重载铁路变电主设备关键状态参数趋势预测技术研究

Trend Prediction Technology of Key State Parameters of Main Substation Equipment in Heavy-Haul Railway

变电主设备在铁路系统中起着至关重要的作用,准确预测其状态参数的趋势对于设备的维护和运行至关重要。从状态参数选取、解释分析、采集和预处理等方面,阐述重载铁路变电主设备关键状态参数分析方法。在此基础上,分析重载铁路变电所场景下的设备状态分类评估、设备状态趋势预测、设备缺陷处置和设备检修时点评估等4类算法场景,提出一种基于机器学习和数据挖掘的趋势预测算法,并根据数据的特点,完善特征提取方法,提高模型的准确性和泛化能力。针对设备状态趋势预测场景,通过实证分析对提出的预测算法进行验证和评估。通过预测关键状态参数的趋势,可以及时采取维护和保养措施,从而降低设备故障率,提高变电所的运行效率和可靠性。

The main substation equipment plays an important role in the railway system.Accurately predicting the trend of its state parameters is significant for equipment maintenance and operation.From the aspects of state parameter selection,interpretation and analysis,acquisition,and pretreatment,this paper expounded the analysis method of key state parameters in the main substation equipment of heavy-haul railway.On this basis,it analyzed four kinds of algorithm scenarios,including equipment state classification evaluation,equipment state trend prediction,equipment defect disposal,and time point evaluation of equipment maintenance under the scenario of heavy-haul railway substation,and proposed a trend prediction algorithm based on machine learning and data mining.According to the data characteristics,the feature extraction method was improved to enhance the accuracy and generalization ability of the model.For the prediction scenario of equipment state trend,the proposed prediction algorithm was verified and evaluated by empirical analysis.By predicting the trend of key state parameters,maintenance measures can be taken in time to reduce the equipment failure rate and improve the operation efficiency and reliability of the substation.