Dynamic Bilinear WLAV Harmonic State Estimation Based on Kalman Filter

As power electronics technology advances, a significant amount of new energy is being integrated into the distribution network, which makes the harmonic pollution of the power system serious. Therefore, it is necessary to obtain the harmonic states of the grid through harmonic state estimation to target the harmonic pollution. The traditional harmonic state estimation generally needs to solve a nonlinear nonconvex optimization problem, which may lead to the result is only a locally optimal solution, and there is also the problem of convergence difficulty in the computation. To address this, proposed a dynamic bilinear weighted least absolute value (WLAV) harmonic state estimation based on the Kalman filter. The method linearizes the nonlinear measurement equation by introducing auxiliary measurement vectors and auxiliary state vectors, which effectively solves the global optimization and convergence problems. Meanwhile, the Kalman filter can be utilized to track the time change of the harmonic states in real time and realize the accurate dynamic estimation. Finally, the feasibility of the method is verified based on simulation examples.