期刊
IEEE ACCESS
卷 9, 期 -, 页码 69307-69315出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3075547
关键词
Wind turbines; Fault diagnosis; Support vector machines; SCADA systems; Wind farms; Kernel; Biological system modeling; Sparrow search algorithm (SSA); fault diagnosis; wind turbines; support vector machine (SVM); parameter optimization
资金
- Ministry of Science and Technology of Peoples Republic of China [2019YFE0104800]
- National Natural Science Foundation of China [U1865101]
Fault diagnosis technology is crucial for the safe and stable operation of wind turbines, with support vector machines being a popular intelligence method in this field. The use of the sparrow search algorithm to optimize SVM parameters has resulted in the SSA-SVM model having high accuracy and optimization ability in wind turbine fault diagnosis.
Fault diagnosis technology is key to the safe and stable operation of wind turbines. An effective fault diagnosis technology for wind turbines can quickly identify fault types to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. Currently, most wind farms obtain operation and maintenance data via supervisory control and data acquisition (SCADA) systems, which contain rich information related to the operation characteristics of wind turbines. However, few SCADA systems provide fault diagnosis functionality. Support vector machines (SVMs) are a popular intelligence method in the fault diagnosis of wind turbines. SVM parameter selection is key for accurate model classification. The sparrow search algorithm (SSA) is a novel and highly efficient optimization method used to optimize the penalty factor and kernel function parameter of SVM in this paper and to construct the SSA-SVM wind turbine fault diagnosis model. Data are acquired from a wind farm SCADA system and form a faulting set after preprocessing and feature selection. Experiments show that the SSA-SVM diagnostic model effectively improves the accuracy of wind turbine fault diagnosis compared with the GS-SVM, GA-SVM and PSO-SVM models and has fast convergence speed and strong optimization ability. Moreover, the SSA-SVM diagnostic model can be used to diagnose faults in practical engineering applications.
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