Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S

Abstract

The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety.

Keywords

• Prediction
• power outages
• natural disasters
• classification
• support vector machine
• random forest