When sediments are transported through the penstock, they are most damaging where water is redirected, accelerated, or hits solid structures – such as guide vanes, inlet components, or in curved and narrow sections. These sensors detect ultrasonic structure-borne signals generated by the impact of sediment particles. Depending on size, velocity, and hardness, the particles produce a specific acoustic signature. An AI model continuously analyses the turbine’s acoustic signature under optimal operating conditions. Even the slightest deviation – such as altered impact patterns caused by increased sediment load – is detected.
Global Hydro’s machine learning algorithms distinguish between operational noise and harmful sediment impacts, enabling precise identification of risk scenarios. This allows the system to determine the most economical operating mode – including when it’s better to temporarily shut down than risk excessive wear. The AI can also send push notifications to operators and even alert downstream plants in real time.
Turning Data into Insight – and Insight into Strategic foresight
An AI model continuously analyzes the turbine’s acoustic signature under optimal operating conditions. Even the slightest deviation from normal – such as altered impact patterns caused by increased sediment load – is detected. Our data science experts interpret these signals using machine learning and anomaly detection to deliver precise recommendations: when do harmful sediments occur? At what load level does it become more economical to shut down than to continue operation?
This 4-nozzle vertical Pelton turbine turns raw acoustic data into a reliable decision-making tool – and transforms digital intelligence into true operational and strategic value.
