Wind Services for Owners and Operators
Sciemus provide specialist independent advice to owners and operators of wind power generation assets, delivering quantitative analysis to optimise asset performance throughout project life.
Sciemus' ability to quantify the expected performance of wind assets and benchmark performance against global peer groups provides invaluable insight to owners and operators. Rather than relying solely on internal data and information from OEM's, Sciemus' clients are able to draw on the relevant data and experience from wind assets around the world.
Each of our models is driven by the world's largest normalised dataset in its sector. The Sciemus Wind Farm Risk Analysis Tool (WindRAT) is driven by a knowledge repository of over 10,000 wind farms, over 900 different wind turbines and operational reliability data for more than 90,000 turbine years covering the last 20 years. Leveraging on Sciemus' proprietary dataset delivers significantly more robust conclusions than using qualitative or subjective engineering analysis in isolation.
Sciemus analytics examine asset performance deterioration up to end-of-life. Projecting future performance from an assets own past performance and from manufacturers estimates has limited value; Sciemus draws on real life, impartial reliability data from turbines that have been operational for up to 20 years, providing unparalleled insight up to decommission or repowering.
Application of Analytics
Sciemus analytics are used by owners and operators of wind energy assets to inform decision making throughout project life:
Compare independent real-life operational performance data of turbine technologies, manufacturers and models. Perform scenario modelling of performance over time for specified turbine options to enable selection of optimal turbines for a given project.
Benchmark performance of wind energy assets against expected performance as generated by WindRAT and against observed performance of predefined peer groups. Identify over/under performance and facilitate margin improvements. Identify problematic components as key risk drivers to guide inspection and review.
Asset Life Cycle Analysis
Increase accuracy of asset lifecycle estimates through utilisation of Sciemus operational data to support asset management, O&M strategy and critical spending decisions such as implementation of condition monitoring, proactive/predictive maintenance.
Operations & Maintenance Optimisation
Use advanced statistical methods to determine the relationships between maintenance and performance and the effect on profit to enable identification of optimal maintenance effort for maximum benefit. Design more effective O&M programs to reflect utilization, environmental factors, and inherent component reliability statistics.
Identify problematic components and key risk drivers to focus risk management activity. Put in place required measures to mitigate risk prior to support deployment of risk transfer and warranty programmes.
Use quant-based analysis to achieve optimal levels of risk retention and transfer. Objectively quantify risk within given retained and transferred layers to inform target premium and terms.
Enhance understanding of warranty coverage across the operational life-cycle; for in-warranty, end-of-warranty, and extended warranty assets. Examine warranty options against the risk profile of the asset to determine optimal levels of coverage and robust cost-benefit decision making. Review existing warranties to identify overlaps and gaps in warranty and insurance coverage