Due to limited amplitude and controlled phase of current supplied by inverter-interfaced renewable power plants (IIRPPs), the IIRPP-side distance protection of lines connected to IIRPPs fails to detect the fault location accurately, so it may malfunction. The composite sequence network of a line connected to an IIRPP during asymmetrical faults is analyzed, and an adaptive distance protection based on the analytical model of additional impedance is proposed in this study. Based on open circuit property of negative-sequence network at the IIRPP-side, the equivalent impedance of power grid and current flowing through fault point are calculated in real-time using local measurements, which are substituted into the analytical model of additional impedance to calculate fault location. In the case of negative-sequence reactive current injection from IIRPPs during asymmetrical faults, the error of calculating fault point current from local measurements is analyzed and corrected to ensure reliability of the proposed protection. The proposed protection alleviates the effect of fault resistance in a system with weak sources. In addition, the proposed protection can adapt to different grid codes (GCs), the operation mode change of the power grid, and the capacity change of the IIRPP. PSCAD/EMTDC test results verify the effectiveness of the proposed protection.
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WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.
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- Award ID(s):
- 1809739
- NSF-PAR ID:
- 10343770
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 11
- Issue:
- 24
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 12148
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app112412148
