AI for Grid Operations: Event Detection, Outage Prediction and Power Quality Analytics

In AI for Grid Operations: Event Detection, Outage Prediction and Power Quality Analytics, you'll learn ...

• The application of AI and machine learning to event detection, outage prediction, incipient failure detection, and power quality analytics in electric grid operations
• The integration of SCADA, AMI, PMU, weather, asset-condition, and other operational data into AI-assisted grid reliability workflows
• The evaluation and validation of AI model performance using technical metrics, reliability indices, uncertainty analysis, and risk-based engineering judgment
• How to implement AI-assisted grid operations with appropriate model governance, professional oversight, traceability, and safeguards for public safety

Overview

Preview a portion of this course before purchasing it.

Credit: 1 PDH

Length: 18 pages

This course examines how artificial intelligence and machine learning can support safer, more reliable, and more proactive electric grid operations. Designed for Professional Engineers, the course explores practical applications of AI for event and anomaly detection, outage prediction, incipient equipment failure detection, and power quality analytics. Participants learn how operational data from SCADA, AMI, PMUs, weather systems, GIS, and asset-condition monitoring can be integrated and transformed into useful features for AI models.

The course addresses supervised, unsupervised, and hybrid anomaly-detection approaches; predictive models for weather-related outages and equipment degradation; and automated classification of voltage sags, swells, harmonics, flicker, and transients. It also examines model validation through accuracy, precision, recall, F1-score, AUC, MAE, MAPE, prediction lead time, and utility reliability indices such as SAIDI, SAIFI, and CAIDI.

Throughout the course, AI is presented as an engineering decision-support tool rather than a replacement for professional judgment. The content is aligned with ISO 9001-style quality management and risk-based thinking. Particular emphasis is placed on uncertainty, fail-safe operations, FMEA, model governance, version control, data lineage, audit trails, regulatory compliance, and the Professional Engineer's continuing responsibility for public safety.

Specific Knowledge or Skill Obtained

This course teaches the following specific knowledge and skills:

• The roles of SCADA, AMI, PMU, weather, GIS, and asset-condition data in developing AI models for grid operations
• The use of temporal, spatial, weather, and equipment-health features to support anomaly detection and outage prediction
• The application of supervised, unsupervised, and hybrid AI methods for identifying abnormal grid conditions and incipient equipment failures
• The management of false alarms and missed detections through confidence scores, alert thresholds, pilot testing, and risk-based model tuning
• The use of AI-based outage prediction to prioritize preventive actions, pre-stage crews and equipment, and support proactive network operations
• The classification of voltage sags, swells, harmonics, transients, and flicker to accelerate power quality diagnosis and root-cause assessment
• The interpretation of accuracy, precision, recall, F1-score, AUC, MAE, MAPE, and prediction lead time when validating AI models
• The relationship between AI-assisted operational improvements and utility reliability indices such as SAIDI, SAIFI, and CAIDI
• The application of FMEA, model version control, data lineage, audit trails, change management, and continual validation to AI model governance
• How to maintain professional defensibility, regulatory compliance, conservative safety margins, and Professional Engineer oversight when using AI-generated recommendations

Certificate of Completion

You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 15 questions. PDH credits are not awarded until the course is completed and quiz is passed.