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Microgrid, which is one of the main foundations of the future grid, inherits many properties of the smart grid such as, self‐healing capability, real‐time monitoring, advanced two‐way communication systems, low voltage ride through capability of distributed generator (DG) units, and high penetration of DGs. These substantial changes in properties and capabilities of the future grid result in significant protection challenges such as bidirectional fault current, various levels of fault current under different operating conditions, necessity of standards for automation system, cyber security issues, as well as, designing an appropriate grounding system, fast fault detection/location method, the need for an efficient circuit breaker for DC microgrids. Due to these new challenges in microgrid protection, the conventional protection strategies have to be either modified or substituted with new ones. This study aims to provide a comprehensive review of the protection challenges in AC and DC microgrids and available solutions to deal with them. Future trends in microgrid protection are also briefly discussed.
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Travelling wave‐based fault detection and location in a real low‐voltage DC microgrid
Abstract This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.
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- Award ID(s):
- 2338555
- PAR ID:
- 10571379
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Smart Grid
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2515-2947
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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