Summary:
Due to the ever-increasing use of non-linear loads and their undesired effects on distribution systems operation, harmonic analysis should be taken into consideration. On the other hand, the probabilistic nature of power systems makes it necessary to consider the harmonic analysis in a probabilistic environment. In this paper, a data clustering based algorithm is used for probabilistic assessment of harmonic load flow, for the first time. Despite the previous probabilistic harmonic load flow (PHLF), in which uncertainties are considered on grid connected renewable generations, load demands, generators, transmission lines probable failure, etc., this paper considers uncertainties on the location and non-linear load portion of nodal loads. Moreover, an organized PHLF algorithm is formulated in this paper. In order to show the superior abilities of the proposed method, the method is applied on the IEEE 37 node test system and the results are compared by the Monte Carlo simulation (MCS) method.
Keywords: Data clustering; Distribution systems; Monte Carlo simulation; Non-linear loads; Probabilistic harmonic load flow; Uncertainty
JCR Impact Factor and WoS quartile: 3,211 - Q2 (2019); 3,900 - Q2 (2022)
DOI reference:
https://doi.org/10.1016/j.epsr.2019.105977
Published on paper: November 2019.
Published on-line: August 2019.
Citation:
S. Galvani, S. Rezaeian-Marjani, J. Morsali, M. Ahmadi Jirdehi, A new approach for probabilistic harmonic load flow in distribution systems based on data clustering. Electric Power Systems Research. Vol. 176, pp. 105977-1 - 105977-13, November 2019. [Online: August 2019]
