Protected: Predictive Maintenance of the Electric Grid

This whitepaper explores how operational data, asset diagnostics, and engineering models can support earlier detection of developing problems and better maintenance planning. Discover how MATLAB®, Simulink®, and Simscape™ support the workflow from data preparation and asset-health assessment to predictive modeling, validation, and deployment.

Through practical applications and an implementation guide, you will gain a clearer understanding of suitable methods, the evidence needed to evaluate them, and how to define a viable predictive-maintenance pilot.

What You’ll Learn from the White Paper

Explore the engineering steps that turn available information into useful maintenance decision support:

• Data and Health Indicators – Combine operational measurements, asset diagnostics, maintenance history, and environmental information to develop meaningful indicators of asset health.

• Analytical and Modeling Methods – Understand when trend analysis, machine learning, physics-based models, or hybrid approaches fit the maintenance problem and available evidence.

• Validation for Maintenance Decisions – Evaluate warning time, false alarms, missed detections, and uncertainty against the needs of the intended maintenance action.

• Practical Applications – See how transformer oil-level monitoring, underground-cable partial-discharge diagnostics, and simulation-based power-converter ageing assessment apply these methods.

• Maintenance Prioritization – Combine health assessments and predictions with asset criticality, failure consequences, and intervention constraints.

• From Pilot to Deployment – Select a feasible first use case, define success criteria, test the workflow in operation, and assess whether wider deployment is justified.

Why This Matters

Earlier insight into asset health gives grid operators more time and a stronger basis for action. A well-designed predictive-maintenance workflow can help you:

• Identify developing problems early enough to plan diagnostics, personnel, spare parts, and outages.
• Focus inspections and maintenance resources on assets that need attention.
• Support life-extension, refurbishment, and replacement decisions with asset-specific evidence.
• Reduce exposure to unplanned interventions and support greater asset availability.

What You’ll Learn 

Explore the engineering steps that turn available information into useful maintenance decision support: 

• Data and Health Indicators – Combine operational measurements, asset diagnostics, maintenance history, and environmental information to develop meaningful indicators of asset health. 
• Analytical and Modeling Methods – Understand when trend analysis, machine learning, physics-based models, or hybrid approaches fit the maintenance problem and available evidence. 
• Validation for Maintenance Decisions – Evaluate warning time, false alarms, missed detections, and uncertainty against the needs of the intended maintenance action. 
• Practical Applications – See how transformer oil-level monitoring, underground-cable partial-discharge diagnostics, and simulation-based power-converter ageing assessment apply these methods. 
• Maintenance Prioritization – Combine health assessments and predictions with asset criticality, failure consequences, and intervention constraints. 
• From Pilot to Deployment – Select a feasible first use case, define success criteria, test the workflow in operation, and assess whether wider deployment is justified. 

Download the white paper

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MATLAB®, Simulink®, and Simscape™ support predictive-maintenance workflows from data preparation and asset-health assessment through modeling, simulation, validation, and deployment.

• MATLAB®
• Simulink® and Simscape™

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