ESO Power Grid Inspection with AI | 96% Detection Accuracy
Revolutionizing Power Grid Inspections with AI-Powered Analysis
How Agmis helped ESO achieve 96% detection accuracy in power grid infrastructure inspections, transforming utility asset management and safety across Lithuania's energy distribution network.
ESO - Lithuania's National Energy Distribution Operator
ESO is the Lithuanian National Energy Distribution Operator, responsible for managing and maintaining the country's electricity distribution infrastructure. With a focus on reliability, safety, and efficiency, ESO oversees extensive low voltage infrastructure networks that power homes and businesses throughout Lithuania. The company continuously seeks innovative solutions to enhance asset management, reduce operational costs, and ensure the safety of both their workforce and the public.
Traditional Inspection Methods Face Critical Limitations
Manual Inspection Inefficiency
Traditional manual inspections of power grid infrastructure are time-consuming, labor-intensive, and prone to human error, limiting coverage and consistency.
Safety Risks
Field inspectors face significant risks when examining high-voltage equipment and hard-to-reach infrastructure in challenging conditions.
Defect Detection Accuracy
Identifying and classifying defects in insulators, crossarms, and pillars requires expert judgment and is difficult to standardize across large networks.
Vegetation Management
Detecting and measuring hazardous vegetation proximity to power lines is critical for safety but challenging to assess accurately and consistently.
Advanced AI Technology for Comprehensive Infrastructure Analysis
Element Object Detection: AI models identify and classify all common power grid infrastructure elements with exceptional 96% precision, ensuring comprehensive coverage.
Defect Classification: Multi-class algorithms classify insulators (intact vs. broken) and crossarms (intact, tilted, crooked) with 92% and 82% accuracy respectively.
LiDAR Data Analysis: Advanced processing of 700km of LiDAR data enables precise calculation of distances, heights, tilts, and vegetation proximity measurements.
Vegetation Detection: Combined RGB and LiDAR analysis identifies hazardous vegetation, measuring height, distance, and coverage area for proactive risk management.
Detection Accuracy
Exceptional precision in identifying power grid infrastructure elements
Transformative Results Across Operations
Enhanced Detection Accuracy
AI-powered analysis delivers consistent, highly accurate detection rates far exceeding manual inspection capabilities, ensuring no critical issues are missed.
Improved Safety
Reduced need for field inspectors to access dangerous locations, while proactive identification of hazards prevents potential outages and accidents.
Operational Cost Reduction
Automated analysis dramatically reduces inspection time and labor costs while covering larger areas more frequently and consistently.
Predictive Maintenance
Early detection of defects and degradation enables proactive maintenance scheduling, preventing failures and extending infrastructure lifespan.
Infrastructure Element Detection
96% average precision in identifying power grid infrastructure elements ensures comprehensive and reliable asset inventory with minimal false positives or missed components.
Defect Classification & Assessment
92% accuracy in insulator classification and 82% in crossarm assessment enables targeted, prioritized maintenance that addresses critical issues first while optimizing resource allocation.
Vegetation Management
Accurate identification and measurement of hazardous vegetation allows for proactive trimming schedules, reducing fire risk and preventing power disruptions caused by vegetation interference.
Risk Prevention & Safety Enhancement
Precise distance calculations and tilt measurements identify high-risk situations before they become critical, protecting both infrastructure and public safety while ensuring regulatory compliance.
Building the Future of Utility Asset Management
For energy distribution operators and utility companies worldwide, the challenges ESO faced are increasingly critical. This AI-powered solution delivers lasting value through:
Scalability
The system efficiently handles massive datasets—200,000 photos and 700km of LiDAR data—and can easily scale to accommodate growing infrastructure networks.
Data-Driven Decisions
Comprehensive, accurate data enables strategic planning, budget optimization, and informed investment decisions based on actual infrastructure condition rather than estimates.
Preventive Maintenance
Early defect detection shifts operations from reactive repairs to planned maintenance, reducing emergency callouts, minimizing downtime, and extending asset life.
Enhanced Reliability
Proactive identification and resolution of issues before they cause outages improves grid reliability, customer satisfaction, and regulatory compliance.