Temperature-Driven Reliability Analysis of Power Grid Failures: A Weibull Distribution Approach To Outage Prediction and Mitigation - Simon Ahumah Ocansey; Marwan Bikdash (Journal of Statistical Theory and Applications (JSTA))
Abstract This study aims to elucidate the critical relationship between temperature variations and power outages. We propose to employ the Weibull distribution, a fundamental tool in reliability analysis, to assess the impact of temperature on power grid failures using the SigmaXL statistical tool. This research adopts a two-parameter Weibull distribution and we set our alpha ( $$\:\varvec{\upalpha\:}$$ ) value as 0.05 with maximum likelihood as the method of estimation. The research utilized a historic multi-sourced comprehensive data on electricity demand, temperature, and power outages of a complete year. We utilised Mean-Time-To-Failure as a crucial metric in reliability analysis, representing the average lifespan of a given asset or component, such as the power grid network. The estimated Weibull shape parameter (β = 3.662) was greater than 1. This suggests a “failure rate” (outage likelihood) that increases over time, potentially influenced by temperature. The estimated scale parameter (η = 32.614 days) translates to a mean time to failure (MTTF) of 29.416 days. This indicates that on average, power outages are expected to occur every 29.42 days, considering the temperature data. Finally, we found that time is directly proportional to cumulative failure probability in predicting power outage possibilities.