Design and Implementation of a Multi-Sensor DAQ System for Comparative Photovoltaic Performance Analysis

TL;DR

This paper details a custom multi-sensor data acquisition system for photovoltaic panels, enabling precise, reliable, and real-time performance analysis with advanced fault recovery and remote monitoring.

Contribution

It introduces a novel integrated hardware-software DAQ system tailored for comparative photovoltaic performance studies, combining custom PCB, sensors, and cloud-based data management.

Findings

System operated reliably during field deployment

Achieved 1-minute sampling rate for real-time data

Enabled continuous remote monitoring and fault recovery

Abstract

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.