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The growing integration of renewable sources and the expansion of interconnected grids have heightened power systems’ susceptibility to parametric uncertainties. These include mechanical deterioration, turbine blade damage, reduced system inertia, and deliberate cyber-physical intrusions impacting turbine-governor assemblies, synchronous generators, and interconnecting transmission paths. Left unaddressed, such anomalies can seriously impair frequency and voltage stability, potentially escalating into widespread grid disturbances. While standard observer techniques successfully flag the presence of irregularities, they typically fall short in determining which specific parameter has deviated. The present work introduces a comprehensive and resilient Fault Detection and Isolation (FDI) methodology that can both estimate and accurately localize alterations in several fundamental dynamic parameters: turbine time constant (Tt), governor time constant (Tg), moment of inertia (H), load damping coefficient (D), and inter-area synchronizing coefficients (Tij). The approach combines a mixed H∞/H₂ observer augmented with regional pole-placement constraints—ensuring strong rejection of external disturbances and rapid residual generation—with a subsequent adaptive sliding-mode estimation stage tailored for precise parameter identification and fault classification. Thanks to this cascaded structure, the scheme reliably separates genuine parametric faults from measurement noise and distinguishes among different classes of parameter drift. Extensive simulations on a representative multi-area load frequency control (LFC) benchmark demonstrate the method’s high estimation accuracy and strong robustness across various fault magnitudes and types. In marked contrast to most existing FDI strategies, which are limited to detection, the proposed framework additionally pinpoints the physical or cyber origin of each fault. This added diagnostic capability supplies operators with concrete, decision-ready information and substantially bolsters the fault tolerance and overall dependability of large-scale modern power networks.