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).

提出了一个可解释的机器学习框架来优化配电网络中的无功功率，通过全局和实例视角的可视分析，能够准确解释基于机器学习模型的无功功率优化解决方案。

基于机器学习的配电网无功功率优化的可解释框架

An Explainable Framework for Machine learning-Based Reactive Power  Optimization of Distribution Network

Conventional static knowledge graphs model entities in relational data as
nodes, connected by edges of specific relation types. However, information and
knowledge evolve continuously, and temporal dynamics emerge, which are expected
to influence future situations. In temporal knowledge graphs, time information
is integrated into the graph by equipping each edge with a timestamp or a time
range. Embedding-based methods have been introduced for link prediction on
temporal knowledge graphs, but they mostly lack explainability and
comprehensible reasoning chains. Particularly, they are usually not designed to
deal with link forecasting -- event prediction involving future timestamps. We
address the task of link forecasting on temporal knowledge graphs and introduce
TLogic, an explainable framework that is based on temporal logical rules
extracted via temporal random walks. We compare TLogic with state-of-the-art
baselines on three benchmark datasets and show better overall performance while
our method also provides explanations that preserve time consistency.
Furthermore, in contrast to most state-of-the-art embedding-based methods,
TLogic works well in the inductive setting where already learned rules are
transferred to related datasets with a common vocabulary.

TLogic 是一个可解释的框架，它基于通过时间随机游走提取的时间逻辑规则，用于处理时间知识图中的链接预测。与现有技术相比，在具有共同词汇表的相关数据集上，TLogic 能够更好地解释和推理，并提供更好的综合性能。