In existing estimation methods for utility harmonic impedance

it is commonly assumed that the harmonic impedance remains invariant

which often diverges from actual conditions. In practice

both the utility harmonic impedance and background harmonics typically time-varying with operating conditions. For the time interval between two sample points

large numerical value probably gives rise to more conspicuous difference between the impedance and the background harmonics at the corresponding time. Based upon the information of the sample points with a far gap

it is difficult to estimate the impedance value of the sample points of concern. As a consequence

a brand new time-varying utility harmonic impedance estimation method is put forward based on locally-geographically weighted regression. Firstly

a weight matrix is constructed based on time interval

assigning smaller weights to sample points with larger intervals from the points of interest. Locally weighted regression (LWR) is then applied to initially estimate the utility harmonic impedance and background harmonic reference values. Secondly

the impedance reference value is used to modify the regression equation to reduce the under determination of the original regression equation. To screen out the sample points that are similar to the background harmonics of the sample points of concern

the background harmonic reference value is simultaneously utilized as the prior information. On the basis of the screened samples

the background harmonic voltage and the utility harmonic impedance at each point are coped well with by geographically weighted regression (GWR). Under strong background harmonic fluctuations

the recommended method can not only identify abrupt changes in impedance

but also estimate the trend of utility harmonic impedance. Lastly

simulation and case studies demonstrate that the proposed method improves estimation accuracy by approximately 40% compared to traditional constant harmonic impedance estimation methods

and by around 30% compared to existing time-varying impedance estimation methods.