An IoT and Information Mining Algorithm-Based Self-Monitoring and Analysis System for Solar Power Plants

• Published in: 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 1268 - 1272
• Main Authors: Abdulwahid, Ali Hadi, Jani, Kruti, Dhaliwal, Navdeep, Singh, Rajesh, Bhasha, A., Lande, Jayapal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.05.2023
• Subjects: Aerospace electronics; data mining; Fault diagnosis; Maintenance engineering; maximum power generation; Production; Renewable energy sources; Solar energy; solar panel maintenance: IoT; Solar panels
• DOI: 10.1109/ICACITE57410.2023.10183233

Because of their affordability and sustainability, renewable sources of energy are receiving a lot of study interest. Particularly, solar power plants are regarded as a type of renewable sources of energy that may be employed in many areas since, while requiring less space than traditional systems, they demand less money for installation and maintenance. The majority of small generating units control space occupancy by putting the machinery on an exposed terrace. However, acres of land is needed for the building of huge power producing stations. It is difficult for human workers to maintain such a huge region of a power plant. The suggested algorithm would assist human workers in identifying the consistency of energy production and failure or faulty areas in solar energy systems using IoT and database mining (DM) techniques. This enables immediate fault repair action to be taken, which increases producing station effectiveness.