According to the American Society of Civil Engineers[1], and virtually every professional in the power industry, the U.S. power infrastructure is an aging and complex patchwork of generation, transmission, and distribution lines. What’s more, the distribution system accounts for 92% of all electric service interruptions as storms, equipment failure or vandalism disrupts the grid. Add increasing stress on the grid brought on by extreme weather and the adoption of DERs and EVs, and improved grid reliability has quickly become essential for most utilities.
Advanced grid modernization technologies such as intelligent line sensors and analytics, address several aspects of grid reliability at once, providing quick time-to-value in three key ways:
- Enhanced fault detection
- Improved load monitoring and management
- Anomaly detection for proactive maintenance and improved power quality
While Intelligent line sensing with advanced analytics addresses a variety of concurrent grid reliability challenges, it also enables utilities to pursue multiple ROI streams that can quickly justify the investment.
Fault Detection and Location for Reduced Outage Durations
Overhead and underground line sensors provide enhanced system visibility along feeders and laterals. With remote communications that provide visibility, sensors are often deployed on overhead lines, underground cables, in padmounted or vault switches, and in underground residential (URD) transformers.
With a more granular view of their distribution grid system, patrol areas are narrowed when a fault is detected, and crews are better equipped to address the fault. Line sensors and communicating faulted circuit indicators (FCIs) 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.
Line sensor data can be integrated with existing utility fault-finding processes in several ways, the simplest being the use line sensing software to visualize the locations of fault indications. This data can be easily compared to SCADA/OMS/DMS data for more precise diagnosis and location detection. Direct integration of sensor data with utility SCADA, OMS, and DMS systems, for a holistic view of system status, is an even more streamlined method. This integration is possible by using system APIs, present in the most sophisticated line sensors and analytics technologies.
A reduction in customer minutes interrupted (CMI) by as much as 20% is achievable when sensor data is integrated with advanced distribution management system (ADMS) applications like fault location isolation and service restoration (FLISR) or distance-to-fault calculations. In the FLISR scenario, sensors are paired with non-communicating reclosers or feeder relays allowing these devices to participate in FLISR schemes.
The improvement is largely due to a significant reduction in patrol time, with some utilities reporting upwards of 65% patrol time savings. Reducing patrol time also saves O&M costs. In one case, a large utility in the southeast achieved an 11% reduction in O&M costs by using line sensing.
As sensor data is integrated with supervisory control and data acquisition (SCADA) or ADMS applications, the value of sensor data increases even further — accelerating time-to-value for the utility.
Load Monitoring for Improved System Planning
Accurate system load data provides critical information for effective planning decisions. Visibility into system load data from intelligent line sensors is especially important as DERs and EVs are increasingly added to the power grid. Â These devices create significant changes in load curves and make load forecasting much more complex. Relying on load data from the substation alone is no longer sufficient.
Load data from more system locations is essential for long term capacity planning. And in the shorter term, load data is critical for switching decisions during an outage to ensure that circuits taking on new load do not become overloaded.Â
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 line. For example, in the graph below, planners can identify a location with phase imbalance . This allows them to initiate actions that rebalance the phases of the feeder to gain efficiency and reliability.
Â
Additionally, utilities are looking for a more proactive approach to asset management on the grid. When added to underground residential distribution (URDs) transformers, line sensors not only enable utilities to detect faults on the primary side, but they also monitor transformer loading. This loading information helps utility engineers 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.
Â
The graph above from a large southwestern utility in the U.S. shows line sensor data indicating a transformer was overloaded. In fact, this transformer was overloaded 150 times in eight months with an average loading of 126% of its rating, and a peak overload of 212%. Overloading of transformers increases the risk of an outage, and more importantly the 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.
Anomaly Detection for Proactive Maintenance Through Analytics and Cloud Computing
Preempting outages due to vegetation contact and equipment failure provides significant system average interruption frequency index (SAIFI) improvements and O&M savings. And by preventing future outages utilities also improve long term SAIDI metrics.
A vast amount of data is collected when capturing system anomalies. Analytics modules used to gain insights from this data require machine learning and computing resources making cloud deployment necessary for analytics. When analytics and machine learning are applied to line sensor data, utilities can proactively identify likely grid faults and conduct preventative maintenance. To gain the ROI of this anomaly detection, line sensor data integration software must also be flexible enough to integrate with cloud analytics components.Â
Anomaly data is typically collected for three to twelve months to establish a baseline. During this time, line sensors detect waveform anomalies and AI-driven analytics models identify those that are precursors to faults. Daily reporting alerts of anomalies caused by vegetation that is in contact with power lines, or equipment failures, call attention to feeder segments with high levels of precursor anomalies so utilities can take action to preempt costly faults.
One of the costliest operations and maintenance challenges is vegetation management. Budgets for vegetation management can exceed $100 million annually for larger utilities. Using line sensors and analytics to predict faults associated with vegetation and line contact is a new strategy that can reduce the risk of faults, outages, and ignition due to vegetation contact with lines.
Failures of equipment such as insulators, cut outs, lightning arrestors, transformers, etc. is another significant line item in most operations and maintenance budgets. Line sensing, data integration, and analytics provide clear indicators of impending equipment failure, allowing utilities to prevent unplanned outages by taking preemptive action.
Final thoughts: A Diversified Approach is Most Effective for Improving Grid Reliability
The complexity of today’s distribution grid calls for reliability solutions that improve power delivery in multiple ways. Intelligent line sensing with analytics offers a multi-pronged and quick time-to-value approach to improving reliability now and into the future.
Fault detection and load monitoring provide immediate ROI to operations and system planning teams. These benefits include reduced outage durations and O&M costs, and better, data-informed planning decisions for switching, load balancing, and asset management.
And while those quick time-to-value benefits are playing out, the third opportunity for reliability improvement, anomaly detection and proactive maintenance, can begin to systematically predict outages before they happen. Achieving high probability prediction takes time and effort but the potential payback in reliability improvements and customer satisfaction can be a game changer.
When planning your utility’s grid modernization investments, technologies that provide multiple reliability value streams help you deliver more easily measured ROI. A strategy that balances near-term utility and customer gains and long-term goals can achieve both reliability and cost savings goals.
Â
Learn more about line sensors & analytics.
Â
Â
Â
Â