A ScienceDirect peer-reviewed paper published in May 2023, “Shedding Light on the Economic Costs of Long-Duration Power Outages: A Review of Resilience Assessment Methods and Strategies” highlights the effects these events have on the country’s power system. Unsurprisingly, the paper states, “Within the United States, many natural disasters cause long-duration outages such as hurricanes in the Southeast, freezing temperatures throughout the country, and wildfires, heat waves and earthquakes in the West. The impacts of these outages are numerous, including financial impacts, infrastructural impacts and societal impacts.”
Undergrounding is costly at an average of US$1.1 million per mile in urban areas and upwards of $6 million per mile in particularly challenging areas, according to a recent Guidehouse Insights article. Leading utilities like Florida Power & Light Co. (FPL), Dominion Energy Inc. and many others have determined the reliability improvement benefits are well worth the cost, with FPL citing 50% better day-to-day performance and 85% better performance during hurricanes.
While utilities see measurable reliability improvements on their strategically undergrounded distribution lines, they may not be aware system visibility through intelligent line sensing can maximize those improvements. When it comes to undergrounding initiatives, utilities should consider the following:
- Bathtub-curve system failure rates
- Challenge of finding and fixing underground faults
- Identification of overhead vs. underground faults at key transition points
- Load monitoring for more effective system planning and asset management.
Although overall fault frequency on underground distribution systems is significantly lower than with overhead lines, two reliability issues specific to undergrounding should be kept in mind:
1. As with other mechanical and electronic systems, underground circuits are susceptible to reliability issues like those represented by a bathtub curve. The bathtub-curve system failure rates for underground systems are higher in early and later life, while lowest in midlife. For new undergrounding projects, the concern is the relatively higher system fault rate in the early-failure period.
2. While underground outages are significantly less frequent, outages that do occur are typically longer. These faults are more difficult to locate and often take more time to repair. It is not uncommon for utilities to see outages on underground distribution systems last an average of 2.2 times longer than on overhead systems. With fault frequency being approximately one-ninth that of overhead lines, reliability expectations are understandably high for new undergrounding projects. However, a doubling in outage durations can be an unwelcome surprise to customers when a fault does occur.
The good news is reliability impacts associated with the bathtub curve and resulting underground fault location as well as the outage duration challenges can be effectively addressed with intelligent line sensing that provides near-real-time visibility and valuable system data.
Augment With Technology
Overhead and underground intelligent line sensors provide visibility of key points along distribution feeders. When deployed at transition points, overhead line sensors help utilities quickly to know whether faults are overhead or underground.
For example, visibility of underground distribution feeders can be improved with intelligent line sensors installed in switch cabinets or vaults. Downstream of the feeder switches, into single-phase underground residential distribution (URD), line sensors can be installed in transformer cabinets for enhanced residential system visibility. Both sensors wirelessly report faults, provide precise location data and load data to a central sensor management software application.
Fault Detection And Location
Fault detection and location information enables quicker responses and less patrol time, reducing outage durations. When utilities have a more granular view of their system, patrol areas are narrowed and crews are better equipped to address a fault. Line sensors are a proven method to improve utility reliability metrics, such as outage durations, system average interruption duration index (SAIDI), and operations and maintenance (O&M) costs.
Although line sensor data can be integrated with existing utility fault-finding processes in several ways, the simplest approach is to use line-sensing software to visualize the locations of fault indications and manually overlay them with supervisory control and data acquisition (SCADA), outage management system (OMS) and distribution management system (DMS) data. Direct integration of sensor data with the SCADA, OMS and DMS applications is a more streamlined method, providing a holistic view of system status. Line-sensing software must be flexible and have application programming interfaces (APIs) to enable this integration.
Integrating sensor data with advanced distribution management system (ADMS) applications, like fault location isolation and service restoration (FLISR) and distance-to-fault calculations, further increases the effectiveness of reliability improvements. In the FLISR scenario, sensors are paired with noncommunicating reclosers or feeder relays, allowing these devices to participate in FLISR schemes — delivering a dramatic reduction in SAIDI.
Utilities typically see improvements of 20% or more in customer minutes interrupted (CMI) on feeders with line-sensing deployments. The improvement is largely the result of a significant reduction in patrol time, with some utilities reporting upwards of 65% savings in patrol time. Reducing patrol time also saves on O&M costs. In fact, a large utility in the Southeast reports line sensing has yielded an 11% reduction in their O&M costs.
System planning teams do not always have load data for the locations they need to make the most effective planning decisions. Visibility of system load at additional points on the system provided by intelligent line sensors can fill in these data gaps. This data is especially important as distributed energy resources (DERs) and electric vehicles (EVs) are increasingly being added to power networks — creating significant changes in historic load curves and making load forecasting much more complex. Relying on load data from the substation alone is no longer sufficient.
Accurate load data from more system locations is essential for longer-term capacity planning. In the shorter term, load data is also critical for switching decisions during an outage to ensure circuits taking on new load do not become overloaded.
Yet another valuable benefit of visibility into system load data relates to system tuning. While the three phases of a feeder may look balanced at the substation, they can become significantly unbalanced at locations down the feeder. Leveraging line sensor data, planners can identify a location with phase imbalance and initiate action to rebalance the phases of the feeder to gain efficiency and reliability.
Current supply chain constraints and lengthy wait times for critical equipment now make what was once a commonly accepted practice of run-to-failure a risky approach to asset management. This, coupled with increased pressure for better grid reliability, necessitates a more proactive approach to asset management.
When added to URD transformers, line sensors not only detect faults on the primary side but also monitor transformer loading. This loading information enables utility engineers to use real system data to improve asset management and replace overloaded assets before they fail. EV charging is increasing the importance of transformer monitoring, as a transformer that was appropriately loaded in the past may become overloaded if multiple customers charge EVs simultaneously.
A Southwestern utility used line sensor data to determine one of its transformers was overloaded 150 times in eight months, with an average loading of 126% of its nameplate rating and a peak loading of 212% during this period. When a transformer is overloaded like that, there is not only risk of an outage but also more severe risk of a transformer catching fire.
If an overloaded transformer is identified and replaced as part of planned equipment upgrades, there are potential savings in CMI and O&M costs, plus reduced legal risk related to fires.
Savings Offset Cost
Remote system monitoring adds only an incremental 1% to 2% to the total undergrounding project cost. Increased savings associated with the resulting reductions in CMI and O&M costs more than offset the cost of the intelligent line sensors.
There is no doubt utilities can achieve meaningful reliability and resilience improvements through strategic underground initiatives. However, without proactively monitoring using intelligent line sensors for fault location and load data, utilities may be challenged to gain the full reliability benefits of underground distribution systems.
Steven Watt is senior product manager for Sentient Energy. He has worked in electric power distribution since 2012 in various roles in product management and marketing. Prior to that, Watt worked for HP in the IT market. He has an engineering degree from Virginia Tech.