本文選題：水電機組 + 軸心軌跡　； 參考：《排灌機械工程學報》2017年12期

【摘要】：為了提高水輪機組診斷的精確性,提出應用時間序列模糊貼近度特征提取軸心軌跡特征參數(shù),通過改進SVM模型并引入故障分類準確性判定因子對參數(shù)化的水電機組軸心軌跡開展了智能診斷.應用改進SVM對時間序列特征引入正確率、錯誤分類率計算方法,從而對診斷后軸心軌跡分類準確性進行判定,由此促進運行狀態(tài)設備智能診斷,提高故障診斷系統(tǒng)的自動診斷水平及準確率;引入多類分類支持向量機算法、分類準確度判斷解決異常狀態(tài)下機組軸心軌跡特征參數(shù)無法識別、識別率低的問題.通過對改進擴展時序距離時間序列貼近度度量算法的應用解決了水電機組實時軸心軌跡特征參數(shù)準確性差和實時性差的問題.該方法提高了檢測精度,同時增強了人機交互性,具有重要的理論意義和實用價值.
[Abstract]:In order to improve the accuracy of hydraulic turbine diagnosis, the feature of fuzzy closeness degree of time series is applied to extract the characteristic parameters of axis locus. By improving the SVM model and introducing the accuracy factor of fault classification, the intelligent diagnosis of the axis locus of the parameterized hydropower unit is carried out. The improved SVM is used to calculate the correct rate and error classification rate of the time series features, so as to judge the accuracy of the axial trajectory classification after diagnosis, thus promoting the intelligent diagnosis of the running state equipment. To improve the automatic diagnosis level and accuracy of fault diagnosis system, the multi-class classification support vector machine algorithm is introduced to determine the classification accuracy to solve the problem that the characteristic parameters of the axis track of the unit can not be identified and the recognition rate is low under abnormal condition. Through the application of the improved time series closeness measurement algorithm of extended time series, the problems of poor accuracy and real-time performance of the characteristic parameters of the real time axis trajectory of hydropower units are solved. This method improves the accuracy of detection and enhances the human-computer interaction, which has important theoretical significance and practical value.
【作者單位】： 蘭州工業(yè)學院電氣工程學院;國網(wǎng)青海省電力公司電力科學研究院;青海師范大學;
【基金】：國家自然科學基金資助項目(51769012) 甘肅省科技計劃資助項目(1506RJZA059)
【分類號】：TV738

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本文編號：2030947