Voltage sag is a typical power quality disturbance. Identifying the type of disturbance source causing voltage sag accurately is one of the important matters in power quality monitoring and management. Due to the correlativity and redundancy of the features, the identification method for voltage sag disturbance source has low accuracy. To resolve the problem, this paper proposes a method of feature reduction of voltage sag disturbance based on principal component analysis (PCA). Through the analysis of single disturbance source of voltage sag and complex disturbance source of voltage sag, multiple feature indices of voltage sag are obtained using wavelet coefficients in terms of statistics, wave morphology, entropy, energy, etc. These original feature indices are correlative and redundant. Based on PCA, the original feature indices are normalized, and then the covariance matrix is calculated, a couple of comprehensive feature indices after reduction can be obtained lastly. The correlativity and redundancy of the comprehensive feature indices are eliminated effectively. General classifier is used to verify the method. The simulation results show that comprehensive feature indices after reduction can effectively reduce the number of feature vectors which are input to SVM and the identification accuracy which is obtained using comprehensive feature indices is higher than the original features indices in the classification and identification of single and complex disturbance source of voltage sag under different noisy conditions.