Xiangyang Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiangyang Liu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
Yiming Guo
School of Electrical Engineering and Automation, Henan Polytechnic University, 454000 Jiaozuo, China
2 shared publicationsHaizhu Yang
School of Electrical Engineering and Automation, Henan Polytechnic University, 454000 Jiaozuo, China
1 shared publication- Haizhu Yang
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
1 shared publication - Haoyang Cui
School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China
1 shared publication - Hong Qian
School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
1 shared publication Peng Zhang
School of Electrical Engineering and Automation, Tianjin University, 300072 Tianjin, China
1 shared publication
Latest Publications
- Open Access
A Novel Fault Location Method for MMC-HVDC Grid Based on Gram Angle Difference Field
When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions.
Open AccessA Novel Fault Location Method for MMC-HVDC Grid Based on Gram Angle Difference Field
When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions.
- Open Access
- 23 March 2021
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2021, 1-10
Corrigendum to “Fault Location of Active Distribution Networks Based on the Golden Section Method”
- 23 March 2021
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2021, 1-10
Open AccessCorrigendum to “Fault Location of Active Distribution Networks Based on the Golden Section Method”
- Open Access
- 8 February 2020
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2020, 1-9
Fault Location of Active Distribution Networks Based on the Golden Section Method
Aiming at the problem of fault location in distribution networks with distributed energy resources (DERs), a fault location method based on the concepts of minimum fault reactance and golden section is proposed in this paper. Considering the influence of distributed energy resource supply on fault point current in distribution networks, an improved trapezoidal iteration method is proposed for load flow analysis and fault current calculation. This method only needs to measure the synchronous current of the distributed energy resource and does not need to measure the voltage information. Therefore, the investment in equipment is reduced. Validation is made using the IEEE 34-node test feeder. The simulation results show that the method is suitable for fault location of distribution networks with multiple distributed generators. This method can accurately locate the faults of the active distribution network under different conditions.
- 8 February 2020
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2020, 1-9
Open AccessFault Location of Active Distribution Networks Based on the Golden Section Method
Aiming at the problem of fault location in distribution networks with distributed energy resources (DERs), a fault location method based on the concepts of minimum fault reactance and golden section is proposed in this paper. Considering the influence of distributed energy resource supply on fault point current in distribution networks, an improved trapezoidal iteration method is proposed for load flow analysis and fault current calculation. This method only needs to measure the synchronous current of the distributed energy resource and does not need to measure the voltage information. Therefore, the investment in equipment is reduced. Validation is made using the IEEE 34-node test feeder. The simulation results show that the method is suitable for fault location of distribution networks with multiple distributed generators. This method can accurately locate the faults of the active distribution network under different conditions.