Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit
Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit
复制标题
DOI：
10.1109/tpwrs.2023.3248941
复制
发表时间：
2023-02
影响因子：
6.6
通讯作者：
Robert I. Hamilton;P. Papadopoulos
中科院分区：
文献类型：
--
作者：
Robert I. Hamilton;P. Papadopoulos
由于可再生能源接入电力系统导致计算需求大幅增加，用于暂态稳定评估的机器学习（ML）受到了关注。为了达到高度的准确性，通常需要黑箱式的ML模型——这阻碍了对预测的解释，从而降低了对使用此类方法的信心。本文提出使用SHapley可加性解释（SHAP）——一种基于合作博弈论中的Shapley值的统一可解释性框架——来深入了解经过训练用于预测临界切除时间（CCT）的ML模型。我们使用SHAP来获取针对特定位置的ML模型的解释，这些模型经过训练用于预测网络中每个母线的CCT。这可以在系统复杂性和不确定性不断增加的情况下，为影响整个稳定边界的电力系统变量提供独特的见解。随后，感兴趣的变量与来自每个特定位置ML模型的相应SHAP值之间的协方差——可以揭示该变量的变化如何影响整个网络的稳定边界。这些见解可以为规划和/或运行决策提供信息。所提供的案例研究在具有IV型风力发电的IEEE 39节点测试网络中使用一种高度准确的不透明ML算法演示了该方法。
Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems. To achieve a high degree of accuracy; black-box ML models are often required – inhibiting interpretation of predictions and consequently reducing confidence in the use of such methods. This paper proposes the use of SHapley Additive exPlanations (SHAP) – a unifying interpretability framework based on Shapley values from cooperative game theory – to provide insights into ML models that are trained to predict critical clearing time (CCT). We use SHAP to obtain explanations of location-specific ML models trained to predict CCT at each busbar on the network. This can provide unique insights into power system variables influencing the entire stability boundary under increasing system complexity and uncertainty. Subsequently, the covariance between a variable of interest and the corresponding SHAP values from each location-specific ML model – can reveal how a change in that variable impacts the stability boundary throughout the network. Such insights can inform planning and/or operational decisions. The case study provided demonstrates the method using a highly accurate opaque ML algorithm in the IEEE 39-bus test network with Type IV wind generation.