A Systems-Engineered ESP32 DAQ Architecture and FAIR Data Workflow for Small-Scale Wind Turbine Performance Measurement in Tropical Environments

TL;DR

This paper introduces a comprehensive, systematized data acquisition and data management framework using ESP32 for small wind turbines in tropical environments, ensuring reliable, high-quality datasets suitable for research and publication.

Contribution

It presents a novel integrated methodology combining hardware design, firmware architecture, and data workflow tailored for resource-limited tropical settings, with open-source tools and templates.

Findings

Achieved over 90% data completeness in a six-month coastal wind turbine campaign.

Developed a robust, open-source ESP32-based DAQ system for harsh environments.

Established a FAIR-compliant data workflow for small wind turbine research.

Abstract

Small-scale wind turbine research in resource-constrained academic settings frequently produces unreliable or unpublishable datasets due to ad-hoc instrumentation, inadequate time synchronization, storage failures, and weak data governance. This paper presents a systematic data acquisition (DAQ) methodology and ESP32-based reference implementation design for field characterization of small wind turbines (100~W--5~kW), emphasizing tropical/coastal deployment constraints typical of Low- and Middle-Income Countries (LMIC). We integrate (i)~a student-adapted V-model with requirements traceability, (ii)~hardware selection strategies for high-humidity and salt-spray environments, (iii)~an embedded firmware architecture featuring interrupt-driven rotor speed measurement, state-machine fault handling, and NTP-based time synchronization, (iv)~a local-first hybrid storage design combining SD-card persistence with optional MQTT cloud telemetry, and (v)~a data-management workflow adapting CRISP-DM and FAIR principles with explicit quality dimensions and publication templates. A detailed helical vertical-axis wind turbine (VAWT) design scenario for coastal Sri Lanka illustrates the complete methodology, targeting $>90\%$ data completeness over six-month campaigns. The methodology is accompanied by open-source firmware, hardware templates, and data-publication workflow artifacts released via GitHub and Zenodo.