基于大数据技术的电缆健康监测与故障预测

Cable Health Monitoring and Fault Prediction Based on Big Data Technology

随着电缆使用年限的增加和环境因素的影响,电缆存在一定的老化和损耗,可能会出现性能下降甚至停电等严重后果。基于此,建立了电缆健康监测的数据模型,应用机器学习算法进行模式识别和异常检测。实时采集电缆的运行数据,并与模型进行比对分析,判断电缆是否处于正常工作状态或存在潜在故障隐患。同时,结合故障历史数据和维修记录,预测电缆故障的可能性和时间。通过监测和分析电缆运行数据,能够及时发现电缆的异常情况,并采取相应的措施进行维修和调整,以避免潜在故障的发生。该方法能够大幅改善电力系统的稳定性和可靠性,降低故障导致的停电时间和维修成本。

With the increase in the service life of cables and the influence of environmental factors,there is a certain degree of aging and loss of cables,which may lead to serious consequences such as performance degradation or even power outage.Based on this,a data model for cable health monitoring is established,and machine learning algorithms are applied for pattern recognition and abnormality detection.Real-time cable operation data is collected and compared and analyzed with the model to determine whether the cable is in normal working condition or there is a potential fault hidden danger.At the same time,the possibility and time of cable failure are predicted by combining fault history data and maintenance records.By monitoring and analyzing the cable operation data,it is possible to discover the abnormal situation of the cable in time and take corresponding measures to repair and adjust in order to avoid the occurrence of potential faults.The method can significantly improve the stability and reliability of the power system and reduce the outage time and maintenance cost caused by faults.