High-Speed Monitoring of Multiple Grid-Connected Photovoltaic Array Configurations and Supplementary Weather Station.

Area of Science:

• Materials Science
• Renewable Energy Engineering
• Atmospheric Science

Background:

• Grid-connected monocrystalline silicon photovoltaic (PV) arrays are crucial for renewable energy generation.
• Accurate performance monitoring and environmental data are essential for optimizing PV systems and validating models.
• The National Institute of Standards and Technology (NIST) campus provides a controlled environment for detailed PV research.

Purpose of the Study:

• To detail the instrumentation strategy for monitoring the performance of diverse PV arrays.
• To establish a comprehensive weather monitoring system with advanced solar irradiance measurement capabilities.
• To collect high-resolution data for validating PV performance and weather prediction computer models.

Main Methods:

• Instrumenting three grid-connected monocrystalline silicon PV arrays (73 kW to 271 kW) with varying configurations.
• Deploying research-grade sensors for irradiance, temperature, wind, and electrical measurements, sampled every 1 second.
• Establishing a dedicated weather station with meteorological instruments and multiple irradiance sensor technologies, including spectral and directional measurements.
• Utilizing imaging systems to capture array conditions, sky imagery, and reference module status for anomaly detection and obstruction monitoring.

Main Results:

• Continuous, high-frequency data acquisition (1s intervals) from PV arrays and the weather station since August 1, 2014.
• Collection of detailed environmental data, including standard meteorological quantities and specialized solar irradiance measurements.
• Generation of image data for monitoring array conditions, shading, and potential obstructions.
• Establishment of baseline performance data from reference PV modules.

Conclusions:

• The described instrumentation approach provides a robust framework for detailed PV system and environmental monitoring.
• The collected dataset is suitable for validating advanced PV performance and weather forecasting models.
• This research contributes to improving the reliability and efficiency of solar energy technologies.