Apprehending Fault Crises for an Autogenous Nanogrid System: Sustainable Buildings

Abstract

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.