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When it comes to monitoring asset health, utilities’ grid assets can be typically divided into two categories:

  • High cost assets, such as power transformers and turbines, which are usually monitored by multiple sensors.
  • Lower cost and more abundant assets, such as underground residential distribution (URD) cables and distribution transformers that are not usually monitored by sensors (or only partially monitored).
PREVENTING CATASTROPHIC TRANSFORMER FAILURE

Due to ever-increasing energy consumption, an aging grid infrastructure and the growing use of renewable resources, stress on power transformers is reaching potentially dangerous levels.

mPrest’s comprehensive Asset Health Management system provides real-time monitoring, powerful analytics, highly accurate predictions and alertsregarding the condition of transformers and other critical assets.

The system collects data from multiple transformer sensors (e.g., online DGA, temperature, load, acoustic, and RFI sensors), incorporates historical data and performs advanced analytics to detect abnormalities even when they occur between thresholds. Machine-learning algorithms let you identify situations where DGA levels are likely to cross a threshold soon, enabling real-time predictive maintenance.

By combining standards-based transformer testing with advanced analytics and predictive capabilities, mPrest helps you avoid unplanned downtime, lower maintenance costs and extend transformer lifetime.

Data-Driven Optimization of URD Cable Fleet Performance

URD cables may be decades old, and utilities have limited visibility into their condition. The lack of reliable data and insight on cable health makes it difficult to gauge cable survivability, prioritize maintenance and replacement activities, and predict impending failures.

Our Asset Health Management application uses big data analytics and AI-driven algorithms to optimize the management of operational URD cable assets and processes. Extracting URD cable segment data from enterprise asset management, SAP and/or GIS systems, specialized algorithms generate valuable insights and visualizations of the attributes that affect the survivability of URD cables.

Our product predicts the probability of failure per cable segment, and offers an interactive budgeting tool for building a proactive and preventive URD cable maintenance/replacement plan.

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