection

In this section we describe the malfunction targeted

for detection in this paper.

PV modules with reduced output power can be

identified with this monitoring technology for

individual PV modules. However, from analyses of

the reasons for power fluctuations with our PV

monitoring system, output levels fluctuate markedly

everyday due to (1) climatic conditions (solar

irradiation and cloud coverage) and (2)

environmental shade from nearby buildings and

trees, etc. Thus, it is necessary to distinguish

between declines due to such factors and decreases

due to a malfunctioning PV module.

Within this paper, malfunction refers to a range

of phenomenon affecting individual modules, from

faults that would require repair to normal

deteriorations in performance over time, as well as

reduced performance due to soil or dirt deposits.

Other causes of declines in output power include

temporary dust covering all modules, such as sand

deposits, and rises in temperature on a module in

summer. However, such factors are beyond the

scope of this paper, because they do not interfere

with the detection of a malfunctioning PV module

due to gradual decreases in output power from the

entire array of modules.

In the present study, we focus on distinguishing

malfunctions from factors (1) and (2) that are

observed on a daily basis with our monitoring

system.

3.2 Approach to Malfunction Detection

Figure 4 presents graphs for output powers from two

modules on the same day. The graph for module #19

indicates the presence of a malfunction, while the

graph for #21 indicates that output declines are due

to shadow from a utility pole. However, these graphs

also demonstrate how it is difficult to distinguish the

two reasons just from the graphs alone. Moreover,

the objective of this study is to detect as soon as

possible malfunctions at their early stages, which is

a more difficult challenge.

In order to correctly distinguish between them,

our method utilizes correlation information obtained

from simultaneously measuring power data from

each PV module. More specifically, there are two

types of correlation information, relating to spatial

and temporal information. The spatial correlation

information is obtained by comparing the output

power of each module at the same time. In this

paper, the spatial correlation information compares

output powers from the entire array of modules with

each individual module. The temporal correlation

information is obtained by plotting a time series

graph for output powers for each PV module and by

comparing the data in each graph.

By utilizing these two kinds of correlation

information, it is possible to distinguish whether a

decline is due to some malfunctioning of PV

modules or just due to environmental conditions,

such as shadow from buildings or trees. The cause is

illustrated with the following example.

Figure 4: Two examples of output power graphs reflecting

different reasons for the power decreases.

Figure 5: Obtaining a graph of maximum output power.

Figure 6: Graph of solar irradiation (same day as Figure

5).

Power (W)

Time (h)

Module #19

Module #21

Power (W)

Time (h)

Module

Envelope

#1 - #24

Irradiation (W/m

)

Time

(

)

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181

Figure 7: Graph of power output from module #19 and the

envelope.

Figure 8: Graph of power output from module #21 with

many small shadows and the envelope.

In our method, spatial correlation information is

represented as a graph plotting maximum power

levels from all the modules, and by comparing this

graph with similar power level graphs for each

individual module. A graph of maximum power

levels is created by superimposing all the graphs for

the individual modules over a day, and plotting the

envelope, as shown in Figure 5. The shape of the

maximum power output represents an approximation

of solar irradiation (Figure 6), which can be

recognized by comparing the plot shapes in Figures

5 and 6. The graph of the maximum power output is

not affected by shadow falling on some of the

modules, such as that from a utility pole or a house,

because the value is derived from the total area of

the complete PV module system.

Thus, the status of any power declines for an

individual module can be determined by comparing

the temporal graphs for maximum power and for

that particular module. The method is illustrated

with the example in Figure 4, where the power

output declines in the two graphs reflect different

causes. When there is a continuous difference

between two graphs, as shown in Figure 7, it would

indicate a continuous reduction in the power level

from an envelope, which would, in turn, indicate

some malfunction with the module. In contrast,

when two graphs plot similar values over some

periods, despite temporal fluctuations between them,

as shown in Figure 8, it indicates that the power

output fluctuations are due to a shadow passing over

the module.

The procedure for calculating this information is

outlined in the next section.

4 THE MALFUNCTION

DETECTION METHOD

4.1 Overview

The section presents an overview of the malfunction

detection method proposed in this paper. First,

monitoring data relating to output powers from each

module are converted into data adjusting for climate

and seasonal changes. In this paper, we call to this

conversion “normalization”. The normalized data

are converted so that maximum value from all

measured output powers from all modules at each

measurement time is assigned the value of 1. The

normalized data express the output powers relative

to all neighbouring modules based on the spatial

correlation information. Moreover, the time series

graph for each individual module shows the

temporal correlation information for each module.

Then, malfunction detection is conducted by

detecting the modules where the normalized data

values are continually at a reduced level.

4.2 Procedure

The method described in the previous section

consists of the following three steps. The procedure

is easily executed on a PC.

1) Creating maximum output power graphs for all

the modules.

First, time series graphs of all the modules for the

same day are overlaid. Next, an envelope from the

overlaid graphs is plotted (Figure 5). This envelope

represents the maximum output power graph for all

the modules.

2) Creating the normalized data.

The measured output powers are normalized so that

the total value of the envelope, representing the

maximum value, is always assigned a value of 1.

The normalized values are obtained by dividing each

output power by the envelope values for the

corresponding time point. The normalized value

represents an output power that is not influenced by

Power (W)

Time (h)

Envelope

Module #19

Power (W)

Time (h)

Envelope

Module #21

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changes in solar irradiation due to seasonal or

weather conditions. Thus, the modules that have

normalized values that are continuously lower than a

certain value can be detected as possibly

malfunctioning. An example of normalization is

presented in the next section.

3) Detecting modules with continuously reduced

normalized values.

Modules for which the normalized value is

continuously lower than 1 are detected as

malfunctioning. The method of detecting

malfunctioning modules is to plot a graph for each

module for the daily maximum normalized values,

and if a value is lower than some threshold level,

that would be detected.

Results from a detection experiment using this

method are presented in the next section.

5 EXPERIMENT SIMULATING

MALFUNCTION DETECTION

To confirm the effectiveness of the malfunction

detection method, we conducted an experiment that

simulated malfunction detection with actual data

from our PV module monitoring system. The

experiment used Showa-Shell CIS thin film 24 PV

modules that are installed within the monitoring

system. The goal of the experiment is to simulate a

malfunction by covering 10% of module #19 (Figure

9) and to detect the period of the simulated

malfunction. The power output from the covered

module decreased only by about 15%, so successful

detection of such a decline would indicate that the

method is capable of detecting malfunctions in the

early stages of malfunctioning.

Figure 7 shown in Section 3 presents a graph for

the power output from the 10% covered module #19.

The data are converted into normalized power values

for easier comparison of power reductions, as

described in Subsection 4.2. Figure 10 presents a

graph of the normalized power values converted

from the data in Figure 7. Graphs for normalized

power are made for all the modules on the days

monitored.

Figure 11 presents the maximum normalized

power output for one day during August 2012 from

the 10% covered module #19, and from module #21

which is a neighbouring module that is

representative of the normal modules. The maximum

normalized values on the graph are calculated from

the daily data for the interval from 1 pm to 5 pm,

because there is relatively little shadow during that

interval.

Figure 9: PV module #19 with covering of 10% total area.

Figure 10: Graph of the normalized power values for

module #19.

Figure 11: Experimental result.

Ten percent of the surface of module #19 was

continuously covered from August 16, and that

resulted in a continuous reduction in the level of

power output. Figure 11 shows that the maximum

normalized values were close to 1 prior to partial

coverage, but that once the cover was in place, the

maximum normalized values decreased to

approximately 0.85. This graph clearly indicates that

the level of power output from this module was

lower from the start of the experiment, and that this

simulated malfunction can be detected by setting a

detection threshold of 0.9 for the maximum

normalized power. This result confirms that our

Normalized Power

Time (h)

#19: 10% covered

Normalized Power

#19

#21

Date

1 5 10 15 20 25 30

Aug.

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method is capable of detecting an individual PV

module with a loss of approximately 15% in power

output. This result also shows that a module where

the level of power outputs begins to decline can be

detected as potentially malfunctioning.

6 DISCUSSION

In this experiment, we only utilized the spatial

correlation information in comparing the power

output with the maximum power for the entire

modules and the power levels for each individual

module. However, there are various kinds of spatial

correlation information, such as correlations

between neighbouring module groups or between

individual modules. In particular, modules within

the same string tend to be highly correlated because

they mutually influence voltages within the same

current. The attainment of better malfunction-

detection performance by utilizing more complex

forms of spatial correlation information will be

tackled in future research.

The present experiment focused on a malfunction

where the level of power output suddenly decreases,

but there are other forms of malfunctioning, such as

gradual decreases in output power. In those

situations, it is not possible to detect the malfunction

until a certain level of decrease is reached. But once

it has been reached, our method can detect such

malfunctions.

In the present paper, we have focused only on

power, which is the representative value of output

within the solar energy data. However, we may

expect even faster and more accurate malfunction

detection performance by using other values, such as

voltage, current, and temperature. We will continue

to consider the utilization of such values for the

detection of malfunctioning PV modules.

Our method can be applied to mega-scale solar

systems by changing the unit of measurement from

the single module to a string of modules. In the

future, we are planning to apply the method to a

mega-scale solar system.

7 CONCLUSIONS

In this study, we have developed a method of

malfunction detection using a monitoring system of

the individual PV modules. The method makes it

possible to detect malfunctions in their early stages

in terms of slight declines in the levels of power

output from a PV module, which it has been very

difficult to be aware of previously. We confirmed

the effectiveness of the method through a detection

experiment for a simulated malfunction using actual

data from our monitoring system for individual PV

modules. In the future, we plan to explore the

application of our method to mega-scale solar

systems.

ACKNOWLEDGEMENTS

We would like to thank Prof. F. Kano in Oyama

National College of Technology for valuable

discussions.

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