Objective
This project aims to provide improved understanding of underground transmission system (UGT) materials, component, and subsystem performance utilizing available data to support utilities in adopting asset management approaches and decision making by:
- Collecting and analyzing industrywide data on cable system inspection, periodic testing (e.g., dissolved gas in oil analysis, insulation tests, partial discharge), and outage and maintenance work
- Developing, populating, and extracting information from industrywide UGT equipment databases to help quantify cable system component historical performance
- Assessing and understanding factors influencing asset performance
- Developing methodologies to assess and predict UGT performance and risk
Research Value
This project aims to provide:
- More effective use of existing infrastructure and data
- Early identification of type issues, reducing unplanned outages
- Improved reliability and availability using analyses based on actual asset health and risk to determine maintenance actions
- Reduced reliance on time-based maintenance
- Improved capital planning decisions based on industrywide equipment performance and failure data
- More accurate and timely knowledge about asset condition and life expectancy
These benefits help utilities make better informed decisions. Public benefits from this R&D may include improved service reliability from a reduction in unplanned outages and maintenance costs.
Approach
Implementing analytics for underground transmission system components and systems is challenging in that historical performance data for components and systems is relatively sparse. Much of this is attributable to limited inspection and routine testing opportunities. Nonetheless, these cable systems are often critical assets and necessitate data-driven decision making. To that end, this project aims to identify opportunities where existing data can be leveraged, in conjunction with advanced data analysis and machine learning methods, to better understand cable condition and incipient fault risk. The success of this effort is predicated on member data contributions and active engagement.
The program includes several interrelated and complementary research efforts:
UGT Asset Characterization and Performance Data Collection plans to support the identification and collection of data needed to support asset management through the following tasks:
- Develop and update data models for capturing test, diagnostics, performance, and failure data for use in industry and utility database applications and performance analysis. These models include lists of what data should be collected and what kind of analyses and decisions can be supported. Data models and definitions have been developed in previous years for extruded, pipe-type, and self-contained cables, joints, and terminations. These data models and definition will be reviewed and updated. If needed, new UGT component data models may be developed.
- Work with member utilities to ensure that collection of equipment performance and failure data is performed in accordance with guidelines to ensure that the correct data are gathered and documented in the correct format for subsequent analysis.
Insights from the Collection and Analysis of Industrywide Failure and Performance Data proposes to collect and analyze industrywide asset component failure and performance data with the goal of developing metrics such as failure rates. The metrics may help utilities better understand component and subsystem performance (e.g., joint and termination defects; oil-filled cable leak rates, etc.), identify outlier designs and optimize maintenance, repair/replacement decisions, and specification practices.
Applying Artificial Intelligence and Advanced Data Science to Readily Available Data to Support Underground Transmission Asset Management intends to evaluate the applications of various data science and artificial intelligence techniques to analyze alarm, inspection, and maintenance records to extract data for developing new metrics that could be applied to analyze various types of underground cables and components. Assessing the present condition and failure risk of underground cable systems and components is challenged by the paucity of useful and timely diagnostic test and inspection data. Gaining timely insights into cable system condition will need to leverage a variety of data sources that generally will not contain information in a directly usable form. Advanced data science and artificial intelligence tools and methodologies will need to be utilized to extract pertinent information from unstructured text, imagery, scanned documents, or other data sources.
Underground Transmission Line Risk Screening Methodology Development aims to develop a conceptual framework for a risk screening methodology to support UGT asset management. The goal is to provide analytical approach for developing an assessment of the condition of underground cable circuits, considering both individual components (such as conductors, pipes, terminations, pumping stations) and complete lines as an integrated system of components. Using industrywide data from condition assessment and failure modes and degradation research, subject matter expert experience, and other inputs (make, model, manufacturer, operating environment), a practical, holistic approach to underground transmission line risk assessment will be investigated. The results of this assessment may be utilized to identify cable circuits for more detailed field testing and evaluation.
Anticipated Deliverables
| Deliverable | Date |
|---|---|
| Tech Update: Guidelines forUnderground Transmission Asset Performance Data | December 2026 |
| Tech Update and InsightsShared Via Transmission Resource Center: Insights from the Collection and Analysis ofIndustrywide Failure and Performance Data | December 2026 |
| Tech Brief and Webinar: Applying ArtificialIntelligence and Advanced Data Science to Readily Available Data to SupportUnderground Transmission Asset Management | December 2026 |
| Tech Brief: Underground TransmissionLine Risk Assessment Methodology Development | December 2026 |