Aadrit Talukdar

Improving predictions of power outages and determining resiliency of power grids on the West Coast utilizing machine learning and sensitivity analysis

Started May 9, 2023

Abstract or project description

Due to climate change and global warming, power outages have been increasing in frequency and severity. Such power outages not only cause disruptions to day-to-day life but also endanger hospital patients and people using power-reliant medical equipment at home. With such an increase in power outages, it becomes imperative to find a solution to mitigate the impacts and frequency of such power outages. This paper aims to analyze the relationships between different climate factors and power outages to determine which factors have the predominant impact. Machine learning techniques are used to develop predictive models to identify climate conditions that are likely to cause power grid failures. In addition, this paper also investigates the use of these models in analyzing the resiliency of power grids. This study is focused on the West Coast of the United States, primarily California. Climate factors such as temperature, precipitation, humidity, wind speed, TBD, along with power outage data from TBD-TBD are used in this analysis. TBD techniques and TBD models were developed to perform the analysis. TBD climate factors were found to be the predominant influencers in power outages when in TBD ranges. This research also shows that power grids that have taken preemptive actions against such climate influences are more robust and resilient. The results from this paper can be used to formulate strategies and policies to strengthen existing and future power grids on the West Coast. The processes and techniques utilized during this research can be applied to develop similar models for other geographical locations.