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Fast dynamic voltage security margin estimation: concept and development

机译：快速动态电压安全保证金估算：概念与开发

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摘要

This study develops a machine learning-based method for a fast estimation of the dynamic voltage security margin (DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a better measure of security than the more commonly used static voltage security margin (VSM). Using the concept of transientP - Vcurves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcome the computational difficulties in estimating the DVSM, this study proposes a method based on training two separate neural networks on a data set composed of combinations of different operating conditions and contingency scenarios generated using time-domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase the computational efficiency in estimating the DVSM. The machine learning-based approach is thus applied tosupportthe estimation of the DVSM, while the actual margin is validated using time-domain simulations. The proposed method was tested on the Nordic32 test system and the number of time-domain simulations was possible to reduce with similar to 70%, allowing system operators to perform the estimations in near real-time.
机译：本研究开发了一种基于机器学习的方法，用于快速估计动态电压安全裕度（DVSM）。 DVSM可以在干扰后结合动态系统响应，并且通常提供比更常用的静态电压安全裕度（VSM）安全的更好的安全度。使用Transientp - Vcurves的概念，本研究首先建立和验证DVSM更喜欢静态VSM的情况。为了克服估计DVSM时的计算困难，本研究提出了一种基于训练两个单独的神经网络的方法，这些方法在由使用时域模拟生成的不同操作条件和应变场景的组合组成的数据集上。训练有素的神经网络用于改进搜索算法，并显着提高估计DVSM的计算效率。因此，基于机器学习的方法是应用TOSUPPORT的估计，而使用时域仿真验证实际边距。在Nordic32测试系统中测试了所提出的方法，可以减少与70％相似的时间域模拟的数量，允许系统运营商在近实时进行估计。

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