Design and Analysis of IoT-Based Smart Monitoring System for Solar Power Plants
DOI:
https://doi.org/10.63891/j-mart.v1i4.135Keywords:
Internet of Things, photovoltaic system, real-time data, smart system, solar monitoringAbstract
The increasing adoption of solar power plants as a renewable energy source requires efficient monitoring systems to ensure optimal performance and reliability. Conventional monitoring methods are often limited in providing real-time data and early fault detection, which can reduce system efficiency and increase maintenance costs. Therefore, this study aimed to design and analyze a smart monitoring system based on the Internet of Things for solar power plants that enables continuous data acquisition, real-time monitoring, and performance evaluation. This research employed an experimental and quantitative approach by integrating sensors, a microcontroller, wireless communication, and a cloud-based platform into a unified system. The system measured key parameters, including voltage, current, power, temperature, and solar irradiance, and transmitted the data to a cloud dashboard for visualization. The results showed that the system successfully monitored all parameters in real time with measurement errors below 3 percent. The data transmission latency ranged from 1.2 to 1.5 seconds, indicating fast and stable communication performance. In addition, the system demonstrated high reliability, operating continuously for 8 hours without downtime or data loss. These findings indicate that the proposed system is capable of providing accurate, reliable, and real-time monitoring of solar power plant performance. The study contributes to the development of an integrated and cost-effective monitoring framework that combines multi-parameter sensing, wireless communication, and cloud-based analysis. However, the system was tested on a limited scale and within a short observation period. Future research is recommended to expand the system for large-scale applications and to integrate advanced data analysis techniques for predictive maintenance and performance optimization.
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