BENEFITS

Minimal effort for fleet level power curve analysis

Avoid energy loss due to undetected performance issues

Efficient follow-up and problem solving

Standardised method across all turbine types

ABOUT

ABOUT

Increase power output across your fleet.

Even a running wind turbine may not be achieving its full potential. There are a large number of issues known to effect operational efficiency but detecting and diagnosing them is often like finding a needle in a haystack. i4SEE Performance™ will regularly scan your fleet, detecting with a high degree of sensitivity any problems as soon as they occur and providing clear recommendations, enabling you to take immediate action.

Wind turbines operating at a non-design performance condition not only contribute to a loss of revenue due to reduced energy yield. Often the resulting loads on the turbine will be higher than intended, leading to more frequent and earlier component failure and an increase in operational costs. Therefore, a regular and accurate analysis of turbine performance is essential in order to maximise the profitability of any fleet.

i4SEE Performance™ replaces traditional, manual power curve analysis with an online monitoring technique. The performance of each turbine is regularly analysed in detail using algorithms which account for differences in local environmental conditions, terrain and flow effects as well as individual turbine characteristics. Available 10-Minute SCADA logs are used, and machine learning techniques are combined with statistical methods to achieve a high degree of accuracy.

A unique, intelligent workflow embedded into i4SEE Performance enables complete automation of the monitoring process. Following initial deployment, the App quickly learns the behaviour of each turbine in your fleet and performs an initial health check. In case any changes are detected during continuous monitoring, the data scientist is provided with a report including the likely root cause and recommended corrective action. No system setup or configuration is required; the data scientist is able to focus fully on gaining a detailed understanding of priority issues and the coordination of corrective actions.