论文标题
通过多任务学习的变异认知概率流量流分析
Variation-cognizant Probabilistic Power Flow Analysis via Multi-task Learning
论文作者
论文摘要
随着电力电网中太阳能光伏发电的高渗透率的增加，电压拟波器和分支功率流的渗透率更高。在这种情况下，概率功率流（PPF）研究的目的是表征系统状态在随机功率注射方面的统计特性。为了避免PPF研究中涉及的重复功率流计算，本文利用回归算法和神经网络来提高估计性能并加快计算的速度。具体而言，基于每个总线处电压幅度的变化水平，我们开发线性回归或完全连接的神经网络，以近似AC AC功率流量映射。提出的多任务学习技术通过将电压差异的误差纳入损耗函数设计中，从而进一步提高了分支流量估计的准确性。在具有实际数据的IEEE-300和IEEE-1354总线系统上进行了测试，所提出的方法在估计电压拟光器和分支流方面具有更好的性能。
With an increasing high penetration of solar photovoltaic generation in electric power grids, voltage phasors and branch power flows experience more severe fluctuations. In this context, probabilistic power flow (PPF) study aims at characterizing the statistical properties of the state of the system with respect to the random power injections. To avoid repeated power flow calculations involved in PPF study, the present paper leverages regression algorithms and neural networks to improve the estimation performance and speed up the computation. Specifically, based on the variation level of the voltage magnitude at each bus, we develop either a linear regression or a fully connected neural network to approximate the inverse AC power flow mappings. The proposed multi-task learning technique further improves the accuracy of branch flow estimation by incorporating the errors of voltage angle differences into the loss function design. Tested on IEEE-300 and IEEE-1354 bus systems with real data, the proposed methods achieve better performance in estimating voltage phasors and branch flows.
