基于数据的高维空间中功率流雅可比矩阵估计
Data-driven Estimation of the Power Flow Jacobian Matrix in High Dimensional Space
AI总结：
本文研究了在高维空间中利用数据驱动方法估计功率流雅可比矩阵，通过理论分析和模型计算获得基准值，并详细探讨了基于最小二乘误差和神经网络偏差估计的算法，分析了其理论、方程、推导、代码、优缺点及应用场景。
摘要：
雅可比矩阵是功率流分析的核心部分，是电力系统规划和运行的基础。本文在高维空间中估计雅可比矩阵。首先，介绍了雅可比矩阵的理论分析和基于模型的计算，以获得基准值。然后，详细研究了基于最小二乘误差的估计算法和基于神经网络的偏差估计算法，包括理论、方程、推导、代码、优缺点和应用场景。所提出的算法是数据驱动的，并且对最新的拓扑参数和状态变量敏感。通过将结果与基准值进行比较来验证这些努力。
英文摘要：
The Jacobian matrix is the core part of power flow analysis, which is the basis for power system planning and operations. This paper estimates the Jacobian matrix in high dimensional space. Firstly, theoretical analysis and model-based calculation of the Jacobian matrix are introduced to obtain the benchmark value. Then, the estimation algorithms based on least-squared errors and the deviation estimation based on the neural network are studied in detail, including the theories, equations, derivations, codes, advantages and disadvantages, and application scenes. The proposed algorithms are data-driven and sensitive to up-to-date topology parameters and state variables. The efforts are validate by comparing the results to benchmark values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
