Autonomous inspection and smart O&M: keys to energy resilience and sustainability

Photovoltaic solar energy has established itself in recent years as one of the main drivers of the energy transition in Spain. The growth of installed capacity has accelerated over the last five years, from just over ten gigawatts to well over thirty gigawatts in grid-connected plants, with a clear upward trend. This massive rollout has made solar power plants critical infrastructure, both because of their contribution to the energy mix and because of the need to ensure their long-term availability and performance.

Photovoltaic plants are characterized by large extensions, thousands of distributed modules, kilometers of roads and fences, as well as constant exposure to demanding environmental conditions. Traditional manual inspection, based on periodic campaigns and spot checks, is becoming increasingly difficult to scale and sustain in terms of operational cost, safety and data reliability.

Among the main challenges are the early detection of hot spots in the modules, progressive degradations, incidents in wiring or connections, localized dirt that penalizes performance, mechanical failures in solar trackers or damage after extreme weather events. When these anomalies are not identified in time, their impact translates into production losses, increased corrective interventions and a reduction in the useful life of the assets.

In this scenario, autonomous inspection based on robotics and advanced sensors emerges as a natural evolution of the operation and maintenance (O&M) model. Autonomous vehicles equipped with visual and thermal sensors make it possible to travel through the plant in a systematic, repeatable and safe way, capturing homogeneous and comparable data over time. However, the value lies not only in the capture of information, but also in its correct orchestration and operational exploitation.

Robot orchestration platforms allow users to plan inspection routes, define periodic frequencies or on-demand missions, monitor execution in real time, and centralize results in a single environment. This means that inspections are no longer a one-time process, but a continuous flow of information for maintenance decision-making.

In this context, GMV has developed uPathWay^®, an intelligent platform for orchestrating, managing, and optimizing heterogeneous robots and autonomous vehicles in complex industrial environments such as photovoltaic plants and other energy facilities.

This approach offers clear benefits: it reduces energy losses by shortening the time between the occurrence of a fault and its detection, while also optimizing costs by automating large-scale inspection routes and freeing up human resources for higher-value tasks. It also enables smarter, more targeted maintenance based on objective evidence and the actual impact on production and safety.

Authors: Ángel Lázaro and Eric Polvorosa