Abstract
Harmonic impedance estimation and suitable evaluation index selection are the key steps of harmonic contribution evaluation. Most of the traditional harmonic impedance estimation methods are only applicable to the scenario, where the background harmonic is stable and the utility impedance is constant. However, this scenario will change in many cases due to the fluctuation of harmonic, the change of system operation mode and so on. In order to improve the estimation accuracy for harmonic impedance, a harmonic impedance estimation method is proposed in this paper based on similarity measure and ordering points to identify the clustering structure (OPTICS). The total harmonic contribution index and harmonic comprehensive contribution index are proposed based on subjective analytic hierarchy process to simplify the evaluation results. Simulation analysis and their comparison with the traditional methods reveal that the proposed harmonic impedance estimation method can reduce the influence of background harmonic voltage fluctuation and utility impedance change, making the harmonic impedance estimation result more accurate. Besides, the proposed total harmonic contribution index and harmonic comprehensive contribution index can effectively simplify contribution evaluation results, which provide a new methodology for the evaluation of harmonic contribution.
Highlights
With the access of high permeability distributed generation to power grid and the popularization of the electric vehicles, the harmonic pollution caused by a large number of power electronic devices such as rectifiers and inverters in power systems has become an increasingly serious problem [1]–[3]
The photovoltaic power source is affected by the light intensity, whose harmonic emission level and fluctuation degree are in a state of change throughout the day
The contributions of this paper can be summarized as follows: 1) The data segment of stable background harmonic voltage can be screened by similarity measurement, and the data of constant utility impedance can be clustered together by ordering points to identify the clustering structure (OPTICS), which improves estimation accuracy of the harmonic impedance
Summary
With the access of high permeability distributed generation to power grid and the popularization of the electric vehicles, the harmonic pollution caused by a large number of power electronic devices such as rectifiers and inverters in power systems has become an increasingly serious problem [1]–[3]. The linear regression method estimates the harmonic impedance by using the relation between harmonic voltage and harmonic current at the point of common coupling (PCC), but data filtering needs to be performed when the background voltage fluctuates [16], [17]. In this paper, considering the background harmonic voltage fluctuation and utility impedance change, a harmonic impedance estimation method is proposed, based on similarity measure algorithm and ordering points to identify the clustering structure (OPTICS). The contributions of this paper can be summarized as follows: 1) The data segment of stable background harmonic voltage can be screened by similarity measurement, and the data of constant utility impedance can be clustered together by OPTICS, which improves estimation accuracy of the harmonic impedance. 2) By using the subjective analytic hierarchy process, the evaluation indices of total harmonic contribution and harmonic comprehensive contribution are obtained, which can effectively simplify the evaluation results when multiple harmonic orders existing in the power grid
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
