A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System With PVs
A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System With PVs
复制标题
数据驱动的光伏三相配电系统全局敏感性分析框架
DOI：
10.1109/tpwrs.2021.3069009
复制
发表时间：
2021
影响因子：
6.6
通讯作者：
Field, Thomas E.
中科院分区：
文献类型：
--
作者：
Ye, Ketian;Zhao, Junbo;Huang, Can;Duan, Nan;Zhang, Yingchen;Field, Thomas E.
配电系统对随机PV和负荷变化的全局灵敏度分析在设计最优电压控制方案中起着重要作用。本文提出了一种数据驱动的配电系统GSA框架。特别是，两个代表性的代理建模为基础的方法，包括传统的高斯过程为基础的和方差分析（ANOVA）内核的。其关键思想是开发一个代理模型，该模型从历史数据中捕获电压与真实的和无功功率注入之间的隐藏全局关系。利用代理模型，Sobol指数可以方便地通过基于采样的方法或分析方法来计算，以评估电压对负载和PV功率注入的变化的全局灵敏度。基于抽样的方法估计Sobol指数使用蒙特卡罗模拟，而分析方法计算他们诉诸方差分析扩展框架。在不平衡三相IEEE 37节点和123节点配电系统上与其他基于模型的GSA方法的比较结果表明，所提出的框架可以实现更高的计算效率，而精度损失可以忽略不计。在一个真实的240节点配电系统上使用实际的智能电表数据的结果进一步验证了所提出的框架的可行性和可扩展性。
Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.