Nowadays, the number of assembled wind turbines in the world is growing more rapidly, which brings an urgent need for intelligent operation and maintenance of wind turbines. The intelligence of wind turbine operation and maintenance is based on the high-precision classification and recognition of SCADA system data. In response to this demand, this paper establishes a wind turbine normal data discrimination model that combines SCADA system data preprocessing and random forest integrated learner. First, obtain a determinable sample dataset according to the principles of statistics and the NearMiss under-processing method. Then build a decision tree, use the features in a variety of SCADA datasets to train and learn the sample dataset, and form a random forest to determine the normal data model of wind turbines. The results show that the model can effectively classify whether the SCADA data of wind turbines is normal, achieve a higher accuracy rate, and improve the reliability of discrimination, which is of great significance to the subsequent research on intelligent operation and maintenance of wind turbines.
KEYWORDS: Data modeling, Wind turbine technology, Frequency converters, Instrument modeling, Data conversion, Statistical modeling, Fluctuations and noise, Analytical research, Wind energy, Temperature metrology
In recent years, with the development of wind power industry, the installed capacity is increasing rapidly, and the installation sites are developing towards the ocean and remote mountainous areas, which makes the maintenance of wind turbines difficult and the cost increases. In order to reduce the maintenance cost and improve the maintenance efficiency, and ensure the economic and reliable operation of the equipment, many state recognition and fault early warning methods have been proposed one after another, and the intelligent fault diagnosis method based on supervisory control and data acquisition(SCADA) data and various kinds of machine learning has gradually become a research hotspot. In this paper, the generator system of a wind turbine in a wind farm is taken as the research object. By using the massive SCADA data recorded in operation, a multivariate state estimation technique (MSET) is established to predict specific operation parameters with relevant operation parameters as input. This method uses clustering algorithm to clean up the selected SCADA data, then uses MSET to establish the prediction model, and calculates the prediction residual by sliding window method to realize the fault diagnosis. Finally, the effectiveness of the method is verified by actual SCADA data
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