Study on the Application of Self-Organizing Map in the Prognostic and Health Management of Wind Turbine
碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 104 === The study builds a prognostic and health management process with self-organizing map method to analyze the wind turbine data. The process of prognostic and health management includes “Data Processing”, “Feature Extraction”, ”Health Assessment”, and ”Perfor...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/83527491560224466842 |
| Summary: | 碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 104 === The study builds a prognostic and health management process with self-organizing map method to analyze the wind turbine data. The process of prognostic and health management includes “Data Processing”, “Feature Extraction”, ”Health Assessment”, and ”Performance Prediction”. The data processing excludes the unusual data according to the normal operating standard. The feature extracting extracts the well features by professional experience and decreases the orders of the data by principal component analysis. The Self-Organizing Map is used to analyze the processed data and Minimum Quantization Error as the health index of the wind turbines is set. Finally, the future health tendency of the wind turbine is predicted by autoregressive moving average model. The analysis set a health index MQE and a threshold value from the SCADA data of wind turbine. The voice data from turbine blades and temperature data from nacelle can used to detect the abnormal operation of the wind turbine. The prognostic and health management process can be used to predict the unnormal operations of the wind turbine.
|
|---|