基于容积卡尔曼滤波的输电线路弧垂预测

Transmission line sag prediction based on cubature Kalman filter

输电线路弧垂是线路安全运维的重要指标,为减少因弧垂过大或过小而产生运行时的安全问题,必须要考虑输电线路在不同气候条件下的应力及弧垂值,因此输电线路弧垂的预测具有重要意义。针对这一现象,国内外学者采用神经网络等预测算法对弧垂进行评估,但在实现动态实时估计的方法较少。文中采用输电线路模型优化融合容积卡尔曼模型,实现在不同工况下输电线路弧垂模拟预测。该方法利用系统状态方程,通过对输入输出数据的观测,对系统的状态进行最优估计,在含有不确定因素的动态系统中对数据进行实时追踪并修正,估计出真实发生的情况。通过对几种工作状态的导线应力预测,并与真实情况进行对比得出该算法具有良好的精度及动态性,为恶劣天气下高压输电线路无人巡检和预防故障的发生提供可能。

The arc sag of the transmission line is an important indicator of the safety operation and maintenance of the line.In order to reduce the safety hazards caused by excessive large or excessive small sag in the operation,the stress and arc sag value of the transmission line under different climatic conditions must be considered.Therefore,the prediction of transmission line arc sag is of great significance.In view of this,domestic and foreign scholars use prediction algorithms such as neural networks to evaluate arc sag,but there are few methods to achieve dynamic real⁃time estimation.In this paper,the transmission line model is used to optimize the fusion cubature Kalman model to realize the simulation and prediction of transmission line arc sag under different working conditions.In this method,the system state equation is used to estimate the state of the system optimally by the observation of input and output data,and track and correct the data in real time in the dynamic system with uncertain factors to estimate the real occurrence.By predicting the wire stress of several working states and comparing it with the real situation,this paper shows that the algorithm has high accuracy and satisfied dynamics,which provides the possibility for unmanned inspection of high⁃voltage transmission lines and prevention of faults in bad weather.