Multi-Agent FLISR (Fault Location, Isolation, and Service Restoration) Automation

By automating fault location, isolation, and service restoration, the system reduces the Customer Minutes of Interruption (CMI) by 60-80% for typical lateral faults. This directly lowers the financial liability from SAIDI/SAIFI penalties and customer compensation claims. The architecture uses smart meter last-gasp signals and PMU data to locate faults within seconds, versus the 15-45 minutes of manual crew patrols.

Automated switching eliminates unnecessary dispatches for fault confirmation and manual switch throws. The workflow coordinates remote-controlled switches and reclosers via the OMS/SCADA, allowing a single operator to manage restoration for multiple concurrent events. This reduces crew rollouts by ~40% for minor events and cuts associated fuel, vehicle, and overtime costs, especially during storm conditions.

The core business case is quantifiable improvement in System Average Interruption Duration Index (SAIDI) and Frequency Index (SAIFI). By restoring customers on healthy feeders within 2-3 minutes instead of 30-90 minutes, a utility can improve its published reliability metrics by 15-25%. This strengthens regulatory standing, improves rate case outcomes, and enhances brand reputation in competitive markets.

Faster restoration and autonomous load transfer unlock latent capacity in the existing network. By dynamically reconfiguring feeders around faults, the system prevents overloads on supporting assets, effectively increasing their usable capacity. This can defer or eliminate the need for costly transformer upgrades or new feeder builds, improving capital planning efficiency and ROI.

Rapid fault isolation limits arc energy and prevents faults from escalating into sustained faults that can cause wildfires or damage adjacent infrastructure. The predictive vegetation management integration allows the system to pre-emptively reconfigure the network during high-wind events, reducing the probability of vegetation-caused outages and associated liability and repair costs.

The multi-agent architecture acts as a force multiplier in the control room. By triaging alarms, simulating switching sequences in a digital twin, and presenting validated recommendations, it reduces operator cognitive load during major events. This allows existing staff to manage more complex grids or growing DER penetration without proportional headcount increases, improving operational leverage.

This agent ingests real-time streams from smart meters (last-gasp signals), PMUs, and feeder relays to pinpoint fault locations within seconds. It correlates event timing and uses impedance-based algorithms on the network digital twin to identify the exact faulted section, replacing manual crew patrols and radio calls. The agent validates signals against weather and asset health data to filter false positives before escalating.

The core logic agent that generates and validates safe switching sequences. It queries the network topology model to identify the nearest upstream and downstream remotely controlled switches or reclosers. The agent simulates the proposed switching order in the digital twin to ensure no overloads, backfeeds, or safety violations, then packages the sequence for operator approval and automated execution via SCADA.

Once a fault is isolated, this agent identifies alternative power paths to restore customers on unaffected segments. It evaluates available tie switches, feeder capacity, and voltage constraints to formulate an optimal network reconfiguration. The agent coordinates with the Switching Orchestrator to execute the restoration sequence, often leveraging adjacent feeders or distributed energy resources (DERs) to minimize Customer Interruption Duration (CID).

A critical middleware component that translates agent decisions into utility system commands. It creates switching tickets in the Outage Management System (OMS), sends control points to SCADA, and logs every action with a full audit trail. This layer enforces operator-in-the-loop approval gates for major switching steps and ensures all autonomous actions are visible, reversible, and compliant with utility switching procedures.

Manages the human-in-the-loop interface for repair work. After isolation, this agent automatically generates a work order with the fault location and affected equipment, dispatching it to the nearest available crew via mobile workforce management systems. It implements a positive safety lockout, holding all automated restoration switching until the field crew confirms they are on-site and clear, preventing accidental re-energization.

The learning and optimization component. After each event, this agent aggregates performance data—time to restore, customers affected, switching accuracy—and compares it against the digital twin's predictions. It identifies discrepancies (e.g., incorrect device status) and proposes updates to the network model or agent logic. This closed-loop feedback is essential for maintaining system accuracy as the grid evolves.