论文标题
国家通过信仰传播的智能电网电力流量的估计
State Estimation of Power Flows for Smart Grids via Belief Propagation
论文作者
论文摘要
信念传播是一种从统计物理和计算机科学中知道的算法。它提供了一种有效的方法来计算边际，涉及大量产品，这些产品被有效地重新排列到总和的嵌套产品中以近似边缘。它允许对国家控制和预测电网管理所需的电网差异进行可靠的估计。在IEEE网格的典型示例中，我们表明，信念传播不仅与状态估计本身的网格大小线性缩放，而且还促进并加速丢失数据的检索，并允许对测量单元进行优化的定位。基于信仰的传播，我们给出了如何评估其他算法（仅使用局部信息）是否足以适应给定网格的状态估计的标准。我们还展示了如何将信念传播用于粗晶功率网格，以朝着在将粗粒化版本集成到更大的网格中时减少计算工作的表示形式。它提供了将电网分配到区域的标准，以最大程度地减少不同区域之间流动估计的错误。
Belief propagation is an algorithm that is known from statistical physics and computer science. It provides an efficient way of calculating marginals that involve large sums of products which are efficiently rearranged into nested products of sums to approximate the marginals. It allows a reliable estimation of the state and its variance of power grids that is needed for the control and forecast of power grid management. At prototypical examples of IEEE-grids we show that belief propagation not only scales linearly with the grid size for the state estimation itself, but also facilitates and accelerates the retrieval of missing data and allows an optimized positioning of measurement units. Based on belief propagation, we give a criterion for how to assess whether other algorithms, using only local information, are adequate for state estimation for a given grid. We also demonstrate how belief propagation can be utilized for coarse-graining power grids towards representations that reduce the computational effort when the coarse-grained version is integrated into a larger grid. It provides a criterion for partitioning power grids into areas in order to minimize the error of flow estimates between different areas.
