Abstract
Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.
Highlights
With increasing global energy demand, wind energy as a promising clean source of renewable energy has become an indispensable force in solving world energy problems
The examples given are real wind turbine events from a wind farm recorded by supervisory control and data acquisition (SCADA) systems
A health monitoring method for wind turbine operational states has been proposed to consider the main bearing nearly 2.2 h earlier than the optimized DBN (ODBN) approach. It means that the dynamic operating conditions of wind turbines and address the difficulty in accurately building there is only a 2.2 h improvement in using the k-means based ODBN model over the ODBN model normal behavior models
Summary
With increasing global energy demand, wind energy as a promising clean source of renewable energy has become an indispensable force in solving world energy problems. The latest annual report released by the Global Wind Energy Council (GWEC) [1] shows that the cumulative and new installed capacity in the world had reached 539,123 MW and 52,492 MW, respectively, by the end of. Wind turbines are generally situated in remote locations and have harsh operating environments, resulting in frequent failures and undesired shutdowns. There is an urgent need for effective prognostics and health management (PHM) technologies to address these problems. It is crucial and valuable to develop advanced health monitoring and fault detection methods to detect impending wind turbine faults as early as possible in order to avoid secondary damage and even catastrophic accidents
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