Causal Auditing and Protection Paradigm for Sub-health Offshore Wind Assets based on Multi-terminal Harmonic Fingerprinting
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<p class="has-small-font-size wp-block-paragraph"><strong>Project Identity:</strong> Shinar of Clark<br><strong>Author:</strong> Yi Zeng<br><strong>Framework:</strong> Causal Auditing and Protection for Offshore Wind Sub-health<br><strong>📄 Manuscript PDF:</strong> <a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/blob/main/papers/paper-2-precursor-signals/Causal%20Auditing%20and%20Protection%20Paradigm%20for%20Sub-health%20Offshore%20Wind%20Assets%20based%20on%20Multi-terminal%20Harmonic%20Fingerprinting.pdf">Read the Full Paper Here</a><br><strong>DOI:</strong> <a href="https://doi.org/10.5281/zenodo.20267143">https://doi.org/10.5281/zenodo.20267143</a><br><strong>github</strong>:<a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative" target="_blank" rel="noreferrer noopener">https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative</a><br><strong>Email:</strong>Clark@ShinarOfClark.com<a href="https://doi.org/10.5281/zenodo.20267143"></a></p>
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<h1 class="wp-block-heading has-large-font-size">📖 Introductio</h1>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#-introduction"></a></p>
<p class="wp-block-paragraph">This repository implements Phase 2 (“Physiology”) of the sub-health diagnostic framework for offshore wind assets under the <strong>“Clark Paradigm”</strong>.</p>
<p class="wp-block-paragraph">Building upon the “Electromagnetic Ledger” established in Phase 1, this research focuses on capturing <strong>precursor signals</strong> and establishing proactive <strong>protection</strong> mechanisms. By deconstructing the temporal evolution of 1st-20th order harmonic fingerprints across dynamic conditions, this phase achieves a critical transition from pure diagnosis to “predictive protection”.</p>
<p class="wp-block-paragraph"><strong>Core Philosophy:</strong> Precursor Warning = Temporal Evolution of Residuals + Dynamic Time-Lag Analysis (<math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="normal">Δ</mi><mi>T</mi></math>).</p>
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<h2 class="wp-block-heading has-large-font-size">📁 Repository Structure</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#-repository-structure"></a></p>
<p class="wp-block-paragraph">To ensure the transparency and reproducibility of our research, the supporting materials for this paper are organized as follows:</p>
<ul class="wp-block-list">
<li><code>figures/</code>: Contains all high-resolution figures, temporal evolution diagrams, and cross-terminal protection architectures presented in the manuscript.</li>
<li><code>data/</code>: Contains the corresponding datasets (time-series dynamic data) used to generate each figure. Researchers can use this data to independently verify the precursor signal capture algorithms and protection logic.</li>
</ul>
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<h2 class="wp-block-heading has-large-font-size">🛠️ Technical Architecture</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#%EF%B8%8F-technical-architecture"></a></p>
<p class="wp-block-paragraph">This phase extends the distributed hardware topology with advanced temporal analysis and control engines:</p>
<ul class="wp-block-list">
<li><strong>Perception Layer (Field Layer):</strong> MCU nodes deployed inside the turbine and at the substation inlet execute high-frequency sampling at <strong>10.24 kHz</strong>, dynamically synchronized with mechanical variables (wind speed, RPM).</li>
<li><strong>Audit & Protection Layer:</strong> Edge hosts execute predictive mapping and temporal correlation using <strong>Dynamic Causal Models (DCM)</strong> to track the time-lag (<math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="normal">Δ</mi><mi>T</mi></math>) of fault contagion across nodes, generating corresponding protective actions.</li>
</ul>
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<h2 class="wp-block-heading has-large-font-size">🚀 Key Features</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#-key-features"></a></p>
<ul class="wp-block-list">
<li><strong>Precursor Signal Capture:</strong> Captures latent sub-health fingerprints (e.g., microscopic insulation degradation and dielectric drift) before catastrophic failures occur.</li>
<li><strong>Dynamic Time-Lag (ΔT) Analysis:</strong> Analyzes the time delay between harmonic frequency shifts across different nodes, utilizing it as a robust precursor indicator of component stress.</li>
<li><strong>Mechanical-Electrical Predictive Mapping:</strong> Establishes the causal correlation between wind turbine mechanical dynamics (aerodynamic turbulence, sudden speed shifts) and the resulting electrical harmonic fluctuations.</li>
<li><strong>Proactive Protection Shield:</strong> Upgrades the causal auditing engine into a closed-loop protection system. It utilizes the time-lag window to issue dynamic early warnings and preemptively safeguards core power assets.</li>
</ul>
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<h2 class="wp-block-heading has-large-font-size">📊 Performance</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#-performance"></a></p>
<p class="wp-block-paragraph">By leveraging dynamic time-lag analysis, the framework successfully identifies precursor signatures of insulation aging and IGBT switching degradation <strong>hours to days</strong> prior to conventional threshold alarms. This significantly extends the critical response and protection window for O&M teams, averting major asset losses.</p>
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<h2 class="wp-block-heading has-large-font-size">📚 Citation</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#-citation"></a></p>
<p class="wp-block-paragraph">If you utilize the concepts or content of this project in your research, please cite our manuscript:</p>
<p class="wp-block-paragraph"><strong>APA Format:</strong></p>
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">Zeng, Y. (2026). Causal Auditing and Protection Paradigm for Sub-health Offshore Wind Assets based on Multi-terminal Harmonic Fingerprinting (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.20267143">https://doi.org/10.5281/zenodo.20267143</a></p>
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<p class="wp-block-paragraph"><strong>BibTeX:</strong></p>
<pre class="wp-block-preformatted">@misc{zeng2026clark_protection,
title={Causal Auditing and Protection Paradigm for Sub-health Offshore Wind Assets based on Multi-terminal Harmonic Fingerprinting},
author={Yi Zeng},
year={2026},
publisher={Zenodo},
version={v1.0.0},
doi={10.5281/zenodo.20267143},
url={https://doi.org/10.5281/zenodo.20267143}
}</pre>
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<h2 class="wp-block-heading">🛡️ License</h2>
<p class="wp-block-paragraph"><a href="https://github.com/Shinar-of-Clark/Clark-Paradigm-Initiative/tree/main/papers/paper-2-precursor-signals#%EF%B8%8F-license"></a></p>
<p class="wp-block-paragraph">This project is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International (CC BY 4.0)</a> License.</p>
<figure class="wp-block-image"><a href="https://creativecommons.org/licenses/by/4.0/"><img src="https://camo.githubusercontent.com/59896db2b47e60cf6b6cdd3af4bc9ec3e8d290389a9d3ce7cdb95a955e9d0923/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d43432532304259253230342e302d6c69676874677265792e737667" alt="License: CC BY 4.0"/></a></figure>
<p class="wp-block-paragraph"><strong>Rights Statement:</strong> You are free to share and adapt this work, provided that you give appropriate credit to the author <strong>Yi Zeng (Project Shinar of Clark)</strong> and indicate if changes were made.</p>
</blockquote>
This repository implements Phase 2 (“Physiology”) of the sub-health diagnostic framework for offshore wind assets under the “Clark Paradigm”.
Building upon the “Electromagnetic Ledger” established in Phase 1, this research focuses on capturing precursor signals and establishing proactive protection mechanisms. By deconstructing the temporal evolution of 1st-20th order harmonic fingerprints across dynamic conditions, this phase achieves a critical transition from pure diagnosis to “predictive protection”.
To ensure the transparency and reproducibility of our research, the supporting materials for this paper are organized as follows:
figures/: Contains all high-resolution figures, temporal evolution diagrams, and cross-terminal protection architectures presented in the manuscript.
data/: Contains the corresponding datasets (time-series dynamic data) used to generate each figure. Researchers can use this data to independently verify the precursor signal capture algorithms and protection logic.
🛠️ Technical Architecture
This phase extends the distributed hardware topology with advanced temporal analysis and control engines:
Perception Layer (Field Layer): MCU nodes deployed inside the turbine and at the substation inlet execute high-frequency sampling at 10.24 kHz, dynamically synchronized with mechanical variables (wind speed, RPM).
Audit & Protection Layer: Edge hosts execute predictive mapping and temporal correlation using Dynamic Causal Models (DCM) to track the time-lag () of fault contagion across nodes, generating corresponding protective actions.
🚀 Key Features
Precursor Signal Capture: Captures latent sub-health fingerprints (e.g., microscopic insulation degradation and dielectric drift) before catastrophic failures occur.
Dynamic Time-Lag (ΔT) Analysis: Analyzes the time delay between harmonic frequency shifts across different nodes, utilizing it as a robust precursor indicator of component stress.
Mechanical-Electrical Predictive Mapping: Establishes the causal correlation between wind turbine mechanical dynamics (aerodynamic turbulence, sudden speed shifts) and the resulting electrical harmonic fluctuations.
Proactive Protection Shield: Upgrades the causal auditing engine into a closed-loop protection system. It utilizes the time-lag window to issue dynamic early warnings and preemptively safeguards core power assets.
📊 Performance
By leveraging dynamic time-lag analysis, the framework successfully identifies precursor signatures of insulation aging and IGBT switching degradation hours to days prior to conventional threshold alarms. This significantly extends the critical response and protection window for O&M teams, averting major asset losses.
📚 Citation
If you utilize the concepts or content of this project in your research, please cite our manuscript:
APA Format:
Zeng, Y. (2026). Causal Auditing and Protection Paradigm for Sub-health Offshore Wind Assets based on Multi-terminal Harmonic Fingerprinting (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.20267143
BibTeX:
@misc{zeng2026clark_protection,
title={Causal Auditing and Protection Paradigm for Sub-health Offshore Wind Assets based on Multi-terminal Harmonic Fingerprinting},
author={Yi Zeng},
year={2026},
publisher={Zenodo},
version={v1.0.0},
doi={10.5281/zenodo.20267143},
url={https://doi.org/10.5281/zenodo.20267143}
}
Rights Statement: You are free to share and adapt this work, provided that you give appropriate credit to the author Yi Zeng (Project Shinar of Clark) and indicate if changes were made.
