The massive integration of distributed renewable energy sources and nonlinear power electronic equipment has given rise to power quality issues such as waveform distortion, voltage instability, and increased harmonic components. Nowadays, the pollution of power quality is becoming increasingly severe, posing a potential threat to the security of the power grid and the stable operation of electrical equipment. Due to the presence of significant noise interference in the collected signals, existing methods still face issues such as low accuracy in disturbance identification and high computational complexity. To address these problems, this paper proposes a hybrid data-driven strategy that can significantly improve the accuracy and speed of identification. Firstly, the wavelet packet transform (WPT) method is employed to denoise the power disturbance signals. Subsequently, the local mean decomposition (LMD) algorithm is used to adaptively decompose the nonlinear and complex time series into multiple product function components. Feature extraction of the disturbance signals is then achieved by calculating entropy values after local mean decomposition, and a feature matrix is constructed from the entropy values of each component for analysis in disturbance identification. Finally, an extreme learning machine (ELM) is employed for the identification and classification of transient power disturbance signals. The verification of numerical examples demonstrates the feasibility and effectiveness of the proposed method in this paper.