Smart Meter Analytics for Transformer Failure Prediction
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Solution Overview
Problem
The utility industry faces challenges in predicting imminent failure of electrical components, leading to increased risk and expense due to emergency outage restoration, and conventional maintenance techniques either incur extra costs or allow components to fail, disrupting service and posing environmental risks.
Innovation Solution
A system utilizing sensor data and advanced analytics, including artificial intelligence and machine learning, to analyze voltage and other data from electrical meters to predict asset failure, enabling proactive maintenance and reducing the likelihood of component failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If emergency outage restoration approach is used, then asset life is maximized, but service reliability deteriorates and risk increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring asset conditions through sensors and predicting potential failures before they occur. The predictive analytics platform analyzes historical and real-time data to identify assets at risk of failure, enabling proactive maintenance scheduling that prevents unexpected outages while maximizing asset utilization.
2Reliability
If scheduled maintenance approach is used, then service reliability is improved, but operational flexibility deteriorates due to planned outages
Solution Approach 1:
The maintenance schedule is made dynamic rather than static. The system continuously updates maintenance recommendations based on real-time asset condition monitoring and predictive analytics. This allows maintenance timing to be optimized dynamically, balancing reliability requirements with operational flexibility by scheduling maintenance during periods of lowest impact when assets are predicted to need it.
3Object-affected harmful factors
If condition-based maintenance approach is used, then risk is reduced, but cost increases due to early asset replacement
Solution Approach 1:
The system changes the decision parameter from fixed condition thresholds to dynamic predictive probability scores. Instead of replacing assets when they reach predetermined condition limits, the predictive analytics platform calculates the probability of failure within specific timeframes, enabling cost-optimal maintenance timing that reduces risk without premature replacement.
4Measurement precision
If predictive analytics system is implemented, then failure prediction capability is improved, but system complexity increases
Solution Approach 1:
The complex predictive analytics system is segmented into modular functional components: data collection layer with sensors, data processing layer with analytics platform, and decision support layer with maintenance recommendations. Each module operates independently with defined interfaces, allowing the system to achieve high prediction accuracy while managing complexity through modular architecture that can be implemented incrementally.
Data Source
AI summary
In some embodiments, systems and methods described herein are directed to using smart meters to determine an operational status of an asset. In some embodiments, the asset is a transformer. In some embodiments, the system receives data from the smart meters such as voltage and associates the data with an asset feeding electricity to the smart meter. In some embodiments, the system includes a data analytics platform that can generate a failure probability prediction using the smart meter data. In some embodiments, the system includes an AI model configured to receive smart meter data, execute a decision analysis, and return a designation of whether the asset is at risk of failure or has failed.