How Power Outage Analytics Drive Lower Maintenance Costs for Utilities

Background

A major French electrical utility provider set out to better understand the root causes of equipment failures leading to both long and short power outages. With growing pressure to improve network reliability and optimise maintenance budgets, the company needed deeper insight into how factors such as asset age, material type, and geographic distribution were influencing outage frequency. By analysing these variables, the goal was to prioritise maintenance interventions more effectively and reduce overall operational costs.

Challenge

The client faced the task of transforming raw operational data into actionable insights to enhance network reliability and optimise costs. Their objectives included: - Calculating average yearly outage rates for both long and short interruptions - Identifying key factors driving outages, such as equipment age, materials used, and geographic conditions - Developing a predictive model to anticipate outages and inform proactive maintenance planning - Reducing overall maintenance costs while improving the reliability of the power system These challenges required not just data analysis, but a solution that could integrate multiple variables and generate predictive, operationally meaningful insights.

Approach

Keyrus began by constructing a comprehensive analysis database, consolidating and validating historical equipment data, including age, material type, cable length, alongside geographical information. The team then applied both univariate and multivariate analyses, using significance tests, correlations, and principal component analysis to identify the key factors driving outages. Next, a multiple regression model was developed to predict annual outage rates, enabling the client to anticipate potential failures and plan maintenance proactively. Finally, the results were visualised and monitored through automated reports, providing real-time insights into outage trends and risk factors, and supporting data-driven decision-making across the organisation.

Key results

Benefits

The project enabled the client to move from reactive to proactive maintenance, scheduling interventions before failures occur and reducing unexpected downtime. By identifying high-priority equipment for replacement, the utility also achieved significant annual maintenance cost savings while improving overall power system reliability.

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