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13:45, 20 March 2026
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Russian University Develops Drone Systems to Lay Groundwork for Digital Energy

The use of unmanned aerial vehicles in the energy sector was discussed at a seminar at Melitopol State University, which has emerged as one of Russia’s centers for UAV development and deployment across a wide range of industries.

The event brought together scientists, students, and representatives of specialized regional companies. Today, unmanned technologies are no longer seen as experimental tools but are steadily becoming part of everyday operations in electric, hydro, and thermal power systems. The seminar focused on concrete applications – from thermal imaging inspections of substations to building digital twins of infrastructure. Industry experts from Melioratsiya, Tavria Energo, and Zaporozhyeenergo joined faculty and students to discuss the most pressing challenges.

Energy companies are particularly interested in monitoring solar panels at power plants using thermal imaging, as well as inspecting high-voltage transmission lines. Drones are also proving useful in addressing newer challenges, such as surveying agricultural land to detect explosive hazards.

Melitopol State University established a laboratory for unmanned aerial systems a year ago and launched an aerospace engineering training program. Events like this seminar serve as a practical training ground for engineers who understand real-sector needs and can integrate UAVs into critical infrastructure.

Benefits That Are Clear to All

The integration of UAVs into the energy sector is one of the most promising developments. It significantly improves system reliability, as drones can inspect energy facilities far more frequently than traditional field crews. Equipped with advanced sensors, they can detect issues invisible to the human eye.

The use of neural networks to process drone-collected data further enhances accuracy in identifying damage and defects. The adoption of predictive analytics algorithms opens the door to anticipating failures before they occur.

Toward a “Mature” Market

The Melitopol seminar highlights how quickly unmanned technologies are evolving in Russia and how strong demand is becoming, particularly in the power sector. Major Russian energy companies have been actively testing and deploying UAVs for several years. What began as isolated trials has now evolved into systematic digital transformation efforts.

Rosseti, for example, uses Supercam drones to inspect power lines, reducing costs by 64% while increasing labor productivity by 84%. In 2025, the company introduced neural network algorithms capable of identifying 41 types of critical defects responsible for more than 90% of accidents.

Researchers at Perm Polytechnic University have developed a UAV capable of applying anti-icing agents to power lines without shutting them down, significantly reducing the risk of winter-related cable failures.

Talent and Technology as Industry Drivers

The seminar at Melitopol State University reflects a broader shift: universities are becoming true centers of technological expertise. Students are not limited to theoretical training but work closely with industry partners, enabling faster responses to real-world challenges.

According to Russia’s Ministry of Industry and Trade, the UAV sector will require around 355,000 specialists by 2030, while the market for civilian drone services could grow to 126 billion rubles (approximately $1.4 billion). The energy and utilities sectors are expected to be among the primary drivers of demand.

In this context, Melitopol State University is addressing not only local challenges but also contributing to the development of a nationwide digital energy infrastructure.

Over the past decade, the role of drones has expanded dramatically, evolving from simple aerial imaging tools into complex autonomous systems capable of performing mission-critical tasks. Where they were once used mainly for visual monitoring, they are now deeply integrated into processes such as equipment inspection, data analysis, and the creation of digital models
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