Abstract
In this paper, we address the problem of real-time fault detection in wind turbines. Starting from a data-driven fault detection method, the contribution of this paper is twofold. First, a sensor selection algorithm is proposed with the goal to reduce the computational effort of the fault detection method. Second, an analysis is performed to reduce the data acquisition time needed by the fault detection method, that is, with the goal of reducing the fault detection time. The proposed methods are tested in a benchmark wind turbine where different actuator and sensor failures are simulated. The results demonstrate the performance and effectiveness of the proposed algorithms that dramatically reduce the number of sensors and the fault detection time.
Highlights
Wind energy, in contrast to burning fossil fuels, is a clean and inexhaustible renewable energy source
Leakage of pitch cylinders can be internal or external [16]. When this fault reaches a certain level, system repair is necessary, and if the leakage is too fast, it will lead to a pressure drop and the preventive procedure is deployed to shut down the turbine before the blade is stuck in undesired position
The goal of this section is to present a method to select a reduced number of sensors to be used in the fault detection method
Summary
In contrast to burning fossil fuels, is a clean and inexhaustible renewable energy source. A sensor selection algorithm based on principal component analysis (PCA) is used to select the sensors that best separate the healthy and the faulty wind turbine with the purpose of fault detection leading to some reduction in the computational and communication effort. In order to test the proposed sensor selection algorithm as well as the fault detection time reduction, we used data from simulations using the comprehensive wind turbine simulator FAST (fatigue, aerodynamic, structures and turbulence) for a 5 MW wind turbine [11]. Different actuator and sensor failures are simulated following the benchmark model proposed in [12] In this benchmark challenge, a more sophisticated wind turbine model (using FAST) and updated fault scenarios are presented.
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