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Market Research Report

Advancing O&M Insights for Wind Plants with Digital Twins

Published by Navigant Research Product code 816934
Published Content info 14 Pages; 3 Tables, Charts & Figures
Delivery time: 1-2 business days
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Advancing O&M Insights for Wind Plants with Digital Twins
Published: March 28, 2019 Content info: 14 Pages; 3 Tables, Charts & Figures
Description

Predictive analytics and digital twin modeling are sweeping across many industries, including the wind sector. Wind asset owners seek to improve power plant performance and manage and minimize operations and maintenance costs. Wind turbines are highly instrumented machines, lending themselves well to collecting vast amounts of data that can inform and refine digital twin models. This report focuses on digital twin technology, which are digital replicas of wind turbines, turbine subcomponents and even entire wind farms.

Digital copies of operating wind turbines can enable asset owners to virtually and remotely monitor, test, and predict the performance and condition of turbines and subcomponents and carry out performance and reliability tests in a virtual environment. This empowers asset owners and wind turbine OEMs operating wind turbines to predict and plan for faults and optimize performance of their assets. Digital twin models enable market players to make better informed decisions, evaluate remaining asset life, and strategize for future investments.

This Navigant Research report discusses wind sector-specific offerings, trends, and intensifying vendor landscape. The analysis examines drivers and barriers for wind turbine asset owners, wind turbine OEMs, and software vendors that are increasingly active in this space. Capacity trends and market dynamics are projected through 2028. The report also discusses specific market players using digital twin technology and provides recommendations for wind market participants.

Key Questions Addressed

  • What are digital twins?
  • What are the implications of digital twins specific to the wind power market?
  • What are some of the specific applications and results of digital twins in wind?
  • Who are the various stakeholders in this big data ecosystem?
  • Who are the vendors and solutions providers of digital twin technology to the wind market?
  • What are recommendations for digital twin implementation for the different stakeholders?

Who Needs This Report

  • Wind turbine manufacturers
  • Wind power plant owners
  • Data analytics vendors
  • Condition monitoring equipment manufacturers
  • Wind turbine component suppliers
  • Government agencies, policymakers, and researchers
  • Investor community
Table of Contents
Product Code: SI-DWF-19

Table of Contents

Spark

Context

Recommendations

Digital Twins Promise to Revolutionize Wind Plant Operations and Maintenance

  • Wind-Specific Aspects of Digital Twins
    • What Digital Twins Promise the Wind Sector
  • Intensifying Vendor Landscape for Digital Twin Offerings in the Wind Energy Market
  • Wind Turbine OEMs Are Actively Offering and Marketing Digital Twin Technology
  • Wind Turbine Subcomponent Vendors Offering and Marketing Digital Twin Technology
  • SaaS Vendors Offering and Marketing Digital Twin Technology

Wind Plant Owners Are Cautious to Invest in Digital Twin and Other PA

  • Wind Plant Owners Are Skeptical About Paying Extra for the Latest Big Data Tech
    • Surveys Show Limited Adoption of Advanced Pattern Recognition, Digital Twin, and Other Data Modeling

Wind Asset Owners Should Cautiously Invest in Digital Twins: Trust but Verify

  • Navigant Research Recommendations to Benefit Stakeholders in this Ecosystem
    • Wind Turbine OEMs Are Vendors and Customers
    • SaaS Vendors Should Tailor Digital Twin Offerings
    • Wind Plant Asset Owners Should Seek Profit-Sharing Contracts
    • Asset Owners Should Test and Verify the Technology

List of Charts and Figures

  • Use of APR, Digital Twin or Other PA
  • Use of a Platform to Address Turbine Life Cycle Reliability and Availability

List of Tables

  • Common Wind Turbine Components and Subcomponents Monitored
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