Dasheng TechnologyWind Engineering Services

Precision O&M: Three Value Objectives

Improve power performance

Assess wind sensing, yaw, pitch and control deviations; verify energy gains and applicable settlement rules

Reduce unplanned downtime

Link events, work orders and operating states to losses; distinguish alarms from verified root causes

Manage major-component risk

Investigate lubrication, hydraulics and load indicators; avoid double-counting repair and downtime costs

Value objectives, not verified returns. Define energy, downtime and repair-cost boundaries to avoid double counting.

Technical O&M & Turbine Condition Management

500 MW

Current capacity under O&M

Company-provided figure, September 2026

Engineering expertise

Turbine expertise, field verification and engineering judgment

AI-assisted diagnostics

Screen anomalies and track trends with maintenance knowledge

Maintenance decision support

Prioritize checks and prepare repair plans, people and parts

AI supports the process; authorized professionals approve maintenance and operating decisions

System Issues: Yaw, Hydraulics & Lubrication

System and symptomEvidence and field checksAction and verification
Yaw: misalignment, frequent moves or slipLink alignment, power and yaw events; separate sensing, strategy and brake issuesCheck offsets; review control and brakes; verify power performance and wear exposure
Hydraulics: pressure drops and frequent rechargeCheck pressure, pump and actuator states; inspect sensors, leakage and accumulatorsRepair the identified circuit issue; retest pressure build-up, retention and response
Lubrication and cooling: delivery issues or heatingReview temperature residuals, vibration and work orders; check delivery, filters and coolingRestore delivery and cooling as justified; track temperature and vibration under comparable conditions

Problem patterns and investigation routes from supplied material, not confirmed diagnoses. Validate model-specific actions and trade-offs.

System Issues: Measurement, Control & Rotor

System and symptomEvidence and field checksAction and verification
Measurement: drift, stuck values or scaling errorsCross-check power, wind and temperature; the source withdrew an initial power-capping diagnosisVerify instruments, input ranges and scaling; reassess performance using corrected data
Control: parameters, parts and versions do not matchLink parameter snapshots and change records to operating groups; verify applicable configurationReview changes and retain rollback; verify function, protection and operating response
Rotor: pitch offsets and blade-angle differencesCombine angle measurement, power and vibration; distinguish sensing, aerodynamic and mass effectsCorrect and remeasure to turbine requirements; verify energy and load effects separately

Problem patterns and investigation routes from supplied material, not confirmed diagnoses. Validate model-specific actions and trade-offs.

Diagnostics Linked to Field Action

Identify

Data screening and field inspection

Assess

Verify evidence and priorities

Execute

Maintenance plan and resources

Verify

Condition checks and recurrence tracking

Deliverables: asset records, defect lists, maintenance plans and post-maintenance reviews

Case and application

Precision O&M Cases: Yaw, Hydraulics & Temperature

Yaw alignment & strategy

Three yaw-angle distributions differ in width. Review power, yaw activity and operating conditions.

Records: Check vane zero, deadband and delays. Test under matched conditions and compare alignment and yaw duty.

Hydraulic pressure drop

Median pressure drops: 3.0 / 8.3 / 5.0 bar across three groups, with 59 turbine summaries.

Records: Check sensors, pumps, valves, accumulators and leakage. Use pressure-holding tests to define repairs.

Temperature & recurring stops

One turbine over 18 months: temperature residuals versus stop/recovery events. Peak residual: 6.36°C.

Records: Check lubrication, cooling and sensors. Select actions using operating and work-order evidence, then retest.

Source: supplied precision O&M review aggregates. Actions are verification pathways; realized benefits are unverified.

Explore these cases interactively ↗

Precision O&M: Yaw Distribution and Strategy

3 yaw-angle frequency distributions

image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/ −40 −30 −20 −10 0 10 20 30 40 Yaw-deviation bin centre (°) 0 0.05 0.1 0.15 0.2 Frequency (fraction) Group 1 Group 2 Group 3

What the figure shows

Distribution widths differ. Interpret them with yaw counts and duration, without treating them as measured energy gains.

Field verification

Check strategy version, wind bins, deadband and delays; assess action count, duration and loading.

Action and retest

If differences persist under matched conditions, test the strategy within a defined scope. Otherwise rebuild a comparable baseline.

Supplied aggregates. Raw sampling, filtering, calibration and independence not reproduced; full sampling window not supplied.

Source case explorer ↗

Case and application

Hydraulics: Pressure Drops & Field Checks

Compare the supplied groups

Compare the supplied pressure-drop summaries to prioritize turbine-level checks

Check the hydraulic circuit

Verify circuit design, actuation, sensors, accumulators and pump supply before diagnosing damage

0510152025Pressure drop (bar)MinimumA n=20: Minimum 2.02.0B n=25: Minimum 5.35.3C n=14: Minimum 2.02.0MedianA n=20: Median 3.03.0B n=25: Median 8.38.3C n=14: Median 5.05.0MaximumA n=20: Maximum 5.05.0B n=25: Maximum 20.220.2C n=14: Maximum 5.05.0A n=20B n=25C n=1459 supplied per-turbine summaries only
View chart data
Rank / groupMinimumMedianMaximum
A n=202.03.05.0
B n=255.38.320.2
C n=142.05.05.0

20/25/14 turbines by group; not event counts; sampling period unspecified

Redrawn from supplied summaries. Sampling period, filters and calibration are incomplete; differences do not establish faults or returns.

Precision O&M: Temperature and Stop Records

18 months, one turbine and fleet medians

image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/ 2 4 6 8 10 12 14 16 18 Month index (18 consecutive months) 0 1 2 3 4 5 6 Temperature residual (°C) Target turbine Fleet median image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/ 2 4 6 8 10 12 14 16 18 Month index (18 consecutive months) 20 40 60 80 100 Stop/recovery events per month Target turbine Fleet median

What the figure shows

Temperature residuals and stop events share a monthly index. Temporal proximity does not establish causality.

Field verification

Match power and ambient temperature; check grease, lubrication supply, cooling and sensors; consolidate alarms into actual events.

Action and retest

If field checks confirm a lubrication or cooling issue, plan maintenance and retest. If measurements or event definitions differ, correct data first.

The source window is Jan 2025–Jun 2026, displayed as month indices. These are repeated observations, not 18 independent turbines. The full temperature-residual model has not been reproduced.

Source case explorer ↗

Case and application

Precision O&M: Data Case Explorer

From differences to field action

Explore ten cases covering wind measurement, yaw, hydraulics, pitch and more.

Open case explorer · 10 topics ↗

From system differences to field checks and remediation plans

Redrawn source aggregates. Raw measurements not revalidated. For analytical demonstration, not confirmed diagnoses or verified gains.

CONTACT

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Business enquiries

zhuo.wu@ds-techcn.com+86 152 3118 3837

Beijing headquarters