A wind turbine will never generate its expected output 100% of the time, and its performance can and almost certainly will change over time. There are various reasons for this. Some are known but cannot be controlled or managed, such as fluctuating inflow conditions. However, occasionally the opposite is the case, the fault is unknown but can be controlled or rectified. With datasets full of noise from the known reasons, is it possible to extract data to identify the unknown causes? Yes, is the answer.
It’s not always clear if a wind turbine is underperforming, but machine learning can help to find out.
Clir Renewables has released a new product feature that automatically detects underperforming assets and highlights key actions to rectify underperformance. By using layered machine learning, built on an advanced data model, the company has created an underperformance detector for its software solution.
This detector works along with other algorithms in the software to analyze the data and classify them based on the reason for the underperformance. It creates a synthetic event when turbine power output is well below the historical mean for that wind speed. This invaluable 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.
Essentially, the detector removes the noise leaving a clean set of data from which the unknown causes can be deduced, and corrective actions created. Alternatively, if the cause is still unidentified, the owner can approach the manufacturer with the cleaned data looking for answers and solutions.
“With noise filled datasets the uncertainty of any conclusions that can be drawn on causes of underperformance will increase significantly, and in a lot of cases, issues can be completely masked by the noise,” said Selena Farris, Data Scientist at Clir Renewables. “Utilizing the advances in machine learning, a well-structured data model and deep domain expertise, Clir software provides a tool to reduce this uncertainty, generating actionable insights for owners to increase performance and protect their assets from faults and failures.”
Filed Under: News, O&M, Software
