A wind turbine is producing almost no power, but the wind speed looks normal. Another turbine nearby is generating electricity under similar conditions. Is the first turbine experiencing a fault, or has its output been intentionally curtailed?
In this seven-turbine demonstration, TDengine combines a CesiumJS 3D site view with operating history, equipment context, and AI-assisted analysis. We follow one low-output turbine from the site overview through a comparison with neighboring machines to possible fault causes and the checks needed to investigate them.
The walkthrough follows four steps: locate the affected turbine, compare the evidence, evaluate possible causes, and identify what to check next.
Locate the Affected Turbine in the 3D Wind Farm
The investigation starts in a CesiumJS 3D view embedded in TDengine IDMP’s Canvas. Seven turbines are organized into North and South groups, with their locations, reported operating states, and current measurements visible in the scene.
The color-coded indicators distinguish three important states:
- Running: A turbine reporting normal operation.
- Faulted: A turbine reporting an abnormal condition that needs investigation.
- Curtailed: A turbine whose output is being intentionally limited.
The operator selects WT-N03, the turbine marked as faulted, and uses Open WT-N03 in IDMP to move from the 3D site view into its operating data.
Figure 1. The CesiumJS wind farm view highlights the faulted turbine WT-N03, showing its operating status, measurements, and a direct link to investigate the turbine in TDengine IDMP
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Compare the Faulted, Normal, and Curtailed Turbines
Low power does not always mean a turbine is broken. Wind conditions may change, a turbine may be intentionally curtailed, or a fault may prevent it from generating normally. The next step is to compare WT-N03 with other machines at the site.
In the selected 3D view, WT-N03 is producing just 9 kW, while the running turbine WT-N04 shows 177 kW. Another turbine, WT-S01, shows 61 kW, but its status is CURTAILED. Its equipment panel explains that this is a controlled operating restriction, not a fault.
Comparing the Three Turbines
| Turbine | Operating state | Active power | What it tells us |
|---|---|---|---|
| WT-N03 | Faulted | 9 kW | Very low output; investigate the turbine’s condition |
| WT-N04 | Running | 177 kW | A nearby turbine is generating normally |
| WT-S01 | Curtailed | 61 kW | Reduced output can also be intentional |
The figures above are taken from the same CesiumJS comparison view. The WT-S01 panel also shows wind at 6.4 m/s and rotor speed at 10.2 rpm, along with its curtailed status.
When WT-N03 is selected in the earlier 3D view, its panel shows wind at approximately 6.4 m/s but a rotor speed of only 0.2 rpm. That makes its very low output harder to explain as a simple lack of wind.
The operator can also open WT-N03’s dashboard to inspect its last 12 hours of power, wind, and rotor measurements. These trends help determine whether the abnormal behavior is persistent or limited to a brief moment.
Figure 2. The CesiumJS view shows power output across the wind farm, comparing the faulted WT-N03 with normally operating turbines and the intentionally curtailed WT-S01.
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How Equipment Context Supports the Analysis
The measurements identify an unusual turbine. To interpret them, the engineer also needs to know which asset they belong to and what its operating states mean.
TDengine IDMP organizes the wind farm’s turbines, measurements, and attributes around their corresponding equipment assets. It can also associate documents with a specific turbine.
For WT-N03, the Related Documents section contains ridge-wind-check.md, an operator reference covering the FAULT, CURTAILED, and IDLE states. These distinctions matter: low output during deliberate curtailment calls for a different investigation than low output accompanied by a fault state.
The AI analysis later lists ridge-wind-check among its references, connecting the investigation with the document attached to the turbine. The document helps explain the operating context; it is not itself proof of a breaker failure.
Figure 3. The WT-N03 asset page in TDengine IDMP includes an operator reference document, ridge-wind-check.md, covering FAULT, CURTAILED, and IDLE states.
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Use AI to Evaluate Possible Causes
With WT-N03 identified as the abnormal turbine, the operator asks TDengine’s AI to explain what might be happening.
From Equipment Data to Possible Fault Causes
The recorded follow-up question is:
Tell me why it could be any of these for north wind turbine 3
The AI response examines the turbine’s FAULT state, very low power, near-stopped rotor, and available wind, while using the operation of a neighboring turbine as a reference. It also cites the ridge-wind-check operator procedure.
In its explanation of a possible breaker trip, AI writes:
If WT-N03’s main breaker or grid connection has tripped, the turbine physically cannot export power, even though wind conditions are sufficient.
The response begins with grid or main breaker trip. It explains that a turbine may be unable to export power even when there is enough wind if its main breaker or grid connection has tripped.
The AI then discusses other possible explanations, including a pitch or brake malfunction, generator or converter failure, protection-system trip, and communication or control-system failure. These can also leave a turbine with little output or an abnormal operating state.
The important result is a practical shortlist of local equipment and control issues to investigate. A breaker trip appears first in the explanation, but the response does not establish it as the confirmed cause.
What Should the Engineer Check Next?
| Possible cause | Why it is worth checking | Next check |
|---|---|---|
| Grid or main breaker trip | WT-N03 has little output and a nearly stopped rotor despite available wind; neighboring turbines remain active | Review breaker status, grid-connection events, and protection or fault codes |
| Pitch or brake malfunction | Pitch controls or braking can stop the rotor, including during a protective shutdown | Check pitch-system commands, brake status, and related alarms |
| Other electrical or control fault | Generator/converter issues, protection trips, or control faults can also interrupt generation | Review converter, safety-chain, controller, and communication fault history |
The immediate task is to match these possibilities against the turbine’s actual fault records and operating state for the same period.
An earlier dashboard interpretation rated the shutdown-or-fault explanation at medium confidence and considered a data-freshness or time-window mismatch as another possibility. Checking timestamps alongside fault history helps the engineer avoid drawing conclusions from mismatched readings.
AI gives the engineer several concrete paths to investigate, rather than leaving the team with only a low-power alert.
Figure 4. TDengine’s AI examines possible causes of WT-N03’s abnormal power output, including a grid or main breaker trip, pitch or brake malfunction, and other equipment faults.
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How CesiumJS Connects with TDengine
CesiumJS and TDengine serve different parts of this workflow. CesiumJS displays the wind farm in 3D, making it easy to locate a turbine and see its current status. TDengine provides the industrial time-series data, equipment history, asset context, and AI-assisted analysis used to investigate the problem.
In the demonstration, the CesiumJS scene is embedded in IDMP Canvas. Selecting WT-N03 leads the operator into the corresponding equipment view. The shared turbine identity connects what the operator sees on the 3D map with the measurements and supporting information used in the investigation.
How the Integration Works
The TDengine CesiumJS integration guide provides an example of the underlying data flow. An integration service queries TDengine through taosAdapter, returns asset data as JSON, and supplies it to the CesiumJS visualization.
The guide also describes an IDMP API option for assets modeled in IDMP. Using that option requires adapting the example service. Figure 5 illustrates the connection between the 3D view and the investigation workflow; the documented integration path is a reference example rather than a claim about the exact backend used in this video.
Figure 5. The CesiumJS 3D view and TDengine IDMP connect turbine monitoring with equipment data, asset context, and AI-assisted fault investigation through a shared turbine identity.
What This Investigation Shows
The demonstration brings together three parts of a wind turbine investigation. CesiumJS helps the operator locate WT-N03. Operating measurements and comparisons with other turbines show why its low output deserves attention. TDengine’s AI then organizes the evidence into possible causes and specific checks.
A grid or main breaker trip is one possibility worth investigating, alongside pitch, brake, and other local equipment or control faults. The next step is to check the turbine’s fault and protection records to find out what actually happened.
Explore Industrial AI with TDengine to learn more about AI-assisted analysis for industrial equipment.
For implementation details, see the TDengine and CesiumJS integration guide.
This is a configured demonstration. The proposed fault causes need to be checked against equipment records and inspection.
