Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction ...Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction model is proposed in this study. In the proposed model, the interpretability is successfully introduced to extra-trees by analyzing and processing the paths of decision trees in extra-trees. As a result, the interpretability of the proposed model is reflected in the following three respects: it can output the importance, contribution, and threshold of weather variables at high risk. The importance of weather variables can help in developing a long-term risk prevention plan. The contribution of weather variables provides targeted operation and maintenance advice for the next prediction period. The threshold of weather variables at high risk is critical in further preventing high risks. Compared with the black-box machine learning risk prediction models, the proposed model overcomes the application limitations. In addition to generating predicted risk levels, it can also provide more guidance information for the risk management of weather-related failures.展开更多
文摘Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction model is proposed in this study. In the proposed model, the interpretability is successfully introduced to extra-trees by analyzing and processing the paths of decision trees in extra-trees. As a result, the interpretability of the proposed model is reflected in the following three respects: it can output the importance, contribution, and threshold of weather variables at high risk. The importance of weather variables can help in developing a long-term risk prevention plan. The contribution of weather variables provides targeted operation and maintenance advice for the next prediction period. The threshold of weather variables at high risk is critical in further preventing high risks. Compared with the black-box machine learning risk prediction models, the proposed model overcomes the application limitations. In addition to generating predicted risk levels, it can also provide more guidance information for the risk management of weather-related failures.
