Harmonic State Estimation Based on Network Equivalence and Closed Loop GAN

Harmonic state estimation is the main method of harmonic source location and harmonic responsibility division, also is a necessary means of reduction harmonic hazard. However, when some regions in the power system are unobservable, the traditional harmonic state estimation methods cannot estimate accurately due to the underdetermined state equations. In order to solve the above problems, a harmonic state estimation method based on the network equivalence and deep learning is proposed in this paper. Firstly, network equivalence and coefficient matching are proposed for locating harmonic source. Then, the harmonic state estimation model is constructed based on generative adversarial network (GAN), and the mechanism equation is fused in the objective function to make the model include the power grid information. Finally, the accuracy of the proposed method is verified on IEEE33 system. The results show that the proposed method can accurately locate harmonic source and has high precision of state estimation.