The high cost of operation and maintenance (O&amp;M) management has become an important factor hindering the sustainable development of the wind power industry. Performing accurate condition assessment of wind turbine&nbsp;components to optimize the structural design and O&amp;M strategy has become a research trend. However, the random and varying operating conditions of wind turbines make this problem difficult and challenging. A Supervisory Control and Data Acquisition (SCADA) system collects signal that contains a large amount&nbsp; of raw and useful information from critical wind turbine sub-assemblies. Extract key information from the SCADA data is an economical and effective way for condition assessment. A real-time reliability assessment method of wind turbine components using a Back-Propagation Neural Network (BPNN) and SCADA data is presented in this paper. The normal behavior models are established with the processed SCADA data, and the real-time reliability of wind turbine components are assessed based on the prediction result. For verification, the BPNN-based reliability assessment method is applied to a gearbox with real SCADA data a 1.5MW onshore wind turbine located along the southeast coast of China. The results show the capability of the proposed model in assessing the reliability of wind turbine components continuously and in real time.

Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and Scada Data

Paper Proceedings: https://asmedigitalcollection.asme.org/MSEC/MSEC2021/volume/85062

2021 International Manufacturing Science and Engineering Conference (MSEC2021)

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