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This paper proposes a wavelet-based extended fuzzy reasoning approach to power-quality disturbance recognition and identification. To extract power-quality disturbance features, the energy distribution of the wavelet part at each decomposition level is introduced and its calculation mathematically established. Based on these features, rule bases are generated for extended fuzzy reasoning. The power-quality disturbance features are finally mapped into a real number, in terms of which different power-quality disturbance waveforms are classified. Numerical results obtained from a large database show that the disturbance waveforms such as high- and low-frequency capacitor switching, voltage sag, impulsive transient, transformer energizing, and perfect sine waveform can all be correctly identified. The effect of the amplitude and frequency content of power-quality disturbance on the energy distribution patterns and the effect of noise on classification accuracy are also discussed in the paper.