Browsing by Author "Farsoni, Saverio"

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Citation - WoS: 3
Citation - Scopus: 3
Design and Validation of a Fault Tolerant Fuzzy Control for a Wind Park High-Fidelity Simulator
(Ieee, 2021) Simani, Silvio; Turhan, Cihan; Farsoni, Saverio
To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real-time solutions have to implement the proposed fault tolerant control strategy. Finally, a high-fidelity simulator including hardware-in-the-loop modules is exploited to validate the reliability and robustness characteristics of the developed methodologies also for on-line implementations.
Citation - WoS: 1
Citation - Scopus: 2
Hardware-In Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator
(Elsevier, 2022) Simani, Silvio; Farsoni, Saverio; Turhan, Cihan
To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real-time solutions have to implement the proposed fault tolerant control strategy. Finally, a high-fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations. Copyright (C) 2022 The Authors.