Wind Turbine Fault Early Warning Based On SCADA Data

With the national emphasis on carbon neutrality and development,wind energy has gained widespread attention as an important component of new energy reserves.With this,the wind power industry has grown considerably,with installed capacity rising year.However,due to the relatively complex operating environment of wind turbines,the complex working conditions make it possible to gradually increase the frequency of unit failures as the units run for long periods of time.Because the units are scattered and mostly located in remote areas,on-site operators are generally unable to repair faulty units in time,causing many difficulties in operation and maintenance and economic losses.The main bearing,as the entire mechanical structure,is more prone to failure and the overall repair and maintenance is more complex,it is important to identify abnormal conditions early in order to avoid serious failures and improve the operating efficiency.Through the modelling and analysis of the unit,real-time monitoring of the system operation status and making abnormal condition warning information are important research directions.This paper proposes a method of main bearing failure warning for wind turbines based on SCADA system to solve the current problems of high main bearing failure rate,difficult maintenance and high cost due to the limitation of technical development.This paper first introduces the basic structure and common faults of wind turbines,and introduces the principle of SCADA system.The paper then analyses various existing means of relevant fault warning and fault diagnosis,compares the advantages and disadvantages,and proposes a fault warning method based on SCADA data,first proposes a data processing method based on statistical principles and the DBSCAN algorithm,uses a variety of correlation analysis indicators to evaluate the data collected by the SCADA system,and selects the characteristic quantity with higher correlation as the prediction of main bearing temperature The indicators of the main bearing temperature are selected.Several machine learning algorithms are used to establish a main bearing fault warning model to predict the main bearing temperature,and compare the prediction accuracy of various methods to select a more suitable prediction model,then a decision model is established using the SPC statistical control principle combined with the sliding window idea method to realise the main bearing fault warning,and a main bearing warning module is programmed using python to complete,providing This will provide wind farm operators and maintenance personnel with more adequate maintenance time and reduce the cost of wind turbine operation and maintenance.