论文标题
电力系统中的快速解决方案通过与数据驱动的电源流模型耦合进行电压估计
Fast Solutions in Power System Simulation through Coupling with Data-Driven Power Flow Models for Voltage Estimation
论文作者
论文摘要
电力系统求解器是计划，操作和优化电气配电网络的重要工具。当前的求解器采用计算昂贵的迭代方法来计算顺序解决方案。为了加速这些模拟，本文提出了一种新的方法，该方法用数据驱动的模型代替了基于物理的求解器，以替代模拟的许多步骤。在这种方法中，计算廉价的数据驱动模型从功率流求解器生成的训练数据中学习，用于预测系统解决方案。聚类用于为系统的每个操作模式构建单独的模型。开发了启发式方法，可以在每个步骤中选择模型和求解器，从而管理误差和速度之间的权衡。对于IEEE 123总线测试系统，该方法可通过避免使用86.7％的测试样本的求解器来减少典型的准稳态状态时间序列模拟的仿真时间，从而达到中位数预测误差为0.049％。
Power systems solvers are vital tools in planning, operating, and optimizing electrical distribution networks. The current generation of solvers employ computationally expensive iterative methods to compute sequential solutions. To accelerate these simulations, this paper proposes a novel method that replaces the physics-based solvers with data-driven models for many steps of the simulation. In this method, computationally inexpensive data-driven models learn from training data generated by the power flow solver and are used to predict system solutions. Clustering is used to build a separate model for each operating mode of the system. Heuristic methods are developed to choose between the model and solver at each step, managing the trade-off between error and speed. For the IEEE 123 bus test system this methodology is shown to reduce simulation time for a typical quasi-steady state time-series simulation by avoiding the solver for 86.7% of test samples, achieving a median prediction error of 0.049%.
