Estimation on Wind Speed and Status AssessmentUsing Kalman Filtering

• Main Authors: CHANG,ZONG-YI, 張榕帟
• Other Authors: WANN,CHIN-DER
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/65g4v7

碩士 === 國立高雄第一科技大學 === 電腦與通訊工程系碩士班 === 106 === Wind speed measuring are used in many environments where winds are too large to cause disasters, so detecting wind speed is one of the most important factors for safety. Current warning system works out with an average or instant maximum wind speed as a parameter. The measurement data might be desalinated or activated by detecting maximum wind speed and lead to tragedy without making a current decision. To improve the accuracy or lower the probability of misjudgment, we use Kalman Filter to estimate wind speed and wind acceleration on its recursive estimation and correction formula. Average wind speed estimation might have credibility problem by averaging critical values. It could cause the detention and inappropriate to the warning system and make a unsuitable decision of current situation. After Kalman Filtering, wind speed and wind acceleration were used as decision parameter. We observed the untness using average or instant maximum wind speed as a decision parameter from different experiment. The wind parameter after Kalman Filtering can be suitable to different kinds of situations. The results show three different kinds of wind parameter processed by Kalman Filter and combined with sliding window signal processing giving suitable feedback to reduce the probability of misjudgment, avoiding the dangerous to drive from strong wind. By using the proposed decision assisted system, we can improve the accuracy of warning system and improve the safety factors of related applications.