SOL-DAQ

Experimental platform for high-frequency photovoltaic data acquisition and diagnostic-oriented monitoring.

Overview

SOL-DAQ (Solar Diagnostic-Oriented Data Acquisition Platform) is a research project dedicated to the development of a new generation of photovoltaic monitoring infrastructures specifically designed for fault diagnosis, predictive maintenance and explainable artificial intelligence applications.

The project is conducted in collaboration between PROMES-CNRS (CSPG team) and LAAS-CNRS (ISGE team) and constitutes the foundational layer of a broader scientific strategy dedicated to intelligent diagnosis of photovoltaic systems.

Unlike conventional monitoring systems, which are mainly designed for supervision and performance assessment, SOL-DAQ adopts a diagnostic-oriented philosophy in which data acquisition is specifically designed to support fault detection, anomaly characterization and prognostics.

Funding

PROMES-CNRS / LAAS-CNRS Collaboration

Participating Institutions

PROMES-CNRS (UPR 8521)

LAAS-CNRS (ISGE Team)

Université de Perpignan Via Domitia (UPVD)

Project Team

  • Edgar Hernando Sepúlveda-Oviedo
  • PROMES-CNRS CSPG Team
  • LAAS-CNRS ISGE Team

Scientific Context

Modern photovoltaic power plants generate large amounts of electrical and environmental data. However, most existing monitoring systems operate at relatively low sampling frequencies and were not originally designed to support advanced diagnostic applications.

As a consequence, many transient phenomena, weak anomalies and early-stage degradation mechanisms remain difficult to identify.

The development of advanced diagnostic methodologies requires access to synchronized, high-quality and high-frequency measurements capable of capturing the dynamic behavior of photovoltaic systems under real operating conditions.

SOL-DAQ addresses this challenge by creating a dedicated diagnostic-oriented acquisition platform capable of providing reliable and structured data for fault diagnosis and prognostics applications.

Main Objectives

The project pursues several complementary objectives:

  • Develop a high-frequency photovoltaic monitoring infrastructure.
  • Synchronize electrical and environmental measurements.
  • Improve data quality and traceability.
  • Support explainable diagnostic methodologies.
  • Facilitate predictive maintenance applications.
  • Create FAIR-compliant photovoltaic datasets.
  • Provide experimental validation data for future AI developments.

Methodology

The project follows a complete diagnostic-oriented data management pipeline.

Global architecture of the SOL-DAQ platform, from data acquisition and transmission to preprocessing, fault detection, diagnosis and decision support.

Stage 1 – Multi-Source Data Acquisition

The platform integrates multiple sources of information, including:

  • DC electrical measurements
  • AC electrical measurements
  • Meteorological variables
  • Irradiance measurements
  • Temperature measurements
  • Operational indicators

Special attention is given to synchronization and temporal consistency.

Stage 2 – Data Transmission and Storage

A dedicated infrastructure is developed to guarantee:

  • Data integrity
  • Temporal synchronization
  • Reliable storage
  • Long-term traceability

The objective is to preserve the diagnostic value of acquired signals.

Stage 3 – Data Quality and Preprocessing

Before any diagnostic analysis, the collected data undergo:

  • Cleaning procedures
  • Missing data handling
  • Signal validation
  • Consistency checks
  • Outlier detection

These steps ensure robust downstream analyses.

Stage 4 – Diagnostic-Oriented Exploitation

The acquired data serve as the basis for:

  • Fault detection
  • Fault classification
  • Prognostics
  • Explainable AI
  • Digital twin development
  • Decision support systems

Scientific Contributions

SOL-DAQ introduces several innovations:

  • Diagnostic-oriented monitoring philosophy.
  • High-frequency synchronized acquisition.
  • Multi-source photovoltaic instrumentation.
  • FAIR-compliant data management.
  • Integration of acquisition and diagnostics within a common framework.

Expected Outcomes

The project is expected to deliver:

  • A fully operational acquisition platform.
  • High-quality photovoltaic datasets.
  • Experimental infrastructures for research.
  • Open scientific datasets.
  • Diagnostic validation campaigns.
  • Scientific publications.
  • Support for future research projects.

Scientific Impact

SOL-DAQ represents the data layer of a broader diagnostic ecosystem developed at PROMES-CNRS.

The project provides the experimental foundations required by subsequent projects such as:

  • FREE – PV-FIT
  • ALARMES
  • SOL-MIND

By improving data quality at the source, SOL-DAQ directly contributes to increasing the reliability and explainability of diagnostic methodologies.

Long-Term Vision

The platform is intended to become a permanent experimental infrastructure supporting future developments in:

  • Photovoltaic fault diagnosis
  • Predictive maintenance
  • Explainable Artificial Intelligence
  • Digital Twins
  • Prognostics and Health Management (PHM)

Keywords

Photovoltaics · Data Acquisition · Instrumentation · Monitoring Systems · Diagnostic-Oriented Monitoring · FAIR Data · Predictive Maintenance · PHM · Explainable AI · Digital Twin

Project Status

Ongoing