This document proposes an IoT-based system for real-time monitoring and fault detection of solar power panels. The system uses sensors to measure temperature, dust, and rainfall conditions near the panels. It sends this sensor data to an Arduino board, which uses an artificial neural network model to analyze the data and detect any faults or underperformance in the panels. If a panel is found to be producing significantly less power than expected, a maintenance alert is sent via IoT. The system is designed to run on low-cost hardware and allow large solar farms to remotely monitor individual panels for optimal performance.
2. ABSTRACT
• To harness reliable energy efficiently, the photovoltaic panels system must
remain in its best condition. This requires continuous maintenance and
monitoring. However, in case of weather dependable energy yield change.
• This requires continuous maintenance and monitoring. However, in case of
weather dependable energy yield change.
• We proposed a novel real-time monitoring system utilizing a small but
efficient articial neural network that is adequate to run on a low-cost system
• To environmental conditions, or is not normal because of faulty, or shaded,
or dust-covered panel, an intelligent monitoring system is required.
• The presented PV monitoring system can identify if the photovoltaic panel
exhibit degradation due to fault conditions.
3. INTRODUCTION
• The fast-ever-growing energy demands and the global environmental issues
necessitate the use of renewable resources.
• Among renewable energy yielding technologies, Solar panels are of the
greatest future projection.
• An intelligent reference analytical module for real-time individual
photovoltaic panel monitoring based on the articial neural network.
• We discuss the system overall implementation, the mathematical module
behind its intelligent reference model, and its hardware implementation.
• The contribution of solar PV energy increased remarkably compared to the
previous four decades. Nowadays, PV energy represents the third-largest
source of renewable energy after wind and hydro
4. PROPOSED SYSTEM
• Photovoltaic panels system is becoming a popular choice as an alternative
source of energy. To harness reliable energy efficiently, the photovoltaic
panels system must remain in its best condition.
• This system consists of sensor like Temperature Sensor, Dust Sensor and
Rain Sensor.
• Temperature sensor is used to measure the temperature value
• Dust sensor used for detecting the dust will be high, at same for Rain
sensor is used to measure the rain value and display on LCD Display. Short
circuiting in solar is detected alerted through IOT
• Measured all sensor information updated to IOT.
6. SOFTWARE REQUIREMENTS
• MC Programming Language :Embedded C
• Coding : Arduino IDE 1.8.3
• Simulation :PROTEUS
7. HARDWARE REQUIREMENTS
• ARDUINO UNO
• SOLAR PANEL
• BATTERY
• TEMPERATURE SENSOR
• DUST SENSOR
• RAIN SENSOR
• LCD DISPLAY
• IOT
• WATER MOTOR
8. CONCLUSION
• Solar panels monitoring system is presented with high prediction accuracy.
The hardware design and implementation of the monitoring system on low
cost microcontroller was discussed.
• The monitoring system has the ability of identifying any individual PV
panel that requires maintenance
• The more PV panels are connected to the monitoring system, the lower is
the additional cost per PV panel.
• The monitoring system will a PV panel for maintenance if the predicted
output power for that PV panel, obtained from articial neural network
model.
• The actual output power of that PV panel, obtained from sensors, has a
percentage difference more than 10%.
9. REFERENCE
• [1] J. Benedek, T.-T. Sebestyén, and B. Bartók, ``Evaluation of renewable
energy sources in peripheral areas and renewable energy-based rural
development,'‘ Renew. Sustain. Energy Rev., vol. 90, no. 1, pp. 516535, 2018.
• [2] M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and
A. P. Sheth, ``Machine learning for Internet of Things data analysis: A survey,''
Digit. Commun. Netw., vol. 4, no. 3, pp. 161175, 2018.
• [3] P. Sethi and S. R. Sarangi, ``Internet of Things: Architectures, protocols,
and applications,'' J. Elect. Comput. Eng., vol. 2017, Jan. 2017, Art. no.
9324035.
• [4] P. P. Ray, ``A survey of IoT cloud platforms,'' Future Comput. Inform. J.,
vol. 1, nos. 12, pp. 3546, 2016.
