Share this page on:

Conference programme 

Back to the programme printer.gif Print

Poster session

Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Philip Totaro Totaro & Asssociates, Germany
Co-authors:
Philip Totaro (1) F P
(1) Totaro & Asssociates, Hamburg, Germany

Printer friendly version: printer.gif Print

Poster
Download poster(0.63 MB)

Presenter's biography

Biographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited

Philip Totaro is the Founder and CEO of Totaro & Associates, a market research and innovation strategy consultancy with offices in Hamburg, Germany and Houston, TX. Mr. Totaro is regarded worldwide as the foremost expert on wind industry technology and IPR matters. He has helped cultivate and disposition over 500 innovations, and his assessments have led to over 300 issued patents. His strategic market analysis has led to the funding justification of over $500M in R&D investment and the development of multi-million dollar product and service offerings. He has provided legal and technical due-diligence for over $1B in M&A.

Abstract

Enabling Energy Output Optimization with Controls, Service Scheduling and Spares Management

Introduction

Energy output optimization is a key driver for wind turbine manufacturers, developers, and wind park owners/operators. The goal is to maximize the return by tinkering with the key elements of the COE equation. Specifically, it is important to develop and implement solutions which address the following:

• Increase annual energy production (AEP).
• Regulating wind turbine / wind farm power output for delivery of power to the electric grid based on price optimal timing.
• Reduce O&M costs.
• Extend life of wind parks to minimize unscheduled maintenance.
• Streamline spare parts inventory in accordance with identified service requirements and known upcoming component replacements.

Approach

This can be achieved by taking advantage of several key technologies which are already deployed in the wind industry. Generally, the goal is to achieve wind turbine and wind park control for energy price optimization taking into account damage accumulation rates and maintenance scheduling. Incorporation of CMS data gathering and analytics into a turbine operational profile for a power plant controller which can produce energy at maximum pricing is achievable today.

Main body of abstract

This system comprises the following technology:

1) Component damage accumulation monitoring including “real-time” stress accumulation monitoring of critical drivetrain, blade and electrical systems components.

2) Using damage accumulation data fed into models of the turbine control and wind farm control system to predict:

a. Component life consumption
b. Component life consumption rate
c. Remaining useful component life

Damage accumulation data must be processed and input into component life estimation models which will take into account:

i. Monitored component data,
ii. Software simulations,
iii. Historical data analysis,
iv. Wind farm site conditions, and
v. Neighboring turbine data

3) An ‘early warning’ notification system to wind farm operators about component damage accumulation, component life consumption and remaining useful life. Additional suggestions for energy price optimization, O&M strategy, spare parts sourcing and maintenance scheduling will be incorporated.

4) Energy output optimization

a. Uprate for AEP increase when remaining useful component life would not cause un-scheduled maintenance / downtime.
i. Uprate at times of peak utility price to increase to maximize electricity price.
ii. Regulation of duration and level of turbine uprate to ensure component life consumption or component life consumption rate are below threshold levels (or average levels).

5) Turbine life / component life preservation

a. Derate for component life preservation in order to prevent un-scheduled maintenance / downtime.
i. Avoidance of derating at times of peak utility price to maximize electricity price.
ii. Component stress reduction with AEP loss minimized.

6) O&M strategy / maintenance scheduling – avoidance of sustained turbine operations that result in un-scheduled turbine maintenance.

Conclusion

Dynamic turbine operational envelope definition and monitoring is enabled by data analytics on CMS and SCADA data being monitored today. This information can be fed into a power plant controller which can utilize forward prediction of site conditions and define a maximum performance profile for the wind turbine in order to provide optimal power output at times of peak electricity pricing.


Learning objectives
1. What technologies are already available to be integrated into a power plant controller for energy output optimization?
2. How can a power plant controller be designed to operate the wind park for power maximization at peak pricing points?
3. How can spares inventory management be regulated by a power plant control scheme which monitors CMS and SCADA data?