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Apprehending Fault Crises for an Autogenous Nanogrid System: Sustainable Buildings

Lookup NU author(s): Dr Muhammad Ramadan SaifuddinORCiD, Dr Thillainathan Logenthiran, Dr Naayagi Ramasamy, Dr Wai Lok Woo

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Abstract

IEEE This paper presents a novel approach for in-house operators to profile and allege fault interferences in a nanogrid system either locally or externally. The proposed hybridized methodology is modeled to apprehend, classify, and locate fault interventions using a heuristic data-driven processing model and a sensing directional flow theorem. Employment of fuzzy control and discrete Fourier transform systems are fused to recompose directional fault relay functionalities. The algorithm is composed of three computational stages: stage 1 quantifies conditional correlate coefficients (COC) based on current and phase angle features sampled at interconnecting feeders, stage 2 engages a fuzzy logic controller to express linguistic truth values against calculated COCs, and stage 3 directs circuit breaker operations advocating post phase-shift aberrations to isolate the faulted region. A nanogrid model inspired by Singapore’s Green Mark Building Incentive is developed in the MATLAB environment consisting of a combine heat and power microturbine, a rooftop photovoltaic system, and battery storage units tied to the 22-kVAC distribution network. Analytical results exhibit practicability and decisive settlements in diagnosing various types of fault crises despite low data logging signal-to-noise ratio. Conjointly, engagements of circuit breakers have rendered accurate switching operations toward isolating faulted regions.

Publication metadata

Author(s): Saifuddin MRBM, Logenthiran T, Naayagi RT, Woo WL

Publication type: Article

Publication status: Published

Journal: IEEE Systems Journal

Year: 2019

Volume: 13

Issue: 3

Pages: 3254-3265

Print publication date: 01/09/2019

Online publication date: 27/09/2018

Acceptance date: 23/06/2018

ISSN (print): 1932-8184

ISSN (electronic): 1937-9234

Publisher: IEEE

URL: https://doi.org/10.1109/JSYST.2018.2853078

DOI: 10.1109/JSYST.2018.2853078

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