Energy & Utilities Asset Intelligence

Yes. Our pilots hold authorised person training and operate enterprise drones with radiometric thermal cameras and high-resolution zoom lenses at safe standoff distances from live conductors (typically 5-15 metres). Substations, switchyards, and transmission lines are inspected while fully energised, eliminating the planned outage window that typically costs $50,000 to $500,000 per event in lost generation or supply penalties. Thermal imaging identifies hot joints, overloaded connections, failing surge arresters, and degraded insulators. Temperature differentials as low as 2-3 degrees Celsius above ambient indicate developing faults, while differentials over 10 degrees Celsius indicate critical defects requiring immediate intervention.

Drone-based powerline inspection typically delivers 60-80% cost savings compared to helicopter methods and 70-90% savings compared to ground-based climbing crews. Helicopter inspection typically costs $1,500-$4,000 per kilometre, while ground-based crews cost $3,000-$8,000 per kilometre. For a 500km distribution network inspected annually, operators typically achieve savings of $325,000 to $1,600,000 per year when switching from helicopter to drone-based methods. Additional ROI comes from higher defect detection rates and precise vegetation encroachment mapping that reduces unplanned outage risk.

Operators typically achieve 60-80% cost reduction per kilometre, faster inspection cycles (30-50% time savings), and higher defect detection rates due to closer proximity to conductors. The ROI compounds through reduced vegetation management costs (precise encroachment mapping versus broad-area clearing), elimination of large landing zone requirements in urban areas, and lower safety risk exposure for personnel. Vegetation encroachment causes approximately 30% of unplanned outages on distribution networks. Precise corridor mapping with LiDAR and multispectral imaging identifies high-risk encroachment zones before they cause outages.

Yes. We deploy enterprise confined space drones to inspect tank internals, turbine housings, pressure vessels, and other enclosed assets without human entry, purging, or scaffolding. The drone captures high-resolution visual imagery, thermal data, LiDAR point clouds, and ultrasonic thickness measurements of shell plates, floor condition, roof structure, and internal fittings. This eliminates the 4-8 hours typically required for isolation, purging, and atmospheric testing. Real-time gas monitoring (14+ combustible gases at 0.1% LEL) runs simultaneously, providing continuous atmospheric awareness.

Radiometric thermal imaging detects hot joints (loose connections), overloaded conductors, failing surge arresters, degraded insulators, transformer cooling issues, unbalanced loads, and corrosion under insulation (CUI). On solar farms, thermal imaging identifies cell and string faults, bypass diode failures, soiling patterns, and potential ignition points before they cause fire or production loss. Visual-only inspection misses up to 85% of subsurface thermal defects on electrical assets. Our AI-powered analysis automatically classifies defects by severity (minor, serious, critical) according to NETA MTS-2019 standards.

Tower and nacelle inspection can proceed during normal turbine operation. Blade inspection requires the turbine to be stopped and locked, typically for 2-4 hours per turbine. A single drone team can inspect 8-12 turbines per day compared to 1-2 per day using rope access, representing a 4-6x throughput improvement. Defects detected include leading edge erosion, lightning strike damage, crack propagation, blade root delamination, and surface coating degradation. Resolution is sufficient to identify cracks as small as 0.5mm.

Yes. We deploy aerial drones for dam wall faces, spillways, and surrounding embankments, combined with underwater enterprise ROVs for submerged components including outlet valves, intake structures, and dam toe areas. This multi-environment approach provides comprehensive dam condition intelligence without draining the reservoir or deploying divers. LiDAR-equipped drones detect millimetre-level movement in dam walls and map cracks with change tracking over time. ROV-mounted sonar maps sediment accumulation and identifies scour patterns. All data is integrated into a single measurable digital twin.

DeepSky IQ holds a CASA commercial operators licence with advanced endorsements including Extended Visual Line of Sight, Enclosed Beyond Visual Line of Sight, night operations, and operations over populated areas. Our pilots are trained as authorised persons for operations around high-voltage infrastructure. We also hold ISO 9001 (Quality), ISO 14001 (Environmental), and ISO 45001 (Work Health and Safety) certified management systems, and carry $20M public liability insurance. All operations are conducted under documented safety management systems with site-specific risk assessments.

Deliverables are structured for direct integration into your existing asset management platforms including Maximo, SAP, and GIS. Outputs include georeferenced defect registers with severity classification, thermal anomaly reports, condition scores, and 3D digital twins with position-tagged observations. AI-powered defect detection automatically classifies anomalies by type and severity. Reports are formatted for audit readiness and aligned to your existing compliance frameworks. Data exports in open formats (LAS, GeoTIFF, CSV, GeoJSON, IFC) for direct import into any enterprise system.

A one-off inspection captures a point-in-time condition snapshot. An ongoing monitoring program establishes a baseline digital twin and tracks change over time through scheduled repeat surveys. Monitoring programs deliver significantly higher value because they enable predictive maintenance, automated change detection, and trend analysis that supports capital planning and regulatory compliance. Each survey compounds the intelligence of the last. Programs are structured around your maintenance windows and compliance cycles with step-down pricing at volume.