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Artículo Científico / Scientific Paper |
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https://doi.org/10.17163/ings.n28.2022.04 |
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pISSN: 1390-650X / eISSN: 1390-860X |
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DESIGN AND VALIDATION OF IoT
MEASUREMENT SYSTEM FOR PHOTOVOLTAIC GENERATION |
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DISEÑO IoT Y VALIDACIÓN DE SISTEMA DE MEDIDA PARA GENERACIÓN FOTOVOLTAICA |
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Thiago
Angelino dos Santos1,* Diego Lima Carvalho Gonçalves 1 |
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Received: 14-05-2022, Received after review:
13-06-2022, Accepted: 18-06-2022, Published: 01-07-2022 |
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Abstract |
Resumen |
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Use photovoltaic
(PV) systems for electricity generation is constantly growing in Brazil. With
the reduction in the price of PV modules and the implementation of the
electric power compensation system by the power distributor, the consumer is
investing in PV microgeneration to reduce the power bill. This article aims
to develop an embedded system in the context of the Internet of Things (IoT). Having an IoT monitoring
system applied to a grid-connected PV system in an educational institution
helps teach IoT and PV generation concepts. The
system is based on the ESP32 development board for acquiring DC voltage and
current generated by a 1.35 kWp photovoltaic system
connected to the grid and installed at the IFCE. This proposal offers a
low-cost educational solution using open source and programmable hardware,
which sends the data to a database in the cloud, allowing remote access
worldwide. Then, using the data analysis methodology, it was possible to
validate the values measured with the inverter installed with an error of
less than 1% for the voltage and current acquired during one day. With this
result, it is concluded that the designed IoT
system can be used for measurement in PV systems. |
El uso de sistemas fotovoltaicos (FV) para la generación de electricidad está en constante crecimiento en Brasil. Con la reducción del precio de los módulos FV la implementación del sistema de compensación de energía eléctrica por parte del distribuidor de energía, el consumidor está invirtiendo en microgeneración FV para reducir la factura de energía. El objetivo del presente artículo es desarrollar un sistema embebido en el contexto de Internet de las cosas (IoT). Tener un sistema de monitoreo IoT aplicado a un sistema FV conectado a la red en una institución educativa ayuda a enseñar conceptos tanto de IoT como de generación FV. El sistema se basa en la placa de desarrollo ESP32 para la adquisición de tensión y corriente continua generada por un sistema FV de 1,35 kWp conectado a la red e instalado en el IFCE. Esta propuesta ofrece una solución educativa de bajo costo, utilizando código abierto y hardware programable, que envían los datos a una base de datos en la nube, lo que permite el acceso remoto en todo el mundo. Utilizando metodología de análisis de datos, fue posible validar los valores medidos con el inversor instalado con un error inferior al 1% para la tensión y la corriente adquiridas durante un día. Con este resultado se concluye que el sistema IoT diseñado puede ser utilizado para la medición en sistemas FV. |
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Keywords: ESP32, IoT,
measurement, photovoltaic, energy, generation |
Palabras clave: ESP32, IoT, medida, fotovoltaica, energía, generación |
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1,* Academic Master’s Degree in
Renewable Energy (PPGER). Federal Institute of Ceará
(IFCE). Maracanaú Campus, Ceará,
Brazil. Corresponding author ✉: thiagoangelinos@gmail.com 2
Research Group in Electrical Technologies for Sustainable and Renewable
Energy. Department of Electrical Engineering, University of Cadiz (UCA). Escuela Politécnica Superior de
Algeciras, Cádiz, Spain Suggested citation: Dos Santos, T. A.; De Freitas, F. G.; Carvalho Gonçalves, D. L. and Fernández-Ramírez, L. M. “Design and validation of IoT measurement system for photovoltaic generation”. Ingenius, Revista de Ciencia y Tecnología. N.◦ 28, (july-december). pp. 44-52. 2022. doi: https://doi.org/10.17163/ings.n28.2022.04.
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1.
Introductión The first modern solar cell,
measuring just two square centimeters in area, was introduced in 1954 with 6%
efficiency and five mW of power, as described in
[1]. A significant advance in the development of the PV market, identified in
[2], was observed from the increase in Chinese production. For the eighth
consecutive year, Asia eclipsed all other regions for new installations,
accounting for nearly 58% of global additions; even excluding China, Asia was
responsible for around 23% of new capacity in 2020. Asia was followed by the
Americas (18%), which moved ahead of Europe (16%). China continued to
dominate the global market (and solar PV manufacturing), with a share of
nearly 35% (up from 27% in 2019). In 2021 the estimated global capacity was
760 Gigawatts, as shown in [3]. In addition to reducing
the cost of PV modules, distributed generation in Brazil has become an
attractive investment in solar energy. Currently, the consumer can generate
electricity and use the compensation system to reduce the cost of energy
consumed directly on the bill. Compensation allows the energy exceeding
consumption for that month to be used within a maximum period of up to five
years. The conditions for connection to the conventional electricity
distribution structure were established on April 17, 2012, by the National
Electric Energy Agency [4]. Knowing how much the
PV system will generate per month is one of the first concerns of the final
consumer. The engineer designs the system, but external factors such as dirt,
system failures, material wear, and weather conditions can alter the energy
generation estimated in the original design. With a monitoring system applied
to the PV generation system, it can monitor the production and consumption of
electricity. This way, it is possible to identify non-standard behaviors for
that system: the faster this identification, the minor damage to the final
consumer. Regarding data
monitoring via the Internet, the number of devices connected to the cloud is
increasing, and, consequently, the volume of data has grown substantially. Our
daily lives are surrounded by constantly updated information. When a status
on a social network is changed, there is an information feed, which generates
updates to the user’s database. This dynamic way to get information quickly,
accessible, and up-to-date does not just apply to social media or journalism.
Considered the fourth industrial revolution, Industry 4.0 has been gaining
prominence and promises to have a more profound and exponential impact than
previous industrial revolutions. One of the industry’s pillars 4.0, according
to Vitalli [5] is the Internet of Things (IoT, English Internet of Things). Among the devices
available on the market for IoT applications are
the ESP32 and ESP-WROOM (model used in this work), which are constantly used
in academic research because of their easy programming. |
IoT systems are applied to facilitate
communication between equipment and human beings in various areas, such as
hospitals [6], manufacturing processes [7], waste management [8], as well as
renewable energies [9] - [10]. The ESP32
development platform has been used in IoT projects
around the world. Then, to enable multimedia data transmission via Wi-Fi,
this device was used to compose a hybrid communication system and data
transmission system in IoT networks [11]. In
addition to Wi-Fi communication, the ESP32 also features Bluetooth
communication. A vehicle window control system was developed in [12] using
Bluetooth communication. ESP32 was also used
in a data center’s temperature, humidity, and air quality monitoring network
to automate the activation and deactivation of the cooling, ventilation, and
air filtration system [13]. 1.1.
Related Works The ESP32 and ESP8266 were used to
build an IoT network to measure weather data and
the temperature of PV modules in [14]. The communication used between the
ESP32 and the ESP8266 was Wi-Fi. A comparative
analysis and practical application of the ESP32 microcontroller module for IoT was illustrated in [15]. The article demonstrated
that ESP32 is an excellent option for IoT systems,
as it presents advantages in performance and price compared to the others
analyzed. Its performance reflects its reliability, ensuring the system is
always up and running. Thus, they can be used in critical systems such as the
one proposed in [16] for monitoring liquefied petroleum gas (LPG) leakage. In PV systems, ESP32
was used in a water pumping control system powered by a solar generator [17].
A web server using ESP32 was developed in [18] to monitor and collect data
from a PV system. Data was stored in a text file and saved directly to the SD
memory card. The data can be retrieved, and the text file downloaded onto a
web page. It was possible to
verify the real behavior of the PV modules using low-cost components, as can be
seen in the tracking system of IV (Current-Voltage) and PV (Power-Voltage)
curves built-in [19]. It is also possible to monitor the PV system using lowcost equipment [20] - [21]. This work proposed the
development and validation of IoT system didactic with
programmable and open-source hardware, aiming at greater flexibility in data
collection and submission to a database. Validation was done from a
commercial inverter with IoT technology. Just as
the software was used in [22] to support teaching, the system designed in
this research can be used in the classroom to teach embedded systems, the
internet of things, or renewable energy as an example of a didactic
monitoring system. Some articles that used monitoring systems, applied or not
to PV generation, were gathered in Table 1. |
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The use of internet
connection through different devices for data communication, processing, and
sending was observed. Most of the articles presented (67%) did not use a
validation system for the data collected, especially in systems for measuring
electrical variables (voltage and current, for example), as in the case of
this work, showing the contribution of this paper in this area. Table 1. Comparison of monitoring articles similar to this
Alves et al. [27]
analyzed a situation using Didactic Training Engineering (DTE) and found that
this structure facilitates didactic mediation and learning. The proposal
system can be used for DTE in renewable energy, programming, or embedded
system. Another application for this project is in Professional Didactics
(PD). Alves [28] accentuated the use of technology to provide an
understanding of the notions discussed in the class. Similarly, the teachers
could iterate with the students using the proposed system in this work. This research aims
to design, develop and validate a didactic IoT
system for monitoring the voltage and current generated by the PV modules.
The focus of this research is to develop a didactic system, easy to reproduce
to disseminate knowledge in this area of research, facilitating the
acquisition of data in photovoltaic solar generation plants. Low-cost sensors
for measuring PV system current and voltage are applied. The data obtained
were compared with the data collected by the installed PV inverter, checking
the error between the systems to validate the developed system. The system developed
in this research can be applied to verify the real power generated for PV
plants in [29], for example. 2.
Materials and
methods 2.1.
Problem and
Methodology This section presents the project
development stages, exposing the materials and methods used. The proposed
system can be divided into 5 (five) parts: 1. Embedded system with Wi- Fi
connection 2. Cloud data storage 3. Sensing 4.
Data provided by the inverter 5. Embedded system programming. |
2.1.1.
Embedded
system with Wi-Fi connection Several low-cost devices can
provide internet connection and perform pre-programmed actions. With these
devices, it is possible to transform a local data acquisition system into an IoT system that constantly feeds a cloud database. The Raspberry Pi
family of devices, developed in the UK by the Raspberry Pi Foundation, has
hardware built into a single card and card slot memory, USB interface, HDMI,
input/output pins, serial interface, and built-in Wi-Fi modem [30]. These
devices can be easily integrated into an IoT
network. A Raspberry was used in [31] to monitor current and voltage in a PV
pumping plant. Some devices from
the Arduino platform [32], such as the ARDUINO UNO Wi-Fi REV2, are specially
designed for IoT applications. These devices have a
user-friendly programming platform (Arduino IDE) in Other devices widely
used due to their low cost are the microcontrollers manufactured by Espressif [33]. These controllers, like the ESP32, allow
microcontrollers to connect to a wireless network. The manufacturer provides
some hardware versions for use as needed. A comparison between
ESP32 and a previous version of Espressif IoT modules (2014) ESP8266 is shown in Table 2. Table 2. Comparison between ESP32 and ESP8266
Note that ESP32 has
more excellent processing and storage power compared to ESP8266. So, in this
research, ESP32 was used for the proposed monitoring system in order to
connect the monitoring system the Internet. 2.1.2.
Cloud data storage Some solutions
available on the market are AWS IoT Services
(Amazon Web Services), CloudMQTT, and Ubidots. AWS IoT is a
specialized service, from the edge |
CloudMQTT is a service that aims to
facilitate sending messages through the MQTT protocol between devices in an IoT system. 24/7 support offers free connection for five
users at a speed of 10 Kbit/s [35]. Ubidots is a platform that allows it to
connect hardware and/or digital data services to the cloud with its
easy-to-integrate API. It has an editable platform for the project’s needs
and a free mobile application. It has an educational license with the right
to connect up to twenty devices with up to ten sensors each [36]. This work, however,
proposes the creation of a system like the one used in [7]. But without the
use of local storage on an SD card. All data is sent to a cloud server for
comparison with data provided by the inverter installed in the PV system. ThingSpeak™ [37] is a free and configurable
analytics platform service often used for prototyping IoT
systems that allow you to aggregate, visualize and analyze real-time data
streams with cloud storage. ThingSpeak provides
instant views of data posted by its devices in ThingSpeak’s
database through a web platform made available to users. Therefore, this work
used the ThingSpeak platform for data storage in
the cloud. 2.1.3.
Sensing For power analysis, two variables
are essential: voltage and current. The PV system used in this work comprises
a set (string) of five PV modules in series, totaling 1.35 kWp. Some technical characteristics of the Jinkosolar PV modules (2019) used can be seen in Table 3. Table 3. Electrical characteristics
of the PV module used in this work
The voltage
measurement of the modules was made at the DC input of the inverter. The
open-circuit voltage, the highest voltage supplied by the system, can be
calculated by Equation (1):
The
voltage difference generated by the string in the PV panel (VFV)
is measured by the voltage divider. Knowing that the analog input ESP32 is up
to 3.3 V (VOUT), we can calculate the resistors for voltage
divider using Equation (2):
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Figure
1.
Schematic of the voltage divider used in the system for reading PV voltage The Vout is connected directly to the ESP32
pin to measure a voltage proportional to the voltage of the PV module’s
string. The ratio between them is VPV = 58.79 ∗ Vout. The current
measurement is made with the 20A ACS712 current sensor module. This module is
shown in Figure 2, highlighting the pin connections. The arrangement chosen
for the sensor is between the inverter’s DC input and the circuit-breaker box
so that the measurement is made parallel to the inverter and as close as
possible. Therefore, the proposed project’s sensing system measures the power
that the PV string provides, comparing it with that registered by the
inverter for system validation.
Figure
2.
Installation schematic of the 20 A ACS712 current sensor module This
sensor has a 66 mV/A proportional analog output that can be read from the
module output pin indicated by number four (4) in Figure 2. The operating
voltage of the
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module is 5VDC (5). Pins one (1) and two (2) correspond to the
inverter’s DC input and the string box circuit breaker. Pin three (3)
corresponds to the source’s zero references (gnd)
that supply the sensor. 2.1.4.
Data provided
by the inverter The inverter used for comparing and
validating the system with hardware in this work is model PHB1500-NS from the
manufacturer PHB (2020). This equipment has a Wi-Fi monitoring system with
real-time data available to users on the manufacturer’s page via login and
password. Some characteristics of this inverter grid-tie are shown in Table
4. Table 4. PHB1500-NS inversor characteristics Note that this model
has, in addition to interfaces for operation configuration, a Wi-Fi interface
used in the monitoring system, which sends the collected data to the
manufacturer’s server. The manufacturer provides a page web for accessing the
generation data collected by the inverter. With the system
proposed in this work, it is possible to program the protocol of how data is
collected and sent to the cloud, enabling integration of this data with the
user’s preferred server. In this way, the user can program the time interval
he/she wants and send this data to any server, for example, to research fault
detection with intelligent algorithms or any research where data acquisition
is necessary. 2.1.5.
Embedded
system programming The use of programmable hardware
(ESP32) allows the choice of this time according to the user’s need and the server
for sending the data, enabling future research. The firmware developed and
recorded in ESP32 consists of a routine to send the average of the
measurements every minute. It is shown in the diagram in Figure 3. It can be seen from
the flowchart in Figure 3 that the program starts by connecting the device to
the internet via Wi-Fi to access the NTP server where they have access to
local time and later sends the |
average value of the measurements
to a server in the cloud, which in this case is the Thing Speak, every one
minute. The use of ESP32
also makes it possible to report information about the generation and
eventual failures in real-time in a customized way. However, this work focuses
on validating the system through comparison with the inverter data, leaving
this functionality for future work.
Figure 3. Flowchart of source code
(firmware) 3.
Results and
discussion In order to ensure the correct measurement of electrical variables,
voltage, and current, tests were carried out to calibrate the sensors with
the ESP32 with a digital multimeter, as seen in
Figure 4. The current sensor
and the divider voltages generate voltages proportional to the current and
voltage value of the string PV, respectively. The tests aim to calibrate the
current sensor and voltage divider with resistors to ensure the correct
proportionality between the value sent to the ESP32 and the value of current
and voltage generated by the string PV. Once calibrated, this data is
compared with the voltage and current values read and stored by the
commercial Inverter of the PV system. The assembly
recorded in Figure 4 shows the current sensor (3) in series with a multimeter (5) between the DC input (2) of the Inverter
(1) and the protection box (4), containing circuit breakers and main switch.
The current, generated by the string PV, passes through the protective box
and is read by the sensor, which, in turn, sends a voltage proportional to
the current to one of the analog input ports of the |
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ESP-WROOM-32
development board (6). It will be installed in a protection frame fixed to
the wall (7) to house the system developed in this work. This assembly
consists of an initial prototype for laboratory testing. Subsequently, a
plate was assembled to extend the ESP-WROOM-32 connections to the voltage
divider and current sensor, as seen in Figure 5.
Figure
4.
Installation schematic of the 20 A ACS712 current sensor module This extension board (Figure 5
(b)) was developed to connect the voltage divider to acquire the voltage
string PV and the current sensor to acquire the current generated by the
string PV to the ESP32. The power to the board comes from an external 5V
source (Figure 5 (a)) connected to the ESP-WROOM-32 development board. The voltage
divider and the current sensor were installed inside the protection box and
the main switch presented in item 4 of Figure 4 and connected to the
connection board with ESP32 via network cable (Figure 5 (b)). With the low-cost IoT system developed in this work, it is possible to obtain current and voltage data generated by the string of five PV modules in series for comparison with the data sent to the cloud by the Inverter installed in the PV plant. The comparative |
graph between the current values
acquired by the Inverter and the IoT system
developed in this work can be seen in Figure 6.
Figure 5. ESP32 and extender plate
Figure 6. Comparison of
current between the inverter and the proposed system Similarly to the current, the voltage
comparison can be seen in Figure 7.
Figure 7. Comparison of
voltage between the inverter and the proposed system From the data collection by the
Inverter and the low-cost system, in addition to generating the graphs shown
in Figure 6 and Figure 7, the error was calculated by the sum of a defined
amount of data collected by each system in the same time interval. A percent
error throughout the day of less than 1% for current and voltage was
obtained. On the day shown in the graphs in Figure 6 and Figure 7, an error
of 0.26% for voltage and 0.56% for the current was
observed.
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4.
Conclusions IoT monitoring systems applied to PV
microgeneration are being increasingly researched and applied, and their
development is needed as PV systems continue to become a viable form of
electricity generation. Modern PV inverters have IoT
technology to send generation data to the manufacturer’s server. However, with
the system proposed in this work, it is possible to configure how this
information is collected and where it is published in the cloud, generating
flexibility in data collection for future research. A low-cost didactic
system using the ESP32 microcontroller development board, the ACS712 20A
current sensor, and the voltage divider resistors was implemented in this
work. With the system in operation, the collected data were compared with the
data provided by the PV inverter, enabling the validation of the proposed
system. From the results
obtained, considering that the validation showed an error was less than 1%,
it can be concluded that the low-cost didactic system using ESP32 can be used
to measure PV plants similar to this one. This proposed system helps teach
concepts of both IoT and PV generation and
encourages the academic community to research renewable energies in technical
and university courses. For future works,
artificial intelligence can be applied to the data to detect failures. In
addition, the proposed project can help measure and report to users and
maintainers, in real-time and customized ways, the performance and any
failures in the electrical generation of the analyzed PV systems. Finally, it can be
concluded that it is possible to develop and apply a didactic monitoring
system with proper calibration and validation to assist academic research and
teaching purposes related to IoT monitoring systems
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