Publicaciones – Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Profesor "Carlos Christiansen".

Saavedra M, Inthamoussou F and Garelli F (2026), "Evaluating the Influence of Input Features for Data-Based Estimation of Wind Turbine Blade Deflections", Processes., January, 2026. Vol. 14(5) MDPI AG.

[Abstract] [BibTeX] [DOI]

Abstract: The increasing scale and structural flexibility of modern wind turbine rotors have made real-time monitoring and active control of blade tip deflection a critical requirement for ensuring operational safety, particularly regarding blade-tower clearance. Since direct measurement through physical sensors is often impractical due to high costs, installation difficulties and maintenance challenges, this work proposes a data-based framework for out-of-plane blade tip deflection estimation. The approach introduces a systematic and hierarchical input selection framework that evaluates sensor signal groups, ranging from standard SCADA measurements to configurations including auxiliary nacelle/tower sensors and dedicated blade-root instrumentation. By combining Spearman correlation and spectral coherence, the proposed framework ensures consistent representation of key turbine dynamics across all operating regions. This framework provides a structured trade-off between implementation feasibility and estimation fidelity, enabling tailored solutions for applications such as structural health monitoring and safety-critical active control. Compact Feedforward Neural Network (FNN) and Time-Delay Neural Network (TDNN) architectures, whose hyperparameters are optimized via Bayesian optimization, are employed to achieve high estimation accuracy while preserving computational efficiency. Evaluated through high-fidelity aeroelastic simulations of the NREL 5 MW turbine using the industry-standard FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool across all operating conditions, the approach achieves R2=0.894 using SCADA-only inputs, R2=0.973 when augmented with nacelle and tower-top sensors and a peak fidelity of R2=0.989 using blade-root bending moment data. These results demonstrate that high-fidelity virtual sensing is attainable without blade instrumentation, providing a viable pathway for real-time tip clearance monitoring and fatigue mitigation. This directly enhances the operational resilience of wind energy systems and their contribution to the stability of renewable-dominated power grids.

BibTeX:

@article{Saavedra2026,
  author = {M. Saavedra and F. Inthamoussou and F. Garelli},
  title = {Evaluating the Influence of Input Features for Data-Based Estimation of Wind Turbine Blade Deflections},
  journal = {Processes},
  publisher = {MDPI AG},
  year = {2026},
  volume = {14},
  number = {5},
  doi = {10.3390/pr14050831}
}

Jamilis M, Castañeda M and Battista HD (2025), "Extremum‐Seeking Control for Methane Production Optimization in Low‐Cost Anaerobic Digesters", Biotech & Bioengineering., December, 2025. Wiley.

[Abstract] [BibTeX] [DOI]

Abstract: ABSTRACT
This study proposes an extremum‐seeking control strategy to optimize methane production in anaerobic digesters. The strategy is aimed at rural and low‐cost plants, with scarce instrumentation, long sampling periods, and minimum knowledge of the process. The proposed scheme maximizes methane productivity by adjusting the dilution rate, relying only on weekly measurements of the methane production rate and the FOS/TAC ratio, which serves as a stability indicator. Stability issues due to process acidification are studied and addressed by including a pH control loop and slope generator acting as an auxiliary safety loop based on the FOS/TAC ratio. The control strategy is validated using numerical simulations with the ADM1 benchmark model. Results demonstrate robust convergence to the optimal methane production point, effective disturbance rejection under time‐varying influent conditions, and the prevention of instability due to process acidification. The proposed method achieves near‐optimal productivity with a low‐cost implementation, making it well‐suited for small‐scale digesters and resource‐limited settings, thus contributing to sustainable bioenergy generation and the efficient utilization of renewable resources.

BibTeX:

@article{Jamilis2025,
  author = {M. Jamilis and M. Castañeda and H. De Battista},
  title = {Extremum‐Seeking Control for Methane Production Optimization in Low‐Cost Anaerobic Digesters},
  journal = {Biotech & Bioengineering},
  publisher = {Wiley},
  year = {2025},
  doi = {10.1002/bit.70121}
}

Jurao FLDR, Fushimi E and Garelli F (2025), "Model based analytical approach for physical activity quantification in people with type 1 diabetes", Med Biol Eng Comput., October, 2025. Vol. 64(2), pp. 465-481. Springer Science and Business Media LLC.