energies

Article
IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse
Farm in an Arid Region
Amor Hamied 1 , Adel Mellit 1 , Mohamed Benghanem 2, * and Sahbi Boubaker 3

1 Renewable Energy Laboratory, Faculty of Sciences and Technology, Departement of Electronics,

University of Jijel, Jijel 18000, Algeria
2 Department of Physics, Faculty of Science, Islamic University of Madinah, Madinah 42351, Saudi Arabia
3 Department of Computer and Network Engineering, College of Computer Science and Engineering,
University of Jeddah, Jeddah 21959, Saudi Arabia
* Correspondence: mbenghanem@iu.edu.sa

Abstract: In this paper, a low-cost monitoring system for an off-grid photovoltaic (PV) system,
installed at an isolated location (Sahara region, south of Algeria), is designed. The PV system is
used to supply a small-scale greenhouse farm. A simple and accurate fault diagnosis algorithm was
developed and integrated into a low-cost microcontroller for real time validation. The monitoring
system, including the fault diagnosis procedure, was evaluated under specific climate conditions.
The Internet of Things (IoT) technique is used to remotely monitor the data, such as PV currents,
PV voltages, solar irradiance, and cell temperature. A friendly web page was also developed to
visualize the data and check the state of the PV system remotely. The users could be notified about
the state of the PV system via phone SMS. Results showed that the system performs better under this
climate conditions and that it can supply the considered greenhouse farm. It was also shown that the
integrated algorithm is able to detect and identify some examined defects with a good accuracy. The
total cost of the designed IoT-based monitoring system is around 73 euros and its average energy
consumed per day is around 13.5 Wh.

Keywords: photovoltaic; monitoring system; fault diagnosis; internet of things

Citation: Hamied, A.; Mellit, A.;

Benghanem, M.; Boubaker, S.
IoT-Based Low-Cost Photovoltaic 1. Introduction
Monitoring for a Greenhouse Farm in Nowadays, as reported by the international energy agency (IEA), some isolated and
an Arid Region. Energies 2023, 16, rural areas are experiencing a large shortage in the supply of electric power [1]. Around
3860. https://doi.org/10.3390/ 770 million people are still living without access to electricity, particularly in Africa and
en16093860 Asia [1]. Sub-Saharan Africa’s share of the global population without access to electricity
Academic Editor: Jesús Polo has risen from 74% before the COVID-19 pandemic to 77% after [1]. Due to the increase in
energy demand, the challenge has shifted from saving classical fuel-based energy sources
Received: 19 March 2023 to creating and efficiently managing renewable energy sources mainly composed of solar
Revised: 26 April 2023
and wind.
Accepted: 28 April 2023
Starting a few years ago, investment in solar photovoltaic (PV) energy has become
Published: 30 April 2023
a common trend in developed and developing countries. This new orientation is mainly
empowered by a relative decrease in solar module cost. Thus, a large number of PV plants
were installed around the world. According to IEA [2], about 940 GW of PV were installed
Copyright: © 2023 by the authors.
at the end of 2021. The African Energy Outlook 2022 report estimates that between 2021
Licensee MDPI, Basel, Switzerland. and 2030, more than 40% of total capacity additions will come from solar PV [3].
This article is an open access article There are various applications of PV systems around the globe where PV sources may
distributed under the terms and provide appropriate solutions for remote sites without access to electricity. As this source
conditions of the Creative Commons of power is free and does not require hard maintenance, most sectors are attracted by the
Attribution (CC BY) license (https:// application of PV systems (e.g., telecommunication, water pumping, rural electrification,
creativecommons.org/licenses/by/ building, health, transportation, street lights, electric vehicles, agriculture, etc.). For the
4.0/). above-cited reasons, the current research work is devoted to investigate the performance of

Energies 2023, 16, 3860. https://doi.org/10.3390/en16093860 https://www.mdpi.com/journal/energies

Energies 2023, 16, 3860 2 of 21

a PV system used to supply a small-scale greenhouse farm in a remote site located in the
south of Algeria.
Monitoring PV installations in order to detect probable defects is a real challenge
that should be faced by PV systems designers and end-users. The main objective of a
monitoring PV system is to maintain a high level of reliability, effectiveness of operation
and availability of the system to provide electricity in the best conditions. The defects that
may occur in a PV installation may significantly decrease the power yield and may exhibit
a high risk of fire [4,5]. As per the reference [6], the annual energy loss due to defects in PV
systems is estimated to be around 18.9%.
From research and engineering design perspectives, various kinds of PV monitor-
ing systems have been designed, deployed and studied. Among the recent automatic
monitoring systems developed worldwide, the system developed in [7] was based on the
European Solar Test Installation sensor. Despite the advanced technologies and novelties of
the developed system, the high sampling period (8 min) is a big drawback. In addition,
the storage capacity, limited to 16,300 measurements (observations), may represent a chal-
lenging limitation. Another recent work dedicated to a universal data acquisition system
(DAQ) for PV performance monitoring is designed based on a microcontroller (68B09). The
collected data can be easily accessed through a server, which can help users to perform the
diagnosis and analysis of the PV system under various operating conditions [8]. In [9], the
authors developed another data logging system using a 12-bit precision Analog to Digital
Converter (ADC). Despite the improvements embedded to the designed monitoring system,
the number of acquired variables remains small, which may limit the deployment of the
device. However, this system has the advantage of not requiring the physical connection
of the monitored system to the data collection server [10]. In [11], the authors designed
an improved Data Acquisition (DAQ) system for which the number of variables to be
acquired has reached 20 analog inputs, which seems to be acceptable, particularly for
small-scale applications.
Recently, with advancements in the field of embedded microcontrollers and telecom-
munication technologies such as wireless sensor networks (WSNs), many researchers were
attracted by the application of the internet of things (IoT) to remotely monitor their PV
systems. For instance, the authors in [12] designed a monitoring system (IoT-DAS) for
grid-connected PV systems. Among the features of such a system, we can cite its ability
to identify non-ideal (faulty or degenerated) operating conditions. The obtained results
are reported to show compliance with the International Electrotechnical Commission (IEC)
standard. Moreover, the developed system is found to be efficient in monitoring all neces-
sary parameters with low power consumption and high accuracy. Additionally, a smart
solar still prototype for water desalination was designed using a remote monitoring system,
based on the IoT technique [13]. The monitoring system is developed and integrated
into the hybrid solar still in order to control its evolution online, as well the quality of
the freshwater.
A monitoring system for smart greenhouses using IoT and deep convolutional neural
networks has been designed [14]. The controlled parameters such as air temperature,
relative humidity, capacitive soil moisture, light intensity, and CO2 concentration were
measured and uploaded to a designed webpage using appropriate sensors with a low-cost
Wi-Fi module (NodeMCU V3). The same Wi-Fi module, NodeMCU V3 ESP8266, was used
in [15] to monitor PV parameters such as current, voltage, and other data (air temperature
and relative humidity). In [16], the authors also used the same Wi-Fi-module (NodeMCU
V3) to monitor data of a 3.6 kWp On-grid PV system. The hardware cost of the designed
prototype is affordable. A low-cost monitoring system based on the internet as a prototype
was designed to measure solar PV generation of an off-grid system. The system cost was
around 33 USD [17] and an html page was used to upload the measured data.
A wireless low-cost solution based on long-range (LoRa) technology was used to
develop a PV monitoring system applied to an installation of 5 kW [18]. It allows for the
correct display of electrical and meteorological data in real tim, while the main limitation is
Energies 2023, 16, 3860 3 of 21

its restricted duty cycle (1%). In [19], a new technique for fault diagnosis of PV systems
based on independent component analysis (ICA) is proposed. It can mainly diagnose
defects related to electrical failures. The system was evaluated based on simulation and
experimental data. A DAQ system based on open-access software and cloud service is
proposed in [20]. A comparative study against other IoT-based monitoring systems was
also presented and the result showed that this system could save energy up to 58%. In [21],
a novel strategy for monitoring a PV junction box based on LoRa for a PV residential
application is designed. The designed DAQ system is able to collect various parameters
and achieve excellent characteristics. The use of low-cost LoRa for designing an IoT-based
monitoring system was evaluated for a large-scale PV system in Istanbul [22]. The main
advantage of such a system is its low-cost. A Supervisory Control and Data Acquisition
(SCADA) system was also implemented through a DAQ. Extensive tests have shown this
system to have the lowest cost when applied for a PV plant with local data logging [23]. The
total cost of the designed monitoring system was around 761 USD, which is competitive
compared to the available SCADA systems. An intelligent monitoring system for automati-
cally monitoring PV plants was described and developed in [24]. To design this monitoring
system, the authors used software and cost-efficient hardware. Another option included in
this monitoring system is its ability to detect defective PV modules. For more details about
various configurations of data-acquisition systems based IoT, a good systematic review can
be found in [25].
A large share of future solar energy plants are going to be located in desert environ-
ments [26]. Dust build-up is the greatest technical challenge facing a viable desert solar
industry. Desertic regions (such as Sahara of Algeria) are more influenced by sandstorms,
and this has a negative impact of the PV plants installed in such regions. 60% energy yield
losses during and after sand storms are widely reported [27]. Various works were carried
out to study the performances of PV plants installed in similar regions [28–31]; however,
few works related to the development of smart PV monitoring systems were found in
the literature [32].
In one of our previous works [33], a DAQ system based on an Arduino board (a
low-cost microcontroller) and an ESP8266 Wi-Fi module, for PV parameters monitoring,
was developed. Although the designed DAQ system is inexpensive and can display the
collected data remotely via a website, some of the sensorsused are not sufficiently accurate,
such as the LM335 for temperature. In addition, this monitoring system is not able to detect
anomalies. To improve the system performance, in [34], we presented a similar work as
the one in [35], but in this work, the developed monitoring system is equipped with a
simple fault detection procedure. We also used more accurate sensors to measure solar
irradiance and cell temperature. The PV monitoring system was tested and evaluated at a
location in the north of Algeria (Jijel region) characterized by a Mediterranean climate. The
idea consists of integrating a fault detection algorithm inside a low-cost microcontroller
in order to detect faults in real-time. The system showed its ability to detect defective PV
modules with acceptable accuracy. The same monitoring system was tested and evaluated
in another location (Amiens, France, characterized by typical oceanic climate) with a little
improvement. In fact, we used the Matlab/Simulink environment with DSpace to examine
the accuracy of the designed monitoring system [35]. Three defects were studied and the
system showed a good ability to detect and identify the origin of the fault [35].
In Table 1 below, a summary of previous systems designed for monitoring PV solar
systems covering the period between 2018 and 2023 is provided. The focus of this compara-
tive study was mainly the location, used equipment/devices, cost, and power consumption.
Later in this paper, the performance of the system designed in this work will be provided
and compared to the systems provided in Table 1.
Energies 2023, 16, 3860 4 of 21

Table 1. IoT-based PV monitoring systems.

Power
System/Monitored The Used Platform/Type of Cost or
Ref./Year Consumed Region
Parameters Devices Network Complexity
Wh/Day
PV module Webpage locally
[33] € 75 North of
Air temperature, DC current, Arduino Mega hosted N/A
2018 Easy Algeria
DC voltage and light intensity Wi-Fi module 8266
Grid-connected PV
[18] Air temperature, DC current, - 39.26 EUR South of
Raspberry PI N/A
2019 DC voltage, solar irradiance LoRa easy Spain
and DC power
PV module
[36] ThingSpeak IoT Easy and North of
Current and voltage at the Arduino Uno N/A
2019 Wi-Fi module 8266 low-cost India
maximum power
PV module
[34] Air temperature, cell Arduino Mega ThingSpeak IoT 80 EUR North of
N/A
2020 temperature, DC current, DC Wi-Fi module 8266 Relatively easy Algeria
voltage and solar irradiance
Web visual
[37] Grid-connected PV interface in HTML East of
N/A N/A N/A
2020 DC power ZigBee module China
4G getway
PV module
[20] Air temperature, relative Blynk App North of
N/A 300 USD N/A
2021 humidity, dust density, wind NodMCU ESP8266 India
speed and solar irradiance
PV module
Low power
[22] Air temperature, DC current, North of
Arduino Nano LoRa and low cost 6.11
2022 DC voltage and Turkey
18.72 USD
solar irradiance
PV string
Low-cost
[38] Air temperature, intensity North pf
Arduino Mega NodMCU ESP8266 Relatively N/A
2023 light, DC current and Pakistan
complex
DC voltage

From the above Table 1 the following points can be highlighted:

- Wi-Fi module 8266 is the most used device to upload and visualize data into a platform
- The main collected data are DC current, DC voltage, module temperature, and so-
lar irradiance
- Most developed IoT-based monitoring systems are cost effective, where the cost ranges
between 39–300 USD.
- Arduino is the most employed microcontroller to develop the monitoring code.
- Based on the previous literature review, the research gaps can be summarized as follows:
- Most available IoT-based PV monitoring systems lack of fault detection and diagnosis
procedure and mainly used to only monitor data.
- Existing IoT-based monitoring systems are not evaluated under climatic conditions
characterized by severe sandstorms.
- The mostly used communication technology are the Wi-Fi, Zigbee, and GPS. Each of
them has a different performance in power consumption, distance covering, and cost.
The objective of the present study is to develop a low-cost IoT-based PV monitoring
system equipped with an effective fault diagnosis procedure. The system is evaluated
under specific climatic conditions (arid climate, Sahara of Algeria) with sandstorms. Addi-
tionally, other improvements are added to the system such as using suitable and low-cost
components. The PV system is used to supply a small-scale greenhouse farm installed in
Energies 2023, 16, 3860 5 of 21

this region. The greenhouse is considered as a load of the stand-alone PV system. Thus,
the novelty is to evaluate the developed system (PV monitoring with fault detection pro-
cedure) under an arid-region with specific climatic conditions. To the best of the authors’
knowledge, this kind of monitoring system was not evaluated under such arid areas.
The main contributions of this work are summarized as follows:
- Develop a low-cost, portable IoT-based PV monitoring system that can be easily
extended to other applications in control and PV systems characterization.
- Integrate a PV fault diagnosis procedure in order to detect failures that may occur in
the PV module.
- Study and verify the feasibility of providing electricity to a mini greenhouse farm
at isolated arid area (Sahara of Algeria) under high temperature in summer and
sandstorms phenomena.
The rest of this paper is organized as follows: Materials and methods are given in
Section 2, including PV system and greenhouse prototype description, as well as the
designed IoT-based monitoring system description. Results and discussion are provided in
Section 3. Concluding remarks and perspectives are reported in the final section.

2. Materials and Methods

2.1. Photovoltaic System Description
The considered stand-alone PV system is installed in a desert region of Algeria (Ouar-
gla city), which is characterized by an arid climate. The system consists of two photovoltaic
panels connected in parallel, a charge regulator and a battery (See Figure 1). The climate of
Ouargla is subtropical desert, with mild winters (during which, it can be cold at night)
Energies 2023, 16, x FOR PEER REVIEW and
6 of 23
very hot sunny summers. The PV system is designed to supply a small-scale greenhouse
farm (prototype).

ThePV
PVsystem
system under
under consideration
consideration and the considered location ◦
Figure 1.
Figure 1. The location (Ouargla
(Ouargla city:
city: 31.9527
31.9527° N,
N,
◦
5.3335 E).
E).
5.3335°

The PV
The PV module
module specifications
specifications and
and the
the corresponding
corresponding I-V
I-V curve
curve are
are shown
shown in
in Table
Table 22
and Figure 2, respectively.
and Figure 2, respectively.
Since the objective of the present study is to develop a PV monitoring system to
analyze
Table 2. PVthemodule
behavior of the PV module under different operating conditions, including
specifications.
different deficiencies, different kinds of faults were created intentionally. Figure 3 shows
Module type onsite of the created/investigated faults.
photos taken 100 P (36)
Maximal
The power 100 W
studied faults are, respectively, shading effect, short-circuited PV module, open-
circuited PV module, sand accumulated on PV modules, and covered
Tolerance ±3%PV module. As can
be seen in
Voltage this figure,
at Pmax (Vmp) PV modules are subject to sandstorms, which17.45 decrease
V their output
Current at Pmax (Imp) 5.73 A
Open-circuit voltage (Voc) 21.87 V
Short-circuit current (Isc) 5.98 A
 Energies 2023, 16, 3860 6 of 21
Figure 1. The PV system under consideration and the considered location (Ouargla city: 31.9527° N,
5.3335° E).
power significantly, in addition to the high temperature, which may reach 55 ◦ C in the
Theat
summer PVthemodule specifications and the corresponding I-V curve are shown in Table 2
study location.
and Figure 2, respectively.
Table 2. PV module specifications.
Table 2. PV module specifications.
Module type 100 P (36)
Module type 100 P (36)
Maximal power 100 W
Maximal power 100 W
Tolerance ±3%
Tolerance ±3%
Voltage at Pmax (Vmp) 17.45 V
Voltage at Pmax (Vmp) 17.45 V
Current at Pmax (Imp) 5.73 A
Current at Pmax (Imp) 5.73 A
Open-circuit voltage
Open-circuit voltage(Voc)
(Voc) 21.87 V 21.87 V
Short-circuit current (Isc)
Short-circuit current (Isc) 5.98 A 5.98 A

Energies 2023, 16, x FOR PEER REVIEW 7 of 23

open-circuited PV module, sand accumulated on PV modules, and covered PV module.

As can be seen in this figure, PV modules are subject to sandstorms, which decrease their
output (a) The
Figure 2.power
(a) The I-V curve
curve of
significantly,
I-V of the
theaddition
in PV module
PV module at Standard
to at
theStandard
high temperature, (STC)
which
Test Conditions may
(STC) (b) the
the used
(b)reach 55 °C
used PV
module.
PV module.
in the summer at the study location.

Since the objective of the present study is to develop a PV monitoring system to an-
alyze the behavior of the PV module under different operating conditions, including
different deficiencies, different kinds of faults were created intentionally. Figure 3 shows
photos taken onsite of the created/investigated faults.
The studied faults are, respectively, shading effect, short-circuited PV module,

Figure 3. Illustration of the investigated defects: (a) dirty PV module, (b) shading effect, (c) sand
Figure 3. Illustration of the investigated defects: (a) dirty PV module, (b) shading effect, (c) sand
accumulated on the surface, (d) open circuit, (e) short-circuit, and (f) covered PV module.
accumulated on the surface, (d) open circuit, (e) short-circuit, and (f) covered PV module.

2.2. Greenhouse System Description

Due to severe climatic conditions in the study location, keeping suitable environ-
mental conditions for plants is a big challenge. Plant watering and providing adequate air
conditioning need energy, which is not sufficiently available in such remote regions.
Energies 2023, 16, 3860 Figure 3. Illustration of the investigated defects: (a) dirty PV module, (b) shading effect, (c)7 of
sand
21
accumulated on the surface, (d) open circuit, (e) short-circuit, and (f) covered PV module.

2.2. Greenhouse System Description

2.2. Greenhouse System Description
Due to severe climatic conditions in the study location, keeping suitable environ-
mentalDue to severe climatic
conditions conditions
for plants in the study
is a big challenge. location,
Plant keeping
watering and suitable environmental
providing adequate air
conditioning need energy, which is not sufficiently available in such remoteairregions.
conditions for plants is a big challenge. Plant watering and providing adequate condi-
tioning need energy, which is not sufficiently available in such remote regions. Figure 4
Figure 4 shows a real photo of the designed greenhouse prototype. The greenhouse is
shows a real photo of the designed greenhouse prototype. The greenhouse is equipped
equipped with a watering system, light, and fans to keep adequate environmental con-
with a watering system, light, and fans to keep adequate environmental conditions inside
ditions inside the greenhouse. A simple controller is developed to monitor the opera-
the greenhouse. A simple controller is developed to monitor the operational parameters
tional parameters such as temperature, humidity, water, and lighting inside the green-
such as temperature, humidity, water, and lighting inside the greenhouse. For that purpose,
house. For that purpose, three sensors were used; namely, soil moisture, Light Depend-
three sensors were used; namely, soil moisture, Light Dependent Resistor (LDR), and
ent Resistor (LDR), and air temperature.
air temperature.

Figure 4.
Figure Photo of
4. Photo of the
the designed
designed greenhouse
greenhouse farm
farm (prototype)
(prototype)with
withsensors.
sensors.

To measure the temperature and the relative humidity, an AM2302 sensor (See Figure 4)
was used. Through this sensor, both the temperature and humidity can be measured
simultaneously. Cooling and heating are performed by using a Peltier cooling piece circuit
(Plate module 12706) (See Figure 4). Soil moisture is measured via the Soil Moisture Detector
Sensor (See Figure 4). Figure 5 shows the developed system during the testing phase. The
direction of the cooling or heating circuit is controlled by a 180◦ motor. It rotates in two
directions, according to the demand, through special electrical circuits (see illustration in
Figure 5, below).
Once the temperature is measured and compared to the reference temperature (Tref ,
stored into the microcontroller), Algorithm 1 is run to set a suitable temperature.

Algorithm 1: Setting a suitable temperature

Step #1: Measure air temperature (Tm )
Step #2: Compare the measured (Tm ) with the reference temperature (Tref ), ∆T = Tm -Tref
If not (−2 ◦ C < ∆T < 2 ◦ C) then
If T > 2 then Open relay #1, open heating system with a delay of 3 min
else open relay #2, open cooling system with a delay of 5 min
endif
endif
Step#3: Display the results

Once the instantaneous value of humidity is measured using the previously mentioned
sensor, it is possible to control the increase or decrease in the humidity through a similar
algorithm used for the control of temperature. When the humidity level is slightly increased,
the fan installed at the top of the greenhouse is turned on until it returns to the reference
percentage. A door could be also opened for fresh air. To measure the illumination intensity,
we used an LDR sensor. When the illumination value decreases, a LED light turns on
immediately. A watering pump is turned on based on the measured value of soil moisture.
 To measure the temperature and the relative humidity, an AM2302 sensor (See Fig-
ure 4) was used. Through this sensor, both the temperature and humidity can be meas-
Energies 2023, 16, 3860 ured simultaneously. Cooling and heating are performed by using a Peltier cooling 8piece of 21
circuit (Plate module 12706) (See Figure 4). Soil moisture is measured via the Soil Mois-
ture Detector Sensor (See Figure 4). Figure 5 shows the developed system during the
testing phase.
Currently, forThe direction
operating of the cooling
actuators, or heating circuit
the implemented is controlled
algorithms compare bythe
a 180° motor.
measured
Itvalue
rotates in two directions, according to the demand, through special electrical
with the reference value and make a decision. Table 3 shows the used components, circuits
(see
theirillustration in Figure
specifications, 5, below).
and cost. The total estimated cost is also provided in this table.

Fan

Window

Figure
Figure 5.
5. Illustration
Illustration of
of the
the whole
whole system
system during testing phase.
during testing phase.

TableOnce
3. Thethe temperature
used components,isspecification
measured and compared
and cost to thefarm).
(Greenhouse reference temperature (T ref,
stored into the microcontroller), the following algorithm is run to set a suitable temper-
Components ature. Specifications Cost (€)
Cooling and heating circuit Step #1:Peltier
Measure
Plateair temperature
Module (Tm)
12706 Thermoelectric Cooler 5
Half-cycle electric motor Step #2: Compare
Motor 180◦ 12 the
VDCmeasured (T m) with the reference temperature 10 (T ref), T =
Exhaust fan
Tm-Tref Fan 12 VDC 4
If not (−2 °C  T  2 °C) then
Linear drive Motor 12 VDC 5
If T  2 then Open relay #1, open heating system with a delay of 3
Aluminum angle tube min Tube 10*10*600 6
Total else open relay #2, open cooling system with a delay 30 of 5 min
endif
2.3. IoT-Based PVendif
Monitoring System Description
Step#3: Display the results
A block diagram of a general PV monitoring system based on IoT technique is shown
Once the instantaneous value of humidity is measured using the previously men-
in Figure 6 [32]. It consists of a PV array, sensors for measuring electrical and climatic
tioned sensor, it is possible to control the increase or decrease in the humidity through a
parameters (DC current, voltage, air temperature, and solar irradiance), a data-acquisition
similar algorithm used for the control of temperature. When the humidity level is slightly
unit based on a low-cost microcontroller (e.g., Arduino Mega), a combiner box, an inverter
increased, the fan installed at the top of the greenhouse is turned on until it returns to the
with other sensors (AC current and voltage), a Wi-Fi module (network), and display devices
reference percentage. A door could be also opened for fresh air. To measure the illumi-
(computer or phone) posting the collected data.
nationTheintensity, we usedWi-Fi
used ESP8266 an LDR sensor.
module is aWhen the illumination
self-contained SOC with value decreases,
integrated a LED
TCP/IP
protocol stack that can give any microcontroller access to a Wi-Fi network. The ESP8266 is
capable of either hosting an application or offloading all Wi-Fi networking functions from
another application processor.
 2.3. IoT-Based PV Monitoring System Description
A block diagram of a general PV monitoring system based on IoT technique is
shown in Figure 6 [32]. It consists of a PV array, sensors for measuring electrical and
climatic parameters (DC current, voltage, air temperature, and solar irradiance), a da-
ta-acquisition unit based on a low-cost microcontroller (e.g., Arduino Mega), a combiner
Energies 2023, 16, 3860 9 of 21
box, an inverter with other sensors (AC current and voltage), a Wi-Fi module (network),
and display devices (computer or phone) posting the collected data.

Blockdiagram
Figure6.6.Block
Figure diagramof
ofaaPV
PVmonitoring
monitoringsystem
system based
based on
on IoT
IoT technique.
technique.

Wi-Fi
The (based
used on IEEE
ESP8266 standard
Wi-Fi module802.11) is a mature networking
is a self-contained technologyTCP/IP
SOC with integrated and is
appropriate
protocol stackfor medium
that can givedistances (100 m—few kms)
any microcontroller accesswith
to amedium power consumption,
Wi-Fi network. The ESP8266
iswhile Zigbee
capable (basedhosting
of either on IEEEan standard 802.15.4)
application has low power
or offloading consumption
all Wi-Fi andfunctions
networking cost, but
it is suitable only for small distances
from another application processor. (up to 100 m). LoRa network is much appropriate for
largeWi-Fi
distances, up to 15 km, with low power consumption [32].
(based on IEEE standard 802.11) is a mature networking technology and is
The electronic
appropriate components
for medium of (100
distances the developed
m—few kms) monitoring systempower
with medium as wellconsumption,
as the cost of
each item are included in Table A1 (See Appendix A).
while Zigbee (based on IEEE standard 802.15.4) has low power consumption and cost,
To measure the PV current and voltage, an ACS712 sensor with a maximum current
but it is suitable only for small distances (up to 100 m). LoRa network is much appropri-
of 30 A, and a voltage sensor with a maximum voltage of 25 V are used. Both sensors are
ate for large distances, up to 15 km, with low power consumption [32].
calibrated using the following expressions:
The electronic components of the developed monitoring system as well as the cost of
each item are included in Table A1 (See Appendix 
Ir A).
To measure the PV current andAvoltage, = 5 an − 2.5 (1)
1024 ACS712 sensor with a maximum current
of 30 A, and a voltage sensor with a maximum voltage of 25 V are used. Both sensors are
where, Ir is the measured real value of current.
 
Vr
V= 5 (2)
1024

where, Vr is the measured real value of voltage, R1 and R2 series resistors (tension divider)
Solar irradiance was measured by using a reference solar cells and calibrated with a
pyranometer (the calibration coefficient is K = 1000), so

Gr = kVm (3)

where, Vm is the produced voltage by the reference solar cell

The estimated total cost is around 73 EUR. As compared to other monitoring systems
such as those, respectively, in [33–35], this cost can be considered as low with accept-
able performance.

2.4. Fault Detection Procedure

The developed fault detection and diagnosis procedure is summarized in Algorithm 2.
Thp and Thv were estimated empirically after several experiments. Additionally, the
value limits of Isc (0.45 A and 0.55 A) were estimated experimentally (based on several
tests). It should be noted that these parameters are related to this PV configuration. K1 and
K2 denote the used relays allowing the measurement of two physical parameters (Isc and
Voc). These later help the estimation of the nature of the defect, which may occur in the
PV module.
Energies 2023, 16, 3860 10 of 21

Algorithm 2: The developed fault detection and diagnosis procedure

Step #1: Read solar irradiance, cell temperature, Ipv, and Vpv
Step #2: Compare the measured power Pm= Ipv*Vpv with the one estimated based on one
diode model Pe, (∆P = Pm-Pe),
if ∆P > = Thp then move to step #3
else move to step #1.
endif
Step #3: Open relay K2 and measure the Vocm
Step #4: Compare the measured Vocm with the one calculated Voce,
if (∆Voc > Thv ) then
if ∆Voc = Thv then send SMS (Shading effect: dust or sand accumulate),
else send SMS (Short-circuited or all PV modules are disconnected)
endif
else
open relay K1 and measure Iscm
calculated ∆Isc = Iscm-Isce
if 0.45 < ∆Isc < 0.55 then send SMS (PV module disconnected)
else send SMS (short circuited)
endif
endif
Energies 2023, 16, x FOR PEER REVIEW 11 of 23
Figure 7a,b show the operation of the electronic circuits related to the two relays
during the measurement of Isc and Voc .

Figure 7.
Figure 7. (a)
(a)Electronic
Electroniccircuit
circuitfor
formeasuring
measuringIscIsc (relays
(relays position)
position) (b)(b) Electronic
Electronic circuit
circuit for for measur-
measuring
ing
V Voc (relays
oc (relays position).
position).

This procedure
3. Results was written and integrated into an Arduino Mega board for a real-
and Discussion
time application. The algorithms built into the circuit were designed through the Matlab
3.1. Experimental Results
program to determine the state of the system, normal or faulty, and then classify the type of
Figure 8a shows the designed PV monitoring system based on the IoT technology. It
the defect.
consists of voltage and current sensors, air temperature sensors, reference solar cell, a
DC-DC MPPT converter, a 16 × 4 LCD display for local results, and an electronic circuit
based mainly on an Arduino Mega2560 board and ESP8266 Wi-Fi module. Figure 8b de-
picts the PV modules used to test the monitoring system under normal and abnormal
Energies 2023, 16, 3860 11 of 21

3. Results and Discussion

3.1. Experimental Results
Figure 8a shows the designed PV monitoring system based on the IoT technology.
It consists of voltage and current sensors, air temperature sensors, reference solar cell, a
DC-DC MPPT converter, a 16 × 4 LCD display for local results, and an electronic 12
Energies 2023, 16, x FOR PEER REVIEW circuit
of 23
based mainly on an Arduino Mega2560 board and ESP8266 Wi-Fi module. Figure 8b depicts
the PV modules used to test the monitoring system under normal and abnormal conditions.

(a) The
Figure 8. (a)
Figure The developed
developed PV
PV monitoring
monitoring system
system based on the IoT technology and (b)
(b) the
the PV
PV
modules used
modules used to test the monitoring system.

In order
In ordertotodisplay
displaythe theresults
results online
online (measured
(measured data),
data), a webpage
a webpage waswas designed.
designed. For
example, Figure 9a shows the collected data, such as the PV current, PV voltage, air
For example, Figure 9a shows the collected data, such as the PV current, PV voltage, air
temperature, and
temperature, andsolar
solarirradiance
irradiance (morning
(morningat 8ato’clock, 3 December
8 o’clock, 2022). 2022).
3 December Figure Figure
9b shows
9b
the measured
shows data ofdata
the measured the greenhouse.
of the greenhouse.
Table 4 summarizes the power consumed by each used component of the monitoring
system. The power consumed by the designed IoT-based monitoring system is estimated
to be around 13.5 Wh/day.

Table 4. Power consumption of the used sensors and components.

Consumed Energy Consumed Energy per Day

Sensors/Component Current Drawn (mA) Time of Use
per Hour (Wh) Wh/day
Voltage sensor 8 10 h 0.048 0.48
Current sensor 10 10 h 0.050 0.50
Energies 2023, 16, 3860 12 of 21

Table 4. Cont.

Consumed Energy Consumed Energy per Day

Sensors/Component Current Drawn (mA) Time of Use
per Hour (Wh) Wh/day
Temperature sensor 2.5 10 h 0.085 0.85
Wi-Fi module ESP8266 80 10 s per min 0.25 3.75
Arduino Mega 79 10 h 0.45 4.50
Figure 8. (a) The developed PV monitoring system based on the IoT technology and (b) the PV
Solar irradiance - modules used to test the
- monitoring system. - -
GSM module sim800l 80 One per 10 h 0.50 0.50
In order to display the results online (measured data), a webpage was designed. For
LCD4 × 16 20 10 s per min 0.15 2.25
example, Figure 9a shows the collected data, such as the PV current, PV voltage, air
Relay 90 temperature, and solar
twice per 10 h (morning
irradiance 0.65 at 8 o’clock, 3 December
0.65 2022). Figure 9b
Total shows the measured data of the greenhouse. 13.48

Energies 2023, 16, x FOR PEER REVIEW 13 of 23

Figure 9. (a) Collected data of the PV system: Solar irradiance, air temperature, PV voltage and
Figure 9. (a) Collected data of the PV system: Solar irradiance, air temperature, PV voltage and PV
PV current. (b) Collected data of the greenhouse farm: Temperature, Humidity, soil moisture, solar
current. (b) Collected data of the greenhouse farm: Temperature, Humidity, soil moisture, solar
irradiance and
irradiance and water
water level.
level.

Figure 10 displays an example of the measured data (DC current and DC voltage) of a
Table 4 summarizes the power consumed by each used component of the monitor-
PV module for a short period of a configuration of three PV modules connected in parallel
ing system. The power consumed by the designed IoT-based monitoring system is esti-
by the developed monitoring system.
mated to be around 13.5 Wh/day.

Table 4. Power consumption of the used sensors and components.

Consumed Energy
Current Drawn Consumed Energy per
Sensors/Component Time of Use per Day
(mA) Hour (Wh)
Wh/day
Energies 2023, 16,
16, 3860
x FOR PEER REVIEW 14 of
13 of2123

Figure 10.
Figure 10. Measured
Measured DC
Dc current and DC voltage
voltage of
of aa PV
PV module.
module.

3.2.
3.2. Discussion
Discussion
Figure
Figure 11
11 reports
reports thethe collected
collected curves
curves under
under normal
normalandandabnormal
abnormaloperating
operatingcondi-
condi-
tions. To check the effectiveness of the developed PV data-acquisition system, we compared
tions. To check the effectiveness of the developed PV data-acquisition system, we com-
the measured (See Figure 11a) with the simulated under the Matlab environment (See
pared the measured (See Figure 11a) with the simulated under the Matlab environment
Figure 11b). As can be seen, a good agreement is obtained.
(See T 11.b). As can be seen, a good agreement is obtained.
To check the effectiveness of the designed system, faulty scenarios were created. As
To check the effectiveness of the designed system, faulty scenarios were created. As
shown in Figure 11b, the measurement intervals were divided into 9 time periods (Z1,
shown in Figure 11b, the measurement intervals were divided into 9 time periods (Z1, Z2,
Z2, . . . Z9) and each experiment lasted approximately 20 min. To test the circuit’s ability
…Z9) and each experiment lasted approximately 20 min. To test the circuit’s ability to
to detect the fault, each period was compared with the corresponding one extracted from
detect
the theobtained
result fault, each period
by the wasprogram.
Matlab compared with the corresponding one extracted from
the result obtainedinby
For example, the Matlab
region 2 (Z2), program.
we notice that an error occurred (anomaly in the output
For example, in region
power). The error was detected 2 (Z2),
basedwe
on notice that an detection
the following error occurred (anomaly
Algorithm 3. Thein the
idea
output power). The error was detected based on the following
consists of comparing the measured power with the estimated power. detection algorithm. The
idea consists of comparing the measured power with the estimated power.
∆P = Pmax_m − Pmax_e
Algorithm 3: The errors detection procedure
If ∆P > Thp then default = true
∆P = Pmax_m else−default
Pmax_e= false
If ∆P > Thp then default = true
endif
else default = false
Where Pmax_m is the measured power, Pmax_e is the estimated power based on an
endif
explicit model [39]. The threshold Thp  3 was estimated empirically throughout the ex-
periments.
Where Pmax_m is the measured power, Pmax_e is the estimated power based on
Then the
an explicit next[39].
model step The
aimsthreshold
to find theThp
fault
∼ type
= 3 wasbased on theempirically
estimated proposed procedure.
throughoutIn
thisexperiments.
the case, a single PV module is disconnected from the system. More details are listed in
TableThen5. the next step aims to find the fault type based on the proposed procedure. In this
case, a single PV module is disconnected from the system. More details are listed in Table 5.
For example, in zone Z6, after measuring the module temperature and solar radiation
values, G = 802 W/m2 , T = 20 ◦ C, it was expected that the maximum power value should
be 182 W. However, the value of the current and voltage in the MPP were 2 A and 14 V,
respectively, and the estimated power was 28 W. Thus, the threshold Thp = 182-28 = 154 W.
The fault detection algorithm detects an anomaly in the system, and by tracking the value
of Voc and Isc , it was estimated that the defect corresponds to a covered solar panel.
Energies 2023, 16, 3860 14 of 21
Energies 2023, 16, x FOR PEER REVIEW 15 of 23

Figure
Figure 11.11.Electrical
Electricaland
and changes
changes in
in the
the faulty
faultysystem.
system.(a)(a)
The curve
The extracted
curve from
extracted our our
from website. (b)
website.
The simulated curve under Matlab.
(b) The simulated curve under Matlab.
Energies 2023, 16, 3860 15 of 21

Table 5. The state of the PV system over the time periods.

Zone Time (min) System Status

Z1 0–20 It works normally
Z2 20–40 Defective system (a single PV module separated)
Z3 40–60 It works normally
Z4 60–80 Defective system (total separation of PV panels)
Z5 80–100 It works normally
Z6 100–120 Faulty system (a significant part of the PV panels is covered, despite the clear weather)
Z7 120–140 It works normally
Z8 140–160 Faulty system (sand deposit on the surface of the PV panels)
Z9 160–180 It works normally

Figure 12 shows other tests developed under IoT-ThingSpeak application in the same
region. As can be seen from 12:55 to 13:00, the system works normally without any
fault (stable DC voltage and DC current). In a very short period of 1 min, we observe a
remarkable decrease in solar irradiance, DC voltage, and DC current. This is not a fault,
rather, the reason is that the clouds moved. However, during the period from 13:03 to
13:06, we can clearly observe a decrease in DC voltage and DC current due to the artificially
covered PV module. In the period from 13:07 to 13:08, the system is also faulty, due17toofan
Energies 2023, 16, x FOR PEER REVIEW 23
accumulation of dust on the PV module. Then, when we removed the sand from the PV
module, the DC voltage and current increased again (time period 13:10).

Figure
Figure 12. Monitored data
data (air
(airtemperature,
temperature,solar
solarirradiance,
irradiance,DC
dc voltage,
voltage,and
andDC
dc current)
current) based
basedon
on
ThingSpeak application.

Once the fault is detected and the nature of the defect

Once defect estimated,
estimated, an
an SMS
SMS is
is sent
sent to
to
notify
notify the user about the state of the system using a SIM8001 module (See Figure 13).
system using a SIM8001 module (See Figure 13).
Energies 2023, 16, 3860 16 of 21
circuit 2PV module, short circuit, and other faults).
gated faults are reported clearly on the website (Faults: open circuit 1 PV module, open
PV system, indicating the type of the defect online. As shown in Figure 14, all investi-
tion on the website. Additionally, the designed webpage is able to display the state of the
check the state of the PV system remotely. As an example, Figure 14 shows the notifica-

of the PV system.
Figure 13. Notifications of faults: Sending SMS messages to notify the user by phone about the state

notify the user about the state of the system using a SIM8001 module (See Figure 13).

ThingSpeak application.
Figure 12. Monitored data (air temperature, solar irradiance, dc voltage, and dc current) based on
The designed system is equipped with an interactive webpage. This can help users

Once the fault is detected and the nature of the defect estimated, an SMS is sent to

Figure 13. Notifications of faults: Sending SMS messages to notify the user by phone about the state
of the PV system.

The designed system is equipped with an interactive webpage. This can help users
check the state of the PV system remotely. As an example, Figure 14 shows the notification
on the website. Additionally, the designed webpage is able to display the state of the PV
system, indicating the type of the defect online. As shown in Figure 14, all investigated
faults are reported clearly on the website (Faults: open circuit 1 PV module, open circuit
2PV module, short circuit, and other faults).
Our IoT-based monitoring system is equipped with a fault detection procedure and
can notify users about the system. In other presented systems, this option is not available.
This is the main difference between our study and those published that are only used to
monitor data.

3.3. Advantages and Limits of the Designed IoT-Based Monitoring System

Some advantages and limits of the proposed monitoring system are listed in Table 6.

Table 6. Advantages and limits of the proposed IoT-based monitoring system.

Advantages Limits

X Low cost and lower power monitoring system X The system was tested and evaluated for a
X Easy to implement small-scale PV system
X Interactive webpage can help users monitor their system remotely X Security of the collected data
X The integrated code can be reprogramed and updated at any time X Limited distance of the used Wi-Fi module
X Other types of defects could be easily integrated into the microcontroller X The fault diagnosis procedure is developed for
X Users can be notified by an SMS regarding the state of their PV system only three types of faults
X The used Wi-Fi module ESP8266 module is an extremely X The system is not able to detect multiple faults
cost-effective board

The benefits of the PV monitoring system-based IoT technique compared to classical

monitoring systems are: (1) cost effective, as we use a low-cost Wi-Fi module, (2) higher
productivity and efficiency, is easily realized, and increases mobility.
Energies 2023, 16, 3860 17 of 21
Energies 2023, 16, x FOR PEER REVIEW 18 of 23

Figure 14. Notification ofNotification

Figure 14. the state onofthe
thePV system
state on thedisplayed
PV systemondisplayed
the website.
on the website.
Energies 2023, 16, 3860 18 of 21

The major drawback of the IoT is to ensure the security of application in its large
database. In addition, a non-smart IoT system will have limited capability and will be
unable to evolve with big data. No security protocol is associated with the system to secure
the uploaded data on the website. Another limit is the short distance of the used Wi-Fi
module. A cost-effective embedded solution including IoT and fault detection techniques
seems to be an important technology that should be further improved for large scale
photovoltaic applications.
IoT technology will continue to play a major role in increasing the quality of the
monitoring and diagnosis of PV plants installed in remote locations. This can help users to
check their PV systems online, predict possible faults, visualize the evolution of different
parameters, and analyze the data [32].

4. Conclusions and Perspectives

In this paper, a low-cost PV monitoring system with a fault detection procedure was
designed. The system was simulated and verified experimentally in a specific desertic
region with a hot climatic condition and sandstorms (south of Algeria). The obtained results
show the designed system to be effective, particularly in its data-acquisition component and
real-time monitoring, specifically in fault detection and isolation. To collect data remotely, a
webpage was developed and activated. The investigated types of defects were, respectively,
disconnection of one or more panels, sand accumulated on a PV module, and short circuit
in a PV module. The IoT is a good platform for the development of a cost-effective smart
monitoring system, assuming the final application successfully complies with the relevant
technical standards. The developed system can help O&M make correct decisions about
the cleaning or changing of the PV modules.
This system was used to supply a small greenhouse farm (prototype) with consistent
environmental conditions. Experimentation investigation showed the capability of this
system to feed the used components by the greenhouse prototype, such as sensors, fans,
lamps, and a water pump.
IoT technology is strongly recommended for designing smart monitoring systems
with fault detection techniques for PV plants installed in desert regions. Furthermore, a
smart scheme is highly recommended for the fast isolation and immediate protection of
the plants.
The main limits of the developed PV monitoring system are: (1) it is suitable only for
very small-scale off-grid PV systems (PV string), (2) the Wi-Fi module used is limited in
terms of distance (up to 100 m), (3) only three major faults can be detected, and (4) the
uploaded data on the developed webpage are not secured.
To address the above issues, we plan to test the system for a large PV array by using
other suitable sensors (current and voltage). Additionally, other type of defects related to
the PV modules will be investigated, such as browning, bubbles, snail trails, and others.
We will use another long-range data transfer technology, such as LoRa, as LoRaWAn works
on a lower radio frequency band than Wi-Fi.

Author Contributions: Conceptualization, A.H. and A.M.; methodology, M.B. and S.B.; software,
A.H.; validation, A.H., A.M., M.B. and S.B.; writing—original draft preparation, M.B. and S.B.;
writing—review and editing, A.M.; visualization, A.H.; supervision, M.B. and S.B. All authors have
read and agreed to the published version of the manuscript.
Funding: Deanship of Scientific Research at the Islamic University of Madinah for the support
provided to the Post-Publishing Program.
Data Availability Statement: Not applicable.
Acknowledgments: The researchers wish to extend their sincere gratitude to the Deanship of Scientific
Research at the Islamic University of Madinah for the support provided to the Post-Publishing Program.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2023, 16, 3860 19 of 21

Appendix A

Table A1. The used components, specification and cost.

Components Specifications Cost (€)

• Supply Voltage: 5 Vdc Nominal.
• Measurement Range: −30 to +30 Amps.
• Voltage at 0 A: VCC/2 (nominally 2.5 VDC).
Current sensor • Scale Factor: 66 mV per Amp.
5
(ACS 711) • 5 µs output rise time in response to step input current.
• 80 kHz bandwidth.
• Total output error 1.5% at TA = 25 ◦ C.
• 1.2 mΩ internal conductor resistance.

• Voltage input range: DC 0–25 V

• Voltage detection range: DC 0.02440–25 V
Voltage sensor 3
• Output signal type: Analog
• Voltage Analog Resolution: 0.00489 V.

• Power supply 3.3–5 V

Temperature sensor • Response time of less than 0.5 s
3
(AM2302) • Measurement range—40 ◦ C to 80 ◦ C
• Accuracy ±0.5 ◦ C

• Solar cell: Monocrystalline Silicon (50 mm × 33 mm)

• Operating temperature: −35 ◦ C to 80 ◦ C
• Electrical connection via shielded cable, length 3 m standard, or IP 67 rated connector
Reference solar cell
• Case, protection mode: Powder-coated aluminum, IP 65 15
Si-V-1.5TC
• Dimension, Weight: 155 mm × 85 mm × 39 mm, Approximately 350–470 g
• Irradiance ±5 W/sqm ±2.5% from value; with temperature compensation, vertical
light beam, and AM 1.5

• Program Memory Size (KB) 256

• CPU Speed (MIPS/DMIPS) 16
• Data EEPROM (bytes) 4096
• Timers 2 × 8-bit—4 × 16-bit
• Stand alone PWM 15
• Number of ADCs 0
• Diff ADC Inputs 14
Microcontroller
• ADC Channels 16 12
Atmega2560
• Max ADC Resolution (bits) 10
• Number of Comparators 1
• Temp. Range Min. −40
• Temp. Range Max. 85
• Operation Voltage Max.(V) 5.5
• Operation Voltage Min.(V) 1.8
• I2C 1–I2C

• Operating Voltage 3.0~3.6 V.

• Operating Current Average value: 80 mA.
• Operating Temperature Range −40◦ ~125◦ .
Wi-Fi module
• Wi-Fi Protocols 802.11 b/g/n. 6
Esp8266
• Frequency Range 2.4–2.5 G (2400–2483.5 M).
• Types of Antenna PCB Trace, External, IPEX Connector, Ceramic Chip
• Distance 20 m.
Energies 2023, 16, 3860 20 of 21

Table A1. Cont.

Components Specifications Cost (€)

• GSM: 850, 900, 1800 and 1900 MHz.
• FLASH: 16 Mbit.
• RAM: 32 Mbit.
GSM Module sim800l • Power supply 3.4~4.4 V. 10
• Power saving. Typical power consumption in sleep mode is 1.04 mA.
• Quad-band: GSM 850, EGSM 900, DCS 1800, PCS 1900. Frequency bands: can search
the four frequency bands automatically.
Relay Maxtor (30 A;12 V), Module 4 relay 5 V, 10 A 7
LCD LCD16 × 4 6
Electronics components Diode, resistor, capacitor, transistor 5
Total 73

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