Dynamic Model Identification for Inverter-Based Resources

从历史上看，同步发电机，发明于1880年代，是主要的工作马发电。目前，同步电机的电网动态分析通用模型已经比较成熟，是电网动态分析不可缺少的工具。目前，电网正在经历重大转型。基于逆变器的资源（IBR）的渗透，例如，风能，太阳能，电池，都在增长例如，美国能源信息署每小时的电网监测显示，2020年5月2日，德克萨斯州电力可靠性理事会59%的电力由风能产生。IBR显著改变了电网的动态特性。电网中出现了前所未有的动态现象。例子包括2009年和2017年在德克萨斯州风电场发生的次同步振荡，2019年8月9日导致英国电力中断的海上风电场低频振荡，2017年和2018年在加州引发大规模太阳能光伏（PV）跳闸的子周期过电压动态，以及瞬时交流过流动态，导致2020年7月在加州发生另一起大规模光伏跳闸事件。IBR的高渗透率导致了大容量电力系统行业建模实践的重大变化。预计具有标准结构的通用模型将作为未来的趋势用于网格动态评估。另一方面，原始设备制造商（OEM）提供的模型是黑匣子。OEM提供的实码模型通常采用动态链接库格式，指定输入和输出，但不发布内部细节。虽然这些实码模型已经通过硬件实验进行了基准测试，但详细的结构和参数是未知的。因此，有一个迫切的需要：如何找到通用模型的参数的基础上，我们有什么，这基本上是黑盒子？建议的研究的目标是解决迫切需要设计一个灰箱模型识别框架IBRs。本项目的创新点主要有两点：（1）基于第一性原理的IBR灰箱模型结构设计和线性时不变模型推导;（2）基于观测的IBR模型结构参数估计，即基于黑箱模型辨识和先进的计算算法。值得注意的创新是来自两个不同领域的两项核心技术的整合：电力电子和系统识别。 两大核心技术是：基于导纳的黑箱模型测量和识别以及通过优化问题公式化和求解的灰箱模型识别。该项目采用计算机仿真和硬件实验进行验证。该项目解决了现实世界的IBR建模挑战，并可能对当前的电网行业产生重大影响。所解决的问题福尔斯属于灰箱模型辨识的范畴，本研究也将对许多其他领域产生参考价值（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Historically, synchronous generators, invented in 1880s, are the main work horse for electricity generation. To date, generic models of synchronous machines for power grid dynamic analysis are well developed and indispensable for power grid dynamic analysis. The current power grid is going through a significant transition. Penetration of inverter-based resources (IBR), e.g., wind, solar, and batteries, is going up. For example, the U.S. Energy Information Administration’s hourly electric grid monitor shows that on May 2, 2020, 59% of power was generated by wind energy in Electric Reliability Council of Texas. IBRs significantly change grid dynamic characteristics. Unprecedented dynamic phenomena appeared in power grids. Examples include subsynchronous oscillations that occurred in 2009 and 2017 in Texas wind farms, low-frequency oscillations in an offshore wind farm that caused Great Britain power disruption on 9 August 9 2019, subcycle overvoltage dynamics which triggered large-scale solar photovoltaic (PV) tripping in California in 2017 and 2018, and instantaneous ac overcurrent dynamics which caused another large-scale PV tripping event in California in July 2020. High penetration of IBRs leads to a significant change in the modeling practice of the bulk power system industry. It is expected that generic models with standard structures will be used for grid dynamic evaluation as a future trend. On the other hand, models provided by original equipment manufacturers (OEMs) are black boxes. Real-code models provided by OEMs are usually in dynamic-linked library format with input and output specified while internal details not released. Though these real-code models have been benchmarked by hardware experiments, detailed structures and parameters are unknown. Thus, there is an urgent need: How to find the generic model’s parameters based on what we have, which are essentially black boxes?The objective of the proposed research is to address the urgent need to design a gray-box model identification framework for IBRs. This project makes two significant innovations, including (i) first principle based IBR gray-box model structure design and linear time invariant model derivation; and (ii) measurement-based IBR model structure parameter estimation relying on black-box model identification and advanced computing algorithms. The notable innovation is the integration of the two core technologies from two different fields: Power Electronics and System Identification. The two core technologies are: admittance-based black-box model measurement and identification and gray-box model identification through optimization problem formulation and solving. The project employs both computer simulation and hardware experiments for validation. This project tackles real-world IBR modeling challenges and can generate significant impact to the current power grid industry. The tackled problem falls into the category of gray-box model identification and this research will also generate reference values to many other domains (e.g., automotive systems and airplane systems) that use gray-box model identification approaches for model building and control design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.