Multi-scale operation and maintenance of renewable energy storage systems: Integrating ChatOps with Bayesian network algorithms

This research paper outlines an intelligent facility-management system (O&M) framework for renewable battery-storage devices used as components in smart power systems. It describes how to use ChatOps integrated into a Bayesian network algorithm. A ChatOps chat room using the messaging tool Slack has been created. This chat room allows for real time monitoring and interaction with the O&M system in addition to monitoring and interacting with devices. Additionally, the O&M system uses the K2 structure-learning algorithm to develop Bayesian networks for the system. By developing the K2 structure-learning algorithm, we have achieved an average prediction accuracy of 0.95 and an average time response of 12 s when predicting faults on multi-scale O&M systems.