@Article{anton2025ieeesensors,
  author  = {Miguel Angel Pérez García and Juan Carlos Álvarez Antón and Juan Carlos Granda Candás and Carlos Carleos Artime and Ana Isabel Suárez and Luis Pérez Castaño},
  title   = {An IoT-Based Instrumentation System for Mechanical Stress Monitoring in Solar Plants},
  volume       = {},
  number       = {},
  pages        = {},
  issn         = {1530-437X},
  abstract     = {This article introduces a versatile and scalable Internet of Things (IoT)-based instrumentation system designed for real-time monitoring of mechanical stresses in photovoltaic (PV) solar structures. The system employs load cells strategically integrated into the PV support structure to measure the tensile and compressive stresses induced by wind, snow, and panel orientation. The sensing infrastructure gathers data from a network of autonomous measurement nodes and transmits it to a central hub. This hub subsequently forward data via Wi-Fi to a cloud-based platform using the MQTT publish/subscribe protocol. The complete development of the measurement electronics is presented, integrating several key features such as 1) complete galvanic isolation between all subsystems to prevent ground loops, achieving a total measurement system uncertainty of 0.1%; 2) a dual communication scheme utilizing both wired controller area network (CAN) and wireless (Zigbee) protocols, ensuring stable wireless data transmission over distances up to 200 m without repeaters, even in electromagnetically noisy environments; 3) highly synchronized data sampling for accurate time correlation with temporal uncertainty below 0.01 ms across distributed measurement nodes; 4) a programmable autoranging mechanism that extends the measurable load range up to ±100 kg, while maintaining
10-bit ADC resolution across all measurement scales; 5) a sampling rate up to 1 kHz; 6) seamless integration into PV plants of any size and configuration; and 7) advanced capabilities for monitoring, storage, and querying through
Grafana and an InfluxDB time-series database. The system was deployed and validated in an operational PV installation, demonstrating its effectiveness in providing accurate structural data to support optimized design and predictive
strategies in PV plants},
  author+an    = {3=highlight},
  date         = {2026},
  year         = {2026},
  doi          = {},
  journal = {IEEE Sensors Journal},
  keywords     = {},
  shortjournal = {},
  jcr          = {4.5 -- Q1 [2024]},
  file         = {revistas/anton2025ieeesensors.pdf}
}