Dataset for the identification of a ultra-low frequency multidirectional energy harvester for wind turbinesMendeley Data
Julen Bacaicoa,
Mikel Hualde-Otamendi,
Mikel Merino-Olagüe,
Aitor Plaza,
Xabier Iriarte,
Carlos Castellano-Aldave,
Alfonso Carlosena
Affiliations
Julen Bacaicoa
Department of Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
Mikel Hualde-Otamendi
Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
Mikel Merino-Olagüe
Department of Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
Aitor Plaza
Department of Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Corresponding author.
Xabier Iriarte
Department of Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
Carlos Castellano-Aldave
Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
Alfonso Carlosena
Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain; Institute of Smart Cities (ISC), Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain
This paper presents a publicly available dataset designed to support the identification (characterization) and performance optimization of an ultra-low-frequency multidirectional vibration energy harvester. The dataset includes detailed measurements from experiments performed to fully characterize its dynamic behaviour. The experimental data encompasses both input (acceleration)–output (energy) relationships, as well as internal system dynamics, measured using a synchronized image processing and signal acquisition system. In addition to the raw input–output data, the dataset also provides post-processed information, such as the angular positions of the moving masses, their velocities and accelerations, derived from recorded high-speed videos at 240 Hz. The dataset also includes the measured power output generated in the coils. This dataset is intended to enable further research on vibration energy harvesters by providing experimental data for identification, model validation, and performance optimization, particularly in the context of energy harvesting in low-frequency and multidirectional environments, such as those encountered in wind turbines.
