Abstract
When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).
Highlights
A wind turbine is a complex electromechanical equipment, usually located in remote areas with harsh environments
This study focuses mainly on the fault diagnosis of wind turbine anemometer based on the Random Walk Improved Sparrow Search Algorithm (RWSSA)-AANN model
AThNeNre, GsuAlt-sAsAhNowN,thanatdtPhCe AMmSEodveallsu,ewohfetrheaes RtWheSFSDAR-AvAaNluNe omf tohdeeRlWisSSloAw-AerANthNanmtohdaet loisf hAigAhNerNth, aGnAth-AatAoNf tNhe, AanAdNPNC, AGAm-AodAeNlsN, wahnedrePaCs AthemFoDdeRlsv. aIltuceaonfbteheseReWn tShSaAt -tAheARNWNSmSAod-AelAiNs hNigmheordtehlaenxhthibaittsofhitghhe aAcAcuNrNac,y GaAn-dAsAmNalNl earnrodr,PwChAicmhoisdbelesn. eItficcaianl bfoersiemenprtohvaitntghethReWacScSuAra-AcyAoNf Nfaumltoddiealgenxohsiibsiotsf hwiginhd actucurbrianceyaannedmsommalelteerr.ror, which is beneficial for improving the accuracy of fault diagnosis of wind turbine anemometer
Summary
A wind turbine is a complex electromechanical equipment, usually located in remote areas with harsh environments. In reference [12], a fault detection and isolation method based on classifier fusion and data-driven fusion of multiple classifiers is proposed to extract features from the measured signals, which enriches the state information of wind turbines and improves the decision-making ability of fault detection and isolation schemes. These methods do not consider the correlation and nonlinear mapping between the same sensors of wind turbines in wind farms, so the detection performance is limited.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
