False Data Injection Screening for Temperature Monitoring of Distribution Transformers Based on Lightweight Blockchain and Winding Structural Fingerprinting

Temperature monitoring underpins the protection and condition assessment of distribution transformers, whose dominant failure mode is overheating-induced insulation aging; this makes temperature data the prime target of false data injection (FDI) attacks. Because FDI acts on data rather than on the physical asset, this paper exploits the winding structure, which data injection cannot alter, as a physical trust anchor: a structural fingerprint is extracted from port measurements through frequency-dependent harmonic resistance; the systematic temperature-response differences between wire-wound and foil-wound windings build an independent physical cross-check channel; and a lightweight PBFT consortium blockchain notarizes the fingerprint baseline so that a compromised local database cannot rewrite it. Structural, physical, and command consistency indicators classify input-, output-, and model-parameter-tampering attacks. Finite-element simulations and 100-round randomized attack tests show a 9.6% structural temperature deviation that provides a clear physical screening margin: the three attack types are detected and classified within 4 s at a 100% detection rate (95% CI [96.4, 100]%) and a 0.3% false-alarm rate, while a stealthy ramp injection is caught after about 105 min and a sub-threshold constant offset is caught by trend monitoring in about 4 h. A seven-node chain implemented with real ECDSA-secp256k1 signatures achieves 0.42–0.90 s finality per notarization, 8.9–19.0 notarizations/s, and a 0.47 s view change, two orders of magnitude above the field notarization demand, meeting lightweight deployment requirements.