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Accurate estimation of system frequency in real time is a prerequisite for the future smart grid, where the generation, loading, and topology will all be dynamically updated. In this article, we introduce a unified framework for the estimation of instantaneous frequency in both balanced and unbalanced conditions in a three-phase system, thus consolidating the existing approaches and providing next-generation solutions capable of joint adaptive frequency estimation and system fault identification. This is achieved by employing recent developments in the statistics of complex variables (augmented statistics) and the associated widely linear models, allowing us to benefit from a rigorous account of varying degrees of noncircularity corresponding to different sources of frequency variations. The advantages of such an approach are illustrated for both balanced and unbalanced conditions, including voltage sags, harmonics and supply-demand mismatch, all major obstacles for accurate frequency estimation in the smart grid.