基于径向基神经网络的低压台区运行状态综合评价方法

Comprehensive Evaluation Method of Low-voltage Transformer Area Operation State Based on Radial Basis Function Neural Network

为提升低压台区管理水平,指导基层运维人员及时发现台区问题,提出基于径向基神经网络的低压台区运行状态综合评价方法,该方法首先利用改进K-means算法对低压台区进行分类,然后获取台区运行状态评价指标体系集,综合序关系分析及加权求和计算历史台区评价值,最后以评价值为训练样本,得到径向基神经网络评价模型,利用该模型开展在运台区状态评价。通过实例分析表明,该综合评价方法能有效开展台区运行状态评价,及时发现线路及计量设备故障或异常问题,为开展低压台区运行问题治理,提升台区管理水平提供技术支撑。

In order to improve the management level of the low-voltage transformer area and guide the grass-roots operations and maintenance staffs found the transformer area problems in time,a comprehensive evaluation method of the operation state of the low-voltage transformer area based on the radial basis function neural network was proposed.In this method,firstly,the improved K-means algorithm was used to classify the low-voltage transformer area.Then the evaluation index system set of the operation state of the transformer area was obtained,the comprehensive order analysis and the weighted sum calculation of the historical transformer area evaluation were carried out.Finally,the evaluation value was taken as the training sample,and the radial basis function neural network evaluation model was designed.And the model was used to carry out the state evaluation in the operation area.The example analysis shows that the comprehensive evaluation method can effectively carry out the operation state evaluation of the transformer area,timely find out the fault or abnormal problem of the line and metering equipment,and provide technical support for the operation problem management of the low-voltage transformer area and the improvement of the management level of the transformer area.