Electronic Theses and Dissertation

Pembangkit listrik tenaga surya (PLTS) off-grid merupakan salah satu solusi penyediaan energi listrik mandiri, namun kinerjanya sangat dipengaruhi oleh kondisi lingkungan dan keterbatasan dalam proses pemantauan serta pemeliharaan. Penelitian ini bertujuan untuk mengembangkan sistem monitoring berbasis Internet of Things (IoT) yang mampu mendukung pemeliharaan prediktif melalui pengamatan kondisi lingkungan dan performa panel surya secara real-time. Sistem dirancang menggunakan mikrokontroler ESP32 yang terintegrasi dengan dua sensor INA219 untuk mengukur tegangan dan arus pada panel surya utama berkapasitas 30 Wp serta panel surya mini 5 V yang digunakan sebagai indikator radiasi matahari. Selain itu, digunakan sensor GP2Y1014AU untuk mengukur konsentrasi debu di udara dan sensor DHT21 untuk memantau suhu lingkungan. Data hasil pengukuran diolah untuk memperoleh parameter daya dan estimasi irradiance, kemudian dikirimkan melalui jaringan Wi-Fi ke aplikasi Blynk untuk pemantauan jarak jauh. Hasil pengujian menunjukkan bahwa sistem mampu memantau hubungan antara irradiance, konsentrasi debu, dan daya keluaran panel surya. Peningkatan irradiance cenderung meningkatkan daya keluaran, sedangkan peningkatan konsentrasi debu menyebabkan penurunan performa panel, terutama pada kondisi irradiance tinggi. Informasi tersebut digunakan untuk mengidentifikasi potensi penurunan kinerja panel secara dini.

An off-grid solar power plant is one of the alternative solutions for independent electricity supply. However, its performance is strongly affected by environmental conditions as well as limitations in monitoring and maintenance processes. This study aims to develop an Internet of Things (IoT)-based monitoring system capable of supporting predictive maintenance through real-time observation of environmental parameters and solar panel performance. The system was designed using an ESP32 microcontroller integrated with two INA219 sensors to measure the voltage and current of a 30 Wp main solar panel and a 5 V mini solar panel used as an indicator of solar irradiance. In addition, a GP2Y1014AU sensor was utilized to measure dust concentration in the air, while a DHT21 sensor was employed to monitor ambient temperature. The acquired data were processed to obtain power parameters and irradiance estimation, and then transmitted via a Wi-Fi network to the Blynk application for remote monitoring. The experimental results indicate that the proposed system is capable of monitoring the relationship between irradiance, dust concentration, and solar panel output power. Higher irradiance tends to increase the output power, whereas higher dust concentration leads to a decline in panel performance, particularly under high-irradiance conditions. These findings can be used to identify potential degradation in solar panel performance at an early stage and to support predictive maintenance strategies.