Dynamic Wind Farm Maintenance Planning

Every maintenance day at a wind farm brings a new balance of safety, people, parts, travel, production and changing events. NTT DATA helped a renewable power generation business replace repeated manual replanning with automated annual and daily preventive-maintenance planning, using optimization algorithms to turn those constraints into practical schedules that field teams can review and adjust.
Two wind technicians prepare equipment beside an onshore turbine at sunrise, with a working wind farm extending across the landscape.
DAILY PLANNING
75% less time
TEAM WORKLOAD
75–80% pair saturation
WORK-ORDER TIMING
Approx. one month earlier
VALIDATION SCALE
4 wind farms
Service vehicles and field teams coordinate maintenance at multiple turbine locations across an onshore wind farm.
Planning maintenance around what matters most

One decision workflow connects field reality, operational priorities and production economics.

From repeated manual replanning to dynamic, constraint-aware maintenance decisions
目的
The initiative set out to automate preventive-maintenance planning at both annual and daily horizons. The aim was to use available information more consistently, reduce errors linked to incomplete manual data use and give maintenance teams schedules that remained practical, safe and reviewable in the field.
OPPORTUNITY
The documented approach creates a path to extend standardized, data-informed planning across wind-farm preventive maintenance. By connecting asset and operational information with risk, condition and work requirements, the business can continue moving from fragmented, reactive activity toward clearer prioritization of scarce field resources.
Making every maintenance constraint count

The challenge was to convert dispersed operational inputs and changing field conditions into schedules that were both optimized and usable by maintenance teams.

主な課題
A wind-farm planner reorganizes printed work orders, route maps and maintenance notes with turbines visible outside the field office.
1: Repeated manual replanning
Planning required frequent rework and could underuse available information, increasing the risk of errors when operational inputs were incomplete.
Wind technicians load tools and spare parts into a service vehicle at dawn before beginning field maintenance.
2: Two planning horizons
The initiative needed to support long-range annual planning and daily execution without losing the practical detail required by field teams.
A wind technician checks climbing safety equipment while a colleague prepares specialist parts at the base of a turbine.
3: Interdependent constraints
Each plan had to balance specialization, job duration, travel, spares, energy prices, maintenance type, safety requirements and unplanned outages.
Two wind technicians review a maintenance schedule on a rugged tablet beside equipment and an onshore turbine.
4: Trust at the point of action
Automatically generated schedules needed to remain transparent, reviewable and adaptable, while connecting with the wider maintenance workflow.
解決策
An optimization engine grounded in field operations

NTT DATA combined task characterization, genetic-algorithm planning, operational data and a review dashboard in an end-to-end decision workflow for preventive maintenance.

チェックアイコン
Structured planning inputs

Task-characterization models brought together execution time, travel and workforce requirements so the planning process could use critical information consistently.

チェックアイコン
Annual and daily optimization

Genetic algorithms generated preventive-maintenance schedules for both horizons, aligning longer-term priorities with day-to-day operational needs.

チェックアイコン
Constraint-aware scheduling

The planning logic incorporated energy price, workforce specialization, maintenance type, spare parts, safety constraints and unplanned outages.

チェックアイコン
Human review and integration

A dashboard enabled maintenance teams to validate and modify generated plans, with support for integration into maintenance systems and execution workflows.

影響
Faster planning, earlier action and a process teams can trust

Daily planning time fell by 75%, from 120 to 30 minutes per day. The documented results also show 75–80% saturation across maintenance pairs, average work-order timing approximately one month earlier and validation across four wind farms. Beyond those measures, the new approach reduced errors linked to incomplete manual data use, balanced workload more effectively and established a standardized planning process that maintenance teams can still review and adjust.

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