Why advanced analytics in utilities depends on capturing the electrical signals already on the system
In May 2024, a series of short-duration disturbances began appearing on a distribution feeder serving a suburban corridor in the Midwest.
Customers weren’t losing power. Protection devices weren’t operating. Each event lasted only fractions of a second—too brief to trigger alarms or register in traditional outage systems.
But the disturbances kept repeating.
To experienced operations teams, this pattern is familiar. Momentary electrical disturbances often indicate developing issues—equipment beginning to fail, insulation breaking down, or intermittent contact from vegetation.
When crews inspected the circuit, they found the cause: damaged insulators had begun to arc intermittently under load. Each arc produced a brief electrical disturbance that propagated along the feeder before clearing itself.
The hardware was replaced before the issue escalated into a sustained outage.
What’s less obvious is this:
This type of activity is not rare. It is happening continuously across distribution systems—and largely going undetected.
The Real Constraint Behind Utility AI
Utilities are increasingly investing in advanced analytics and artificial intelligence to improve reliability, optimize asset performance, and manage growing grid complexity.
But a fundamental limitation is emerging:
Analytics systems cannot interpret signals that are never captured.
The grid is already producing the data needed for predictive operations. The challenge is that much of that data—particularly on distribution feeders—is not being observed.
Before utilities can fully realize the value of AI, they must first address a more basic issue:
Visibility into the electrical behavior of the distribution system.
The Distribution Visibility Gap
Utilities have strong visibility at the substation and across transmission networks.
SCADA systems provide real-time insight into voltage, current, breaker status, and power flow. Transmission systems are even more extensively instrumented, with high-resolution monitoring and synchronized measurements.
However, once power leaves the substation, visibility declines sharply.
Across most distribution networks, utilities rely on a combination of:
- Recloser and sectionalizing device data
- Limited feeder SCADA points
- AMI-based outage notifications
- Customer calls
- Periodic field inspections
These systems are essential—but they do not continuously capture how circuits are behaving electrically between events.
From an operational standpoint, this creates a structural gap.
Many of the conditions that lead to outages develop on parts of the grid that are not continuously monitored.
As a result, utilities are often forced into reactive operating modes—responding to outages rather than identifying the conditions that precede them.
The Grid Is Already Communicating
Electric power systems continuously generate electrical signatures that reflect system condition. Engineers see evidence of this in power quality and disturbance data:
- Insulation degradation can produce partial discharge or intermittent arcing
- Vegetation contact can create repeating momentary faults
- Loose hardware may generate irregular transient signatures
- Conductor movement can introduce phase imbalance and disturbance patterns
- Failing components can produce localized voltage and current anomalies
These events often occur in milliseconds. Individually, they may seem insignificant. But collectively, they form patterns that reveal developing reliability risks.
The distribution grid is not silent—it is constantly communicating its condition.
The limitation is not the absence of signals.
It is the absence of continuous visibility into those signals.
From Data Scarcity to Data Relevance
Utilities exploring AI-driven applications—predictive maintenance, anomaly detection, reliability forecasting—require a specific type of data foundation. For distribution systems, that includes:
- High-resolution voltage and current measurements
- Time-synchronized waveform data
- Transient disturbance signatures
- Event frequency and location patterns
- Historical circuit behavior over time
Without this level of operational data, analytics platforms have limited ability to detect emerging issues or identify meaningful patterns.
This is where many initiatives stall.
The challenge is not building better models.
It is capturing the right data from the system.
From Events to Patterns
One of the most valuable capabilities enabled by continuous monitoring is the ability to identify patterns in electrical behavior.
For example, repeated momentary disturbances occurring at the same location under specific conditions—such as peak load or wind events—often indicate localized issues:
- Deteriorating hardware
- Insulation breakdown
- Intermittent conductor contact
- Vegetation interaction
Individually, these events may not trigger alarms. But when analyzed collectively, they provide early warning of developing reliability risks.
By identifying these patterns, utilities can investigate and address issues before they result in sustained outages.
This represents a fundamental shift: From event-based response to condition-based awareness.
Building the Foundation for Grid Intelligence
As utilities face increasing complexity—from distributed energy resources to electrification and large new loads—the need for better operational awareness is growing.
Feeder-level monitoring provides a foundational data layer. Equally important, continuous datasets enable utilities to build and train analytics models over time—improving accuracy and operational value.
In this context, grid monitoring is not just about visibility. It is about enabling a more predictive, data-driven operating model.
Starting Where It Matters Most
Utilities do not need to instrument every circuit to begin realizing value.
Many start with targeted deployments on feeders where improved visibility can deliver immediate insight, such as:
- Circuits with recurring momentary disturbances
- Areas with heavy vegetation exposure
- Feeders experiencing rapid load growth
- Locations with unexplained reliability issues
- Segments of the network with limited existing visibility
Within a relatively short timeframe, these deployments often reveal how circuits behave under real operating conditions—and where emerging risks exist.
The Path Forward
The electric grid is becoming more dynamic. New load profiles, distributed generation, and environmental pressures are introducing operating conditions utilities have not historically encountered.
At the same time, expectations around reliability, safety, and performance continue to rise.
Advanced analytics and AI will play an important role in managing this complexity.
But their effectiveness depends on a simple prerequisite:
The ability to see what is happening on the system.
The next step is capturing them—and turning them into actionable insight.
For utilities pursuing AI-driven grid management, that is where the transformation begins.
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