三相配电系统代理模型的样本高效学习
Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System
AI总结：
本文提出一种定点数据驱动的代理模型，利用有限样本高效学习三相配电系统功率-电压关系，在IEEE 13、37和123节点系统中验证了其优于深度神经网络和高斯过程代理模型的预测精度与样本效率。
摘要：
准确预测配电系统电压的代理模型对于可靠的智能电网规划和运行至关重要。本文提出一种定点数据驱动的代理建模方法，利用有限数据集学习不平衡三相配电系统的功率-电压关系。所提出的代理模型基于定点潮流方程设计，并采用随机梯度下降法与自动微分技术，利用复功率和电压样本更新代理模型的参数。在IEEE 13节点、37节点和123节点系统中的数值算例表明，所提出的代理模型在预测精度和样本效率方面优于基于深度神经网络和高斯过程的代理模型。
英文摘要：
A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiency
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。
