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A computationally efficient method for identifying network parameter errors

机译:用于识别网络参数错误的计算有效方法

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A recently proposed method that is based on the normalized Lagrange multipliers for identifying network parameter errors has shown to be very effective yet also carries a high computational burden prohibiting its efficient use for large transmission grids. This paper presents an efficient solution that greatly reduces this burden facilitating the method's implementation in large practical power systems. The reduction in the computational cost is achieved by exploiting the sparse structure of the jacobian and gain matrices. The necessary subset of entries of the inverse matrix is determined by a two-step back stepping logic, and they are computed by a modified version of the well-known “sparse inverse” algorithm. Simulation results show that the proposed approach drastically reduces the computational load. Scenarios with different types of parameter errors are simulated to illustrate the application of the proposed implementation for very large size power systems.
机译:基于用于识别网络参数误差的归一化拉格朗日乘数的最近提出的方法已经显示出非常有效,并且还具有高计算负担,禁止其对大型传输网格的有效使用。本文提出了一种有效的解决方案,极大地减少了这种负担,促进了该方法在大型实用电力系统中的实现。通过利用雅加诺和增益矩阵的稀疏结构来实现计算成本的减少。逆矩阵的必要子集由两步后踩踏逻辑确定,并且它们由众所周知的“稀疏逆”算法的修改版本来计算。仿真结果表明,该方法大幅减少了计算负荷。模拟具有不同类型参数误差的场景以说明建议实现非常大尺寸的电力系统的应用。

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