@ARTICLE{Wang_Siming_Fault_2023,
 author={Wang, Siming and Kaikai, Zhao},
 volume={vol. 72},
 number={No 2},
 journal={Archives of Electrical Engineering},
 pages={461-481},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences},
 abstract={As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.},
 type={Article},
 title={Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression},
 URL={http://www.czasopisma.pan.pl/Content/127613/PDF/art11_int.pdf},
 doi={10.24425/aee.2023.145420},
 keywords={distributed generation, distribution network fault location, fault type, Karrenbauertransform, SVR agent prediction model},
}