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Alessio Dallabona

Publications and source records attributed to Alessio Dallabona.

3 recordsLinked to original sources

Dynamic Input Mapping Inversion to Eliminate Algebraic Loops in Hydraulic Actuator Control

The application of nonlinear control schemes to electro-hydraulic actuators often requires several alterations in the design of the controllers during their implementation. This is to overcome challenges that frequently arise in such control algorithms owing to model nonlinearities. Moreover, advanced control solutions for this type of systems often introduce input algebraic loops that pose significant design and tuning difficulties. Conventional methods to avoid such loops introduce chatter, which considerably degrade tracking performance and has oil degradation and wear as side effects. This study presents a nonlinear control architecture for hydraulic actuators that comprises low-complexity modules that facilitate robust high performance in tracking and avoids the drawbacks of chatter. The salient feature is a dynamic input-mapping inversion module that avoids algebraic loops in the control input and is followed by dedicated position control. The stability of the closed-loop system is analyzed using arguments from Lyapunov theory for cascaded non-autonomous nonlinear systems. The effectiveness of the proposed solution is evaluated on a high-fidelity simulator of a wind turbine pitch system, and validated on a full-scale laboratory setup that includes a hydraulic pitch system and blade bearing. Appropriate quantitative metrics are used to evaluate the closed-loop system performance in comparison to a state-of-the-art nonlinear design.

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Condition monitoring of wind turbine blades via learning-based methods

This paper addresses the topic of condition monitoring of wind turbine blades and presents a learning-based approach to fault detection. The proposed scheme utilises Principal Components Analysis and Autoencoders to derive data-driven models from root-bending moment and other measurements. The models are trained with real data obtained from a fault-free wind turbine, and then validated on data corresponding to unknown health condition. Online test statistics, employing static thresholds and Generalized Likelihood Ratio tests, are used on residual signals generated by discrepancies between the actual and reconstructed measurements to detect deviations from nominal operation. The efficacy and effectiveness of the proposed methods are demonstrated using real-life data collected from wind turbines experiencing blade faults.

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Fault Diagnosis and Prognosis Capabilities for Wind Turbine Hydraulic Pitch Systems

Wind energy is the leading non-hydro renewable technology. Increasing reliability is a key factor in reducing the downtime of high-power wind turbines installed in remote off-shore places, where maintenance is costly and less reactive. Defects in the pitch system are responsible for up to 20% of a wind turbine downtime.Thus, monitoring such defects is essential for avoiding it. This paper presents a generic assessment of the diagnosis capabilities in hydraulic pitch systems, which are used in high-power wind turbines. A mathematical model of the non-linear system dynamics is presented along with a description of the most frequent faults that occur. Structural analysis is used to assess which defects can be detected in the pitch system. The structural properties are furthermore explored to investigate the possibility of reducing the amount of sensors without compromising the fault diagnosis capabilities. Robustness to model uncertainty is finally addressed and generic principles for estimating the detectable magnitude of wear and tear are presented.

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