Adaptive Harmonic State Estimation Algorithm for Distribution Networks Considering Pseudo Measurement of New Energy

The distribution network environment is complex and has serious harmonic problems. The large-scale integration of new energy has exacerbated the diversification of harmonic sources and voltage distortion. Aiming at the poor estimation accuracy of traditional dynamic harmonic state estimation algorithm when there are time-varying noise and abrupt data, an adaptive harmonic state estimation algorithm for distribution networks considering new energy pseudo-measurement is proposed. Firstly, the Sage-Husa noise estimation method based on the forgetting factor is introduced to estimate the time-varying noise interference when the noise covariance is reduced in real-time. Then, an adaptive factor is constructed to online correct the error variance matrix, thereby reducing the prediction error caused by sudden load changes. Secondly, a confidence interval pseudo measurement generation method for quantile regression Bayesian gated recurrent neural network is proposed to solve the problem of missing short-term harmonic data in new energy and improve effect of the real-time harmonic state estimation. Finally, the effectiveness of the algorithm proposed in this paper is verified by an modified distribution network system.