Background Harmonic Probabilistic Model for Harmonic Responsibility Assessment Based on Nonparametric Methods

As the increasing integration of inverter-interfaced renewable energy generators intensifies the volatility and uncertainty of background harmonics in the utility system, it is crucial to construct a precise background harmonic model and determine the harmonic responsibility to promote harmonic management. This paper proposes a probabilistic model of background harmonics for harmonic responsibility assessment based on nonparametric methods. First, the amplitude data of background harmonic voltage are segmented in chronological order considering the stochastic features. Then, the nonparametric Bootstrap resampling is introduced to handle the case of insufficient samples, and the Kernel density estimation with optimal bandwidth is used to depict the probability distribution characteristics in each time interval. For the sake of identifying the piecewise probability density function with optimal fitting effect, the goodness-of-fit test is subsequently applied. Finally, based on the proposed background harmonic probabilistic model, the harmonic responsibility assessment is conducted without the involvement of phase angle information. The detailed performance verification of the proposed model is carried out in light of the field-measured data. The results validate that the proposed model outperforms the conventional parametric models in handling insufficient data and fitting accuracy and is helpful to achieve the effective quantification of the overall harmonic responsibility within a period of time. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.