1. Faulty Feeder Detection Based on Space Relative Distance for Compensated Distribution Network With IIDG Injections
- Author
-
Song Qin, Chen Qiaoshan, Xiangjun Zeng, Yuanyuan Wang, and Huang Xiancheng
- Subjects
Boosting (machine learning) ,Distribution networks ,Computer science ,Energy Engineering and Power Technology ,Kalman filter ,Current source ,Symmetrical components ,Control theory ,Electromagnetic coil ,Electrical and Electronic Engineering ,MATLAB ,Electrical impedance ,computer ,computer.programming_language - Abstract
To improve the accuracy of faulty feeder selection based on zero sequence current mutation, distribution networks with inverter-interfaced distributed generators (IIDGs) are analyzed in detail. It is found that the IIDGs cannot be completely equivalent to a positive sequence current source due to their boosting increments. Thereby, a more accurate IIDG current estimation model based on the Gauss-Seidel method that considers the control strategy at the moment of fault is established. Moreover, a real-time estimation algorithm is introduced for faulty feeder detection to adjust the compensation degree of the Petersen coil. On this basis, this paper proposes a novel faulty feeder detection approach using the zero-sequence current space relative distance between the increments of each feeder and an estimation of the faulty path generated by Kalman filtering (KAF). The feasibility of the proposed approach is demonstrated in both a PSCAD/EMTDC simulation and a MATLAB experimental laboratory. The test results indicate that the proposed approach can achieve a high speed when employed for different fault conditions.
- Published
- 2021