Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble

Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble.

Automated summary

This paper proposes a deep neural network ensemble model for outage estimation, which enhances the reliability of power distribution systems by partitioning the input space and training multiple neural networks. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning, and a comparative analysis with a previously published model further establishes the overall effectiveness of the deep neural network ensemble.