  To reduce the heavy computational burden of reactive power optimization of
distribution networks, machine learning models are receiving increasing
attention. However, most machine learning models (e.g., neural networks) are
usually considered as black boxes, making it challenging for power system
operators to identify and comprehend potential biases or errors in the
decision-making process of machine learning models. To address this issue, an
explainable machine-learning framework is proposed to optimize the reactive
power in distribution networks. Firstly, a Shapley additive explanation
framework is presented to measure the contribution of each input feature to the
solution of reactive power optimizations generated from machine learning
models. Secondly, a model-agnostic approximation method is developed to
estimate Shapley values, so as to avoid the heavy computational burden
associated with direct calculations of Shapley values. The simulation results
show that the proposed explainable framework can accurately explain the
solution of the machine learning model-based reactive power optimization by
using visual analytics, from both global and instance perspectives. Moreover,
the proposed explainable framework is model-agnostic, and thus applicable to
various models (e.g., neural networks).
