Spatio-Temporal Fault Contagion Tracking and Predictive Asset Management for Offshore Wind Farms Based on Multi-Terminal Harmonic Fingerprints

Core Content Description Research Context: As offshore wind farms evolve into deep-water, large-scale clusters, localized "sub-health" states no longer remain isolated; they propagate through complex electrical coupling into field-wide chain reactions. This paper confronts the challenge of fault contagion—the silent, networked spread of anomalies across assets. By shifting the perspective from individual component monitoring to Spatio-Temporal Causal Tracking, we aim to decode the dynamic evolution of failures before they paralyze the entire wind farm infrastructure. Proposed Framework: Serving as the "Pathology & Prognosis" phase (Phase 3) of the highly acclaimed Clark Paradigm, this paper introduces a holographic Fault Contagion Tracking and Predictive Asset Management Framework. It evolves the "Preemptive Protection" of Phase 2 into a system-wide Digital Immune System. By quantifying the exact Propagation Delay (ΔtΔt) across a "5-Level 7-Node" multi-terminal topology, it transforms offshore assets from vulnerable nodes into a self-auditing, resilient network. Technical Methodology: Two-Stage Collaborative Intelligence: Beyond binary alarms, we implement a dual-engine architecture. Stage 1 (CNN Trigger) performs real-time scans of multi-terminal harmonic matrices to capture the "Neural Envelope"—microscopic distortions that signal the initial onset (tchangetchange) of an anomaly with millisecond precision. Spatio-Temporal Algorithmic Engine: Powered by a Transformer-based analyzer with self-attention mechanisms, the engine decodes the hidden contagion logic across the "5-Level 7-Node" topology. It tracks the exact trajectory of "sub-health" signatures as they infect the network, predicting the time remaining until substantive organic damage occurs. Decision Philosophy ("Lesion Elimination" Economics): We establish a proactive "Lesion Elimination" strategy. By securing a 10-30s golden window—sufficient for smooth power derating or active current limiting—the system neutralizes the pathological fault before it causes irreversible destruction to high-value assets. Hardware Architecture:A distributed edge-computing topology designed for high-fidelity auditing. The sensing layer executes synchronized sampling at 12.8 kHz to capture the 1st-50th harmonic fingerprints. The system establishes a "Physical Degradation Mapping Dictionary," allowing standard terminals to inverse-infer non-visible processes such as "cable water tree aging" or "tower mechanical resonance" by analyzing low-to-mid frequency spectral shifts. Experimental Results:Rigorous validation via a high-fidelity Digital Twin platform proves that the CNN-Transformer hybrid outperforms traditional LSTM or data-driven models in tracking cascading failures. The framework significantly reduces contagion delay prediction error and provides ample warning lead time. This critical window effectively mitigates cascading trip-off risks under extreme operating conditions and maximizes residual asset value. 🔗 Clark-Paradigm-Initiative / paper-3-fault-correlation