Detailed analytics of feeder circuits are essential for utilities seeking to improve reliability, limit the impact of outages, and ultimately increase customer satisfaction. For many utilities, however, valuable data generated by feeders and collected in the Advanced Distribution Management System (ADMS) goes untapped.

This article examines how feeder analytics drive multi-faceted value for utilities “before and after the emergency,” enabling smarter storm response, data-driven preventive maintenance, and more.

Feeders are often interchangeably referred to as “distribution circuits.” This article provides a useful breakdown of the terminology distinction.

Why Utility Feeder Analysis Provides Valuable Intelligence for Data-Driven Decision Making

Feeder circuits are a core piece of power delivery infrastructure, and timely analytical details on feeder performance are essential to answer fundamental operational questions like “What are my worst performing circuits?” and “which circuits are experiencing a downward trend in performance?”

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Depending on the operational challenge at hand, utility personnel may need to answer this question for a current year, month, weeks, or during time related to specific events. The ability to answer fundamental infrastructure performance questions with reliable, readily-accessible analytics can enable a larger shift to data-backed decision making for maintenance prioritization, asset management, storm response and more.

For example, by generating a “weighted count” (with additional weight given to critical customers) for each feeder, a utility can rationally prioritize service restoration work based on factors beyond simply the actual number of customers affected.

Ideally, utilities should have the ability to quickly generate business intelligence such as the Top 10/20 worst-performing feeders and those with the highest concentration of critical customers. The feeders driving the most weighted outages can be prioritized for replacement or maintenance as part of continuous improvement programs. Or, in other cases, detailed metrics can help uncover instances where an asset is performing poorly due to an issue upstream in the distribution network. As asset management initiatives are executed, detailed feeder-level analytics are the perfect tool for tracking results (and demonstrating them to executive stakeholders). We take a deeper look at predictive analytics for asset management in our blog here.

Detailed feeder analytics are valuable for strategic planning, long-term maintenance, and urgent storm response. The same intelligence that can help decide where to dispatch technicians first in the wake of a storm can be used to determined which assets need to be prioritized for replacement in the next year’s capital budget, where maintenance programs are falling short, and where additional preventive maintenance spend could drive the most ROI. The ability to more precisely track and optimize feeder performance can lead directly to operational benefits like improved SAIDI/SAIFI metrics, happier customers, and happier regulators.

Enhanced feeder analytics capabilities are also valuable for paving the way to a next-generation grid, rich with distributed energy resources (DERs). For example, this article from UtilityDive examines how major California utilities are “working with DER providers to develop a methodology to identify where DERs can connect to the distribution system, and how much each feeder can handle. Those findings will be made public and inform a parallel proceeding on how to monetize the value of the DERs.” As alternative energy sources proliferate, the grid will only become more dynamic (we explore this topic in our blog here), and feeder analytics requirements will only grow more complex.

Implementing Enhanced Feeder Analytics Capabilities

As we have explored in this article, enhanced feeder analytics can drive value immediately through improved asset management and storm response, while preparing utilities for a more dynamic future grid. Today, however, many utilities are simply not taking advantage of the potential value of data generated by feeder circuits. Although the relevant operational metrics are all collected in a prototypical ADMS (Advanced Distribution Management System) implementation, this data often sits siloed and underutilized. Data streams from various assets are never systematically transformed into the sort of real-time operational intelligence that can support decision-making.

Without access to detailed, timely analytics, feeder management today generally relies heavily on experience-based management. In the face of outages, crews are typically dispatched based on rule-of-thumb prioritization. By contrast, improved feeder analytics facilitate a broader move to data-based decision making. Instead of educated guesses, operations, outage, and asset-management teams can leverage predictive analytics to move toward a more programmatic approach and, by improving reliability, also move to a more customer-centric approach.)