Design and Implementation of a Multi-Sensor DAQ System for Comparative Photovoltaic Performance Analysis
This addresses the problem of precise photovoltaic performance analysis for researchers, but it is incremental as it applies existing methods to a new setup.
The paper tackled the need for a custom data acquisition system to compare the performance of two photovoltaic panels with different cooling systems, resulting in a reliable system with a 1-minute sampling rate and continuous operation from 5 AM to 6 PM daily.
The rigorous analysis of specialized physical processes often demands custom data acquisition architectures that offer flexibility and precision beyond the capabilities of general-purpose commercial loggers. This paper presents the design and implementation of a robust data acquisition system (DAQ) for a comparative analysis of the performance of two photovoltaic panels with two different cooling systems. The system integrates a custom PCB design for 20 thermistors, dual high-precision INA228 current/voltage sensors, environmental monitoring equipment, and a Raspberry Pi 4-based acquisition platform. The software architecture implements autonomous operation with enhanced fault recovery, dual storage redundancy (local CSV and InfluxDB), cloud synchronization via Google Drive, and real-time visualization through Grafana dashboards. Field deployment demonstrated system reliability, including automatic recovery from power interruptions, a 1-minute sampling rate, remote monitoring capabilities, and continuous operation during a 5 AM to 6 PM daily window. The modular hardware and software architecture enables simultaneous monitoring of two photovoltaic panels for research on direct performance comparison under identical environmental conditions.