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Clir Renewables to Use Machine Learning to Detect Underperforming Turbines
Monday, April 1, 2019Company Profile | Follow Company
It's not always clear if a turbine is underperforming, but machine learning can assist in finding out.
Vancouver, BC, April 1, 2019--(T-Net)--Clir Renewables announced that it has released a new product feature which automatically detects underperforming assets and highlights key actions to rectify underperformance.
A wind turbine will never generate its expected output one-hundred percent 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.
Using layered machine learning, built on an advanced data model, Clir Renewables 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. The detector 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.
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.
Selena Farris, Data Scientist at Clir Renewables
Selena Farris, Data Scientist at Clir Renewables, said "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. 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."
About Clir Renewables
Clir is a renewable energy AI software company whose industry-leading cloud-based tools help asset managers and owners maximize production, and give owners clarity on performance. The founding team and many early staff members worked at notable consultancies and wind farms. After repeatedly witnessing the same issues they decided to create an innovative software solution. With their expertise in renewable energy, the Clir team built a unique optimization tool. Working with renewables for years our analysts understand the way assets function, the key issues, and what needs to be done to improve performance. In just one year Clir has created an advanced software product with algorithms to pinpoint underperformance and use AI to communicate the detections. We have also created the Clir Data Exploration Environment, a data visualization tool to quickly demonstrate the findings that our software detects. Founded in 2017, the company now serves over 2 GW of assets. Our mission is to provide an affordable complete understanding of renewable energy power plant technical performance with clear actionable items to maximize project returns. Benefits of using Clir's software
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