AI in Electric Power Systems Protection and Control

Walk into any control room today and you’ll see two realities living side by side. On one wall: decade-old SCADA screens, breaker symbols, and event logs that look as if time stopped in the last era of grid modernization. On another: real-time PMU heat maps, streaming analytics dashboards, and a quiet hum from servers running models that didn’t exist five years ago. That juxtaposition captures the story of 2025: electric power systems protection and control (P&C) are becoming natively data-driven, and artificial intelligence is moving from pilots to plant-wide practice.

This article looks past the buzzwords to explain where AI is delivering measurable value, where it’s not, and how utilities can deploy it without compromising safety or reliability. Think of it as a reporter’s notebook from a year spent inside substations, control centers, and vendor labs.

Why AI, and why now?

Three converging trends made AI inevitable in P&C:

• Sensing exploded. Widespread phasor measurement units (PMUs), high-resolution disturbance recorders, and smart meters produce a torrent of synchrophasors and waveform snippets—far more than traditional rule-based logic can fully exploit.
• Power electronics changed grid dynamics. Inverter-dominated resources (wind, solar, storage) and HVDC links introduce fast, nonlinear behaviors. Protection settings that were tuned for synchronous machines now face edge cases they were never designed to see.
• Computing moved to the edge. Substation servers and ruggedized gateways can host containerized models, making millisecond-scale inference plausible right next to the relay.

AI doesn’t replace proven protection schemes; it augments them—catching subtle precursors, triaging events faster, and shaping control actions when the system is stressed.

What AI actually does in protection and control1) Disturbance and fault analytics

• High-fidelity fault classification. Convolutional and graph neural networks trained on labeled oscillography can distinguish single-line-to-ground vs. line-to-line vs. three-phase faults and estimate fault location along a line segment.
• Incipient failure detection. Autoencoders flag anomalies in current/voltage waveforms that precede nuisance trips or CT/PT degradation.

2) Wide-area protection and oscillation damping

• Mode shape tracking. Kalman filters and ML hybrids estimate inter-area oscillation modes from PMU streams, triggering damping controls (FACTS, power system stabilizers) before oscillations grow.
• Remedial Action Scheme (RAS) validation. Reinforcement learning (RL) agents can stress-test RAS logic in a digital twin, revealing hidden interactions between contingencies.

3) Substation asset health

• Transformer diagnostics. Gradient-boosting and attention models fuse dissolved gas analysis, thermal profiles, and load history to predict insulation stress and tap-changer wear.
• Breaker condition monitoring. Vibration signatures and travel time curves feed classifiers that estimate contact erosion or mechanism fatigue.

4) Adaptive settings and protection coordination

• Context-aware thresholds. Models learn seasonal and topology-dependent load envelopes, nudging settings (within engineered bounds) to reduce misoperations.
• Islanding detection for microgrids. Pattern recognition on ROCOF, voltage phase jumps, and harmonic content separates genuine islanding from mere disturbances.

5) Operator decision support

• Event summarization. Language models translate cryptic event lists into human-readable incident reports (“Protection Group 3 opened CB-12 at 14:02:07 due to reverse power; upstream voltage dip began 120 ms earlier”).
• Contingency ranking. ML‐based security indices prioritize which N-1/N-2 scenarios deserve operator attention right now.

Data: the fuel that makes (or breaks) projects

A protection-grade model lives or dies by data quality. The practical recipe:

• Time alignment first. PMU streams, relay SOE logs, and SCADA tags must be synchronized. A 30-ms skew can turn a promising model into a hallucination engine.
• Label sparingly, but well. You’ll never have ground truth for every event. Invest in a gold-standard labeling protocol for a small, representative subset; use semi-supervised learning to generalize.
• Engineer the “boring” meta-data. Breaker and CT asset IDs, protection groups, line segments, and switching states must be consistent; they’re the join keys for everything else.
• Retain raw waveforms. Compressed, tamper-evident storage of oscillography enables future re-training when disturbances you’ve never seen before finally appear.

The deployment patterns that work

1. Edge-inference, cloud-training. Train models on historical data in the cloud; deploy distilled versions in substation servers or hardened gateways for low-latency inference.
2. Guardrail the outputs. AI never drives a breaker directly. It issues advice to engineered logic—e.g., “raise suspicion score for line L-17”; protection trips only when deterministic criteria are met.
3. Shadow mode before control. Run the model in parallel for 3–6 months, compare to ground truth, and only then allow it to condition non-critical thresholds.
4. Human factors. If an alarm floods operators, it will be ignored. Set precision/recall to match the room’s appetite for false positives, and log why a recommendation was made (feature importance, saliency).
5. MLOps for the grid. Version datasets, models, and feature pipelines; track drift; rehearse rollback. Treat a model like a protection setting file—with documented change control.

A mid-article aside: the tools that glue it together

A lot of teams discover they don’t just need one model—they need a bench of utilities to translate formats, test prompts, and create operator-friendly summaries. This is where broad, multi-tool workspaces help. The Jadve AI platform is one such option: instead of a single, narrow app, it offers multi-model chat plus utilities for text generation, conversion (e.g., HTML → Markdown), lightweight code assistance, PDF/web page summarization, and prompt libraries. In a P&C context, teams use a workspace like this to draft incident summaries from SOE logs, create operator manuals from engineering notes, or compare wording for protection advisories across different language models before release. It’s not a relay or a control algorithm; it’s the day-to-day productivity console that keeps documentation and communication from lagging behind the engineering.

Real-world use cases (that aren’t hype)

• Misoperation triage. After a storm, hundreds of events need review. A classifier groups similar misoperations, extracts the common root cause (“current inversion after CT saturation on feeder F-12”), and proposes a short list of setting changes to test in the digital twin.
• Wide-area oscillation guard. PMUs pick up a 0.4–0.6 Hz mode after a large import ramps up. The estimator tracks mode damping in real time; when it decays below a threshold, the system arms a damping controller and alerts operators with a “what changed” snapshot (tie-line flows, VAR support, HVDC setpoints).
• Wildfire risk-aware protection. A model conditions reclosing logic based on live weather, fuel moisture, and fault likelihood; during Red Flag conditions, it delays or inhibits reclose attempts on selected circuits, documenting every deviation for compliance review.
• DER ride-through analytics. When a feeder trips unexpectedly during a voltage sag, waveform clustering reveals a specific inverter firmware version that dropped out too early; operators escalate a targeted update instead of blaming the relay.

Control: from setpoints to strategies

Protection trips are binary, but control is continuous—and here AI helps operators navigate the gray areas:

• Model predictive control (MPC) with learned surrogates. Instead of solving a full AC optimal power flow every few seconds, a neural surrogate approximates the physics, letting MPC test many what-ifs (VAR dispatch, tap changes, FACTS actions) quickly.
• RL for microgrids. In islanded or campus systems, RL can learn policies that balance fuel cost, battery cycle life, and frequency stability while respecting hard safety constraints enforced outside the agent.
• Restoration guidance. After a blackstart, graph-based search orders feeder energization to minimize cold-load pickup, while an NLP layer converts the plan into step-by-step switches with clear holds and verifications.

The rule remains: AI proposes; deterministic interlocks dispose.

Cybersecurity and compliance

AI expands the attack surface—new data flows, new containers at the edge—so security must ride shotgun:

• Segment ruthlessly. Inference services sit on a non-routable zone; only signed, whitelisted containers can deploy.
• Protect the training data. Event and waveform archives are as sensitive as relay settings; encrypt at rest, log reads, and treat them as regulated assets.
• Adversarial awareness. Add sanity checks to reject physically impossible suggestions (e.g., power factors outside feasible bounds) and detect manipulated inputs.
• Auditability. Every model decision that influenced an operator action must be reproducible—versioned data, model hash, and feature set stored alongside the event.

Interoperability and standards

AI that can’t talk to the rest of the grid won’t last. Favor architectures that:

• Speak IEC 61850 and sample values/MMS where appropriate, or cleanly bridge to them.
• Ingest COMTRADE and standard PMU streams without bespoke parsers.
• Respect utility change-management processes already defined for protection settings and EMS/SCADA updates.

It isn’t glamorous, but these details separate flashy demos from systems that survive a relay technician’s scrutiny.

A 90-day blueprint (copy/paste)

Days 1–15: Frame the question. Pick one problem (e.g., transformer incipient fault detection). Assemble a data pack: 3–5 years of DGA, loading, temperatures, and maintenance logs. Define success: fewer unplanned outages, earlier alarms, lower false positives than existing rules.

Days 16–45: Build the baseline. Split data chronologically. Train a simple gradient-boosting model and an autoencoder. Stand up feature and model versioning. Draft the operator screen with only three outputs: health score, top drivers, recommended action.

Days 46–75: Shadow and explain. Run in shadow mode against live data. Compare alarms to lab tests and field inspections. Tune thresholds for the room’s tolerance. Add a one-click “why” panel (SHAP or similar) and a “disagree” button that creates feedback tickets.

Days 76–90: Govern and hand over. Write the operating procedure, rollback plan, and retraining cadence. Present to protection, planning, and cybersecurity. Only then propose limited, supervised influence on non-critical thresholds.

Risks and limitations (the non-marketing section)

• Dataset bias. If your history lacks inverter-heavy disturbances, your classifier will be falsely confident when they arrive. Augment with simulated events from a digital twin.
• Model drift. Settings and topology evolve; performance decays silently. Drift monitors and scheduled re-training are not optional.
• Operator trust. Black-box alarms get ignored. Invest in interpretability from day one, and show “what changed” with each recommendation.
• Over-automation temptation. It’s easy to let a “high confidence” label nudge protection too far. Keep AI behind engineered interlocks.

AI is not a new relay. It’s a set of tools that make existing protection smarter and system-level control more anticipatory. The winning deployments respect the culture of power engineering—deterministic where it must be, data-driven where it can be. Done right, AI shortens the time between disturbance and understanding, trims the false alarms that exhaust operators, and helps a grid full of inverters behave like a coherent machine. Done wrong, it adds noise. The difference is design discipline—and a willingness to start small, prove value, and only then expand.