Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

Haleem Medattil Ibrahim, M.I. (TU Delft Intelligent Electrical Power Grids; University of Petroleum and Energy Studies)
Sharma, Madhu (University of Petroleum and Energy Studies)
Subramaniam Rajkumar, Vetrivel (TU Delft Intelligent Electrical Power Grids)

2023

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units ((Formula presented.) PMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed (Formula presented.) PMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different (Formula presented.) PMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by (Formula presented.) PMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

μPMUs
fault detection
fault management
fault classification
section identification
distribution network
fault indicators
modelling
simulation
reliability indices

http://resolver.tudelft.nl/uuid:c2448a0b-35f0-4f0b-8677-6c7f78867cd7

https://doi.org/10.3390/en16114262

1996-1073

Energies, 16 (11)

Institutional Repository

journal article

© 2023 M.I. Haleem Medattil Ibrahim, Madhu Sharma, Vetrivel Subramaniam Rajkumar