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Predictive Maintenance in Power - Strategic Intelligence

Publisher GlobalData
Published Jul 15, 2025
Length 77 Pages
SKU # GBDT20265878

Description

Predictive Maintenance in Power - Strategic Intelligence

Summary

Predictive maintenance strategies is enabling power companies to transition from traditional, reactive maintenance models to a more sophisticated, data-driven paradigm. This shift not only optimizes asset performance but also extends the lifespan of critical infrastructure.

Artificial Intelligence (AI) is markedly enhancing predictive maintenance within the power sector. AI-driven systems are increasingly being incorporated into power plants and grids to meticulously monitor equipment, analyze data, and forecast potential malfunctions. This integration facilitates proactive maintenance strategies and aids in averting expensive outages.

Wind and solar photovoltaic (PV) systems are progressively utilizing predictive maintenance to enhance reliability and efficiency.

Scope
  • The report focuses on predictive maintenance in power as a theme.
  • It provides an industry insight on how predictive maintenance drives proactive maintenance strategy and can deliver efficient power generation.
  • The report discusses on how artificial intelligence is driving predictive maintenance in power.
  • The report briefs on growing application of predictive maintenance in wind and solar PV and its use cases in power utilities.
  • The report delivers an overview on how predictive maintenance can optimize energy storage.
  • The report covers mergers & acquisitions (M&As), venture financing deals and patent trends in predictive maintenance.
  • The report provides an overview on competitive position held by power utility companies adopting predictive maintenance in business operations.
Reasons to Buy
  • A comprehensive analysis on the growing market trend of predictive maintenance in the power industry.
  • The report provides an overview on the leading players in predictive maintenance theme and where do they fit in the value chain.
  • Technology briefing on reactive approach, preventive approach, condition-based approach and predictive approach maintenance.
  • A detailed analysis of predictive maintenance value chain.
  • Company profiles of leading power utilities in predictive maintenance.
  • An overview on predictive maintenance service and solution providers.
  • The report emphasizes the role of artificial intelligence in predictive maintenance and discusses how it will transform solar PV and wind.
  • A snapshot of power sector scorecard predicting the position of leading power utilities in predictive maintenance theme.

Table of Contents

77 Pages
Executive Summary
Players
Technology Briefing
Evolution of maintenance: from reactive to proactive
Reactive approach
Preventive approach
Condition-based approach
Predictive approach
Predictive maintenance technologies in the power industry
Vibration monitoring
Infrared thermography
Lubricant oil analysis
Ultrasonic and acoustic emission monitoring
Setting up a predictive maintenance system
The importance of predictive maintenance for aging infrastructure
Trends
Technology trends
Macroeconomic trends
Industry Analysis
Understanding key principles of predictive maintenance
How does predictive maintenance work?
Reasons to adopt predictive maintenance
Operational efficiency
Cost savings
Improved safety
Sustainable production
Inventory management
Improved product quality
AI-driven predictive maintenance in the power sector
PdM is gaining ground in solar PV and wind power
Optimizing energy storage with predictive maintenance
Predictive maintenance solution providers
Use cases
EDF’s predictive maintenance of equipment
Enel’s predictive diagnostics for batteries
ENGIE’s predictive maintenance for high voltage (HV) systems
Evergy’s intelligent substation monitoring
Iberdrola’s Project WinDTwin
RWE’s condition monitoring of wind turbines
Vattenfall’s digital twin for wind farms
Timeline
Signals
M&A trends
Venture financing trends
Patent trends
Value Chain
Device layer
Sensors and probes
Thermal imaging
Measurement devices and instruments
Connectivity layer
Edge and cloud infrastructure
Networking equipment
Wireless network
Data layer
Data storage
Data processing and analysis
Business intelligence
App layer
Services layer
System design and integration
Inspection and maintenance
Digital twins
Companies
Power companies
Sector Scorecards
Power sector scorecard
Who’s who
Thematic screen
Valuation screen
Risk screen
Industrial automation scorecard
Who’s who
Thematic screen
Valuation screen
Risk screen
Glossary
Further Reading
GlobalData reports
Our Thematic Research Methodology
About GlobalData
Contact Us
List of Tables
Table 1: Technology trends
Table 2: Macroeconomic trends
Table 3: Predictive maintenance solution providers
Table 4: Key M&A transactions associated with the predictive maintenance theme
Table 5: Power companies
Table 6: Glossary
Table 7: GlobalData reports
List of Figures
Figure 1: Who are the leading players in the predictive maintenance theme, and where do they sit in the value chain?
Figure 2: Predictive maintenance through on-site condition monitoring
Figure 3: The different maintenance strategies: an overview
Figure 4: Vibration monitoring of rotating equipment
Figure 5: Infrared thermography of 13.8kV busbar bushings
Figure 6: Lubricant oil analysis of rotating equipment
Figure 7: An example of ultrasonic testing
Figure 8: Predictive maintenance workflow
Figure 9: AI-driven predictive maintenance focuses on
Figure 10: Predictive maintenance of rotating machinery
Figure 11: Predictive diagnostics of a battery
Figure 12: Predictive maintenance of HV Systems
Figure 13: A top view of Evergy’s substation
Figure 14: An offshore wind power plant site
Figure 15: An offshore wind turbine being inspected
Figure 16: A digital display of wind turbine predictive analytics
Figure 17: The predictive maintenance story
Figure 18: Predictive maintenance deals in power
Figure 19: Deal volume by countries for 2021−2024
Figure 20: Sector-wise deal volume trend in predictive maintenance for 2021−2024
Figure 21: The increase in patent activity is driving growth in the adoption of predictive maintenance
Figure 22: Predictive maintenance activities will continue to increase in the power industry
Figure 23: The predictive maintenance value chain interaction with the power industry value chain
Figure 24: The predictive maintenance value chain
Figure 25: Predictive maintenance value chain: Device layer
Figure 26: The predictive maintenance value chain: Connectivity layer
Figure 27: The predictive maintenance value chain: Data layer
Figure 28: The predictive maintenance value chain: App layer
Figure 29: The predictive maintenance value chain: Services layer
Figure 30: Who does what in the power space?
Figure 31: Thematic screen - Power sector scorecard
Figure 32: Valuation screen - Power sector scorecard
Figure 33: Risk screen - Power sector scorecard
Figure 34: Who does what in the power space?
Figure 35: Thematic screen - Industrial automation scorecard
Figure 36: Valuation screen - Industrial automation scorecard
Figure 37: Risk screen - Industrial automation scorecard
Figure 38: Our five-step approach for generating a sector scorecard

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