Clir Renewables has released a new product feature which automatically detects underperforming assets and highlights key actions to rectify underperformance. Using layered machine learning, built on a data model, Clir Renewables has created an underperformance detector for its software solution.
This detector works along with other algorithms in the software to analyse the data and classify them based on the reason for the underperformance. The detector creates a synthetic event when turbine power output is well below the historical mean for that wind speed. This piece of information helps identify ongoing issues at a turbine, not indicated by the SCADA data, inflow conditions under which the turbine does not perform well, and the duration and lost energy associated with the underperformance. It also highlights a hardware or software configuration change that reduces power performance.