Enhanced fault identification in grid-connected microgrid with SVM-based control algorithm

Abstract

The penetration of renewable energy sources, electric vehicles (EVs) and load dynamics, and network complexities often lead to nuisance tripping in grid-connected microgrids. Traditional protection methods fail to discriminate fault and other dynamic volatilities in the system. The paper presents a novel two-level adaptive relay algorithm to avoid nuisance tripping in a grid-connected microgrid under varying grid dynamics. The novelty of the adaptive relay algorithm is that nuisance tripping is eliminated by precisely determining normal system-level dynamics at the first level using a phase deviation reference block. The first level determines the necessity for activating the second level, which consists of a detection scheme combining a multiclass support vector machine (SVM) and discrete wavelet transform (DWT). The hybrid DWT-SVM methodology ensures effective fault diagnosis, adapting to variations in energy sources, load fluctuations, and fault scenarios. Real-time hardware-in-the-loop (HIL) simulation validates the system’s effectiveness in dynamic microgrid environments. Extensive experiments on scenarios, including faults, fluctuations in renewable energy generation, and intermittent simulations of EV charging and capacitor switching, were conducted to test the efficacy of the adaptive relay algorithm. Finally, experiments using OPAL-RT HIL real-time simulator and the Raspberry Pi microcontroller validated the adaptive relay algorithm in a grid-connected microgrid under varying grid dynamics.