arXiv:2609.16185v2 Announce Type: replace Abstract: Rapid proliferation of power electronic loads at the distribution grid edge has made accurate harmonic load modeling critical for power quality assessment, transformer derating, and grid planning. Existing Frequency Coupling Matrix (FCM) identification methods produce static, one-time models that rapidly lose fidelity as load composition evolves. This paper proposes a distributed edge-to-cloud architecture for autonomous, continual FCM re-identification under real field conditions, with two contributions: (i) a state-machine-driven pipeline that monitors point-on-wave (PoW) measurements and triggers FCM re-identification only when model accuracy falls below a set threshold; and (ii) an edge-to-server architecture confining waveform processing to a low-cost Raspberry Pi node, transmitting only compact JSON model updates upon confirmed change rather than streaming raw PoW data. Validated through a residential field deployment, the framework triggered retraining in 3 of 33 monitoring cycles and restored system modeling accuracy above the 80\% threshold after each event, reducing upstream data transmission by 99.7% relative to continuous streaming.