Most wind acquisition teams don't fail because they skip due diligence. They fail because their DD is incomplete, inconsistent, or organized in a way that makes it impossible to act on what they found.
This checklist covers the categories that matter for technical and operational DD on wind assets. It's not a regulatory compliance list. It's the practical stuff — the items that separate a clean close from a deal that blows up six months into operations.
1. Turbine identity and fleet composition
Before you assess condition, you need to know exactly what you're buying. That sounds obvious. It isn't always.
- ☐ Complete turbine list with make, model, and serial numbers
- ☐ Commission dates for each unit
- ☐ Nameplate capacity vs. actual rated output
- ☐ Hub height, rotor diameter, and swept area for each turbine
- ☐ Any turbines that have been re-powered, retrofitted, or de-rated
Mismatched serial numbers or missing commission dates are the first red flag. If the seller can't produce a clean asset register, the rest of the DD is built on sand. A structured DD workflow catches these gaps on day one instead of week three.
2. Component condition assessment
This is where most DD campaigns either shine or fall apart. You need consistent, component-level condition data across the entire fleet.
- ☐ Gearbox condition scores with oil analysis history
- ☐ Generator inspection records and bearing condition
- ☐ Blade inspection reports with photographic evidence
- ☐ Main bearing vibration data and replacement history
- ☐ Yaw system, pitch system, and converter condition
- ☐ Transformer and electrical infrastructure status
The key isn't just having this data — it's having it in a format that lets you compare across turbines. If turbine 7 got a 3/5 on gearbox condition and turbine 12 got "fair," you don't have comparable data. You have two opinions. A standardized risk register solves this by enforcing the same scoring rubric across every component.
3. Maintenance and operations history
What's been done to the fleet matters as much as what you find during inspection.
- ☐ Complete maintenance logs for each turbine (scheduled and unscheduled)
- ☐ Major component replacement history (gearboxes, generators, blades)
- ☐ Current O&M contract terms, expiration, and renewal options
- ☐ Warranty status for all major components
- ☐ SCADA data availability and quality
A turbine that's had two gearbox replacements in eight years tells you something the current condition score alone doesn't. History is predictive. Treat it that way.
4. Performance and energy production
Revenue projections depend on production forecasts. Production forecasts depend on historical performance data that's actually reliable.
- ☐ Minimum 3 years of monthly production data per turbine
- ☐ Availability rates (time-based and energy-based)
- ☐ Curtailment records and reasons (grid, noise, environmental)
- ☐ Independent energy assessment (P50/P90) vs. actual production
- ☐ Wake effect analysis for the site layout
If actual production consistently trails the original P50 estimate, find out why before you close. Degradation, wake effects, and curtailment are fixable — but only if you price them into the deal. See how production projections work in a structured DD system.
5. Financial and contractual
- ☐ Power purchase agreement (PPA) terms, pricing, and expiration
- ☐ Land lease agreements and escalation schedules
- ☐ Tax equity structure and flip dates
- ☐ Insurance coverage and claims history
- ☐ Interconnection agreement and grid curtailment exposure
6. Environmental, permitting, and compliance
- ☐ Environmental permits and any active compliance orders
- ☐ Avian/bat mortality monitoring results
- ☐ Noise compliance measurements and any neighbor complaints
- ☐ Decommissioning bond or security requirements
The checklist isn't the hard part
Every experienced deal team knows most of what's on this list. The hard part is executing it consistently across 20, 40, or 80 turbines — and doing it fast enough to win the deal.
That's where the format matters more than the content. A checklist in a spreadsheet becomes a checklist that drifts. Different evaluators score differently. Data gets entered in different formats. By the time you're building the investment memo, you're reconciling data instead of analyzing it.
A database-first approach enforces the checklist structurally. Every turbine gets the same assessment categories. Every component gets scored on the same scale. And the data you collect during DD becomes the operational baseline after close — no re-keying, no reformatting, no starting over.
One team used this approach to complete full technical DD on 25 turbines in 48 hours. Not by cutting corners. By eliminating the structural overhead that slows every spreadsheet-based campaign.
Run your next DD campaign in days, not weeks.
Ridgeline gives your team a structured, repeatable DD workflow built for wind assets.
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