基于神经网络的状态估计方法研究

Research on state estimation based on artificial neural networks

智能电网建设的快速推进,导致状态估计算法所处理的数据量急剧增加。串行状态估计算法求解速度慢,无法满足电力系统实时分析的要求;而并行状态估计方法需要大规模计算集群的支持,会占据大量的硬件资源并产生高能耗。为解决上述问题,提出一种基于神经网络的状态估计方法。该方法以离线方式搭建并训练神经网络。在状态估计的实际计算中,以神经网络的前向计算代替传统算法中的迭代最小二乘拟合,从而大幅减少状态估计算法的执行时间。由于神经网络的前向计算所需时间很短,即使处理大规模电网,提出的方法仍可在单机平台上运行,从而避免使用大规模计算集群所需的能耗。同时,神经网络自身的高容错性还能有效地修正量测数据中的误差。实验结果表明,与串行方法相比,所提方法计算速度提升了约205倍。

With the rapid progress in smart grid,data required to be processed by the state estimation algorithm increases sharply.However,the existing serial algorithms suffered from lower computing speed and cannot meet the requirement of real-time analysis;while deployment of the parallel algorithms needs large scale computing cluster which occupies huge amount of hardware resources and resulting in high energy cost.To overcome above mentioned problems,this paper proposes a Neural Network(NN)based state estimation algorithm.The proposed algorithm constructs and trains the neural network in an offline manner.While solving the actual estimation problem,the iterative least square fitting in traditional state estimation schemes is replaced with forward calculation of the trained neural network.This can reduce execution time of the overall state estimation algorithm significantly.Because the time consumed by forward calculation of the neural networks is very short,the proposed algorithm can still run on a single machine even confronting with large scale power grid,so as to avoid the energy consumption required for computing cluster.Moreover,the neural network has high robustness and can effectively correct the gross error in the measured data.The performance comparison demonstrates that the calculation speed of the proposed scheme is improved by 205 times compared with the serial algorithm.