faults are recognised, as well as monitor their asset’s performance for compliance and valuation purposes. This solution was developed with the goal of facilitating a clean and sustainable path for cities and governments to achieve their sciencebased emission reduction targets.
Solar panels Conventional solar plant inspection methods are mostly done manually, which as a result doesn’t allow photovoltaic defects to be detected accurately, making the reporting process inaccurate, complex and time-consuming. However, it’s been proven that drones can detect malfunctioning panels quicker than a field crew on foot. According to T&D World, inspecting solar panels with unmanned aerial vehicles (UAVs) saves on average $US1.2K per megawatt in costs and is 95% more efficient than manual inspections. What makes Unleash live’s Solar A.I. solution different from other drone inspection providers is that it turns real-time
data into insights. The platform is integrated to Unleash live’s customer’s workflows, delivering a continuous real-time end-to-end visual inspection process, which allows the users the opportunity to schedule fully autonomous drone flights, while automating the aerial data collection process and reporting systems. Lastly, they’re able to obtain real-time insights in order for them to be able to predict issues and incidents before they happen.
Artificial Intelligence
and sharing visual maintenance data and insights. It’s a centralised location that integrates the data captured and generates reports on workflows. This enables experts to: - collaborate from the field or the office - recognise issues - categorise insights - build a digital passport of the asset. Allowing the users to improve their asset productivity and reduce time-to-insight, from anywhere. The images below show one of Unleash live’s customers (Hugo Velasquez from Zenobe Energy) performing a real-time inspection of a wind turbine, located 1,078 km away, all from the comfort of the office. To eliminate the manual time-consuming analysis, Unleash live trained their A.I. algorithm to identify up to 9 different types of blade faults with high accuracy. It is also able to predict maintenance issues and therefore mitigate the probability of risks. Additionally, the users of this solution are able to generate alerts when certain events/
“they’re able to obtain real-time insights in order for them to be able to predict issues...” ACCELERATE
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