基于Hermite网络的低压配电网多源数据处理与融合模型

Multi-Source Data Processing and Fusion Model of Low Voltage Distribution Network Based on Hermite Network

低压配电网系统的不断升级和改造增加了电网中数据信息的复杂性,为了实时有效地处理获取的多源数据,提高计算速度和融合效率,对传统的神经网络进行改造,根据多项式插值和逼近理论提出一种Hermite正交基前向神经网络,并在此基础上搭建基于Hermite正交基前向神经网络的算法模型,最后在MapReduce框架下将算法并行化。通过仿真验证,发现所提模型对低压配电网多源数据的处理效率更高、计算速度更快、误差更小。因此,提出的基于Hermite正交基前向神经网络的低压配电网多源数据处理与融合模型能更好地满足低压配电网中对多源数据的实时处理要求。

The continuous upgrading and transformation of the low-voltage distribution network system has increased the complexity of the data information in the power grid.In order to effectively process the acquired multi-source data in real time and improve the calculation speed and fusion efficiency,this paper reforms the traditional neural network and proposes a Hermite orthogonal basis forward neural network based on the theory of polynomial interpolation and approximation.An algorithm model based on Hermite orthogonal basis forward neural network is built,and finally this algorithm is parallelized under the MapReduce framework.Through simulation verification,it is found that the model has higher processing efficiency,faster calculation speed and smaller error for multi-source data of low-voltage distribution network.Therefore,the multi-source data processing and fusion model of low-voltage distribution network based on Hermite orthogonal basis forward neural network proposed in this paper can better meet the real-time processing requirements of multi-source data in low-voltage distribution network.