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In order to improve the accuracy of harmonic detection in the noisy environment and improve the stability of the Kalman filter, the covariance of the system noise is analyzed. By continuously recognizing the process noise covariance continuously, an adaptive process Noise covariance Kalman filter algorithm is presented. Firstly, the algorithm uses prioritized maximization of confidence to update prior information to identify process noise, Then iteratively calculates the amplitude and phase through Kalman filter. The biggest feature of this algorithm is that the sudden increase of the identified process noise Q is the occurrence of the sag of the harmonic amplitude. The simulation experimental results show that the proposed algorithm can track harmonic state of power system under quasi-steady state condition better than conventional Kalman, Kalman based on maximum likelihood criterion, wavelet / wavelet packet Compared with the transform, the adaptive algorithm has faster convergence speed, higher filtering precision, better real-time and stability, and has important practical significance.