Actuator and Sensor Fault Diagnosis for Wind Energy Conversion Systems

Sharan, Bindu; Jain, Tushar

doi:10.1109/icarcv.2018.8581339

Cited by 4 publications

(2 citation statements)

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“…Since the rotor torque cannot be measured, an estimator based on an algebraic map that depends on the measurement of wind speed, pitch angle, and generator speed is used in this scheme. This algebraic map is described in more detail in [19]. In the residual generator block, the estimation errors of each of the outputs are used as residuals.…”

Section: Fdi Configurations For Drive Train Systemmentioning

confidence: 99%

“…The methods proposed in the literature generally use thresholds for the residual values generated by the difference between the outputs of observer banks and the output signals of the system, as shown in [18]. Fault detection in wind turbine systems is reported in [19] and fault diagnosis based on state estimation is presented in [20]; these works describe the detection and isolation of faults in sensors and actuators of reference wind turbines. An approach to detect faults in actuators and sensors of wind turbines using unknown input observers (UIOs), is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

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Sliding Mode Observer-Based Fault Detection and Isolation Approach for a Wind Turbine Benchmark

et al. 2021

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A fault detection and isolation (FDI) approach based on nonlinear sliding mode observers for a wind turbine model is presented. Problems surrounding pitch and drive train system FDI are addressed. This topic has generated great interest because the early detection of faults in these components allows avoiding irreparable damage in wind turbines. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed due to its ability to handle model uncertainties and external disturbances. Unlike the reported solutions, the solution approach does not need a priori knowledge of the faults and considers system uncertainty. The robustness to disturbances, uncertainties, and measurement noise is shown in the dynamic of the generated residuals, which is sensible to only one kind of fault. To show the effectiveness of the proposed FDI approach, numerical examples based on a wind turbine benchmark model, considering closed loop applications, are presented.

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Section: Fdi Configurations For Drive Train Systemmentioning

confidence: 99%