This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing traveling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults.

Keywords: SMES (superconducting magnetic energy storage) Battery Off-grid wind power system Battery lifetime model Discharge rate a b s t r a c tIn off-grid wind energy systems, batteries often undergo frequent charge/discharge cycles, which reduce battery service life. In addition, due to motor start and other high 'inrush current' loads batteries undergo high rates of discharge which also degrade battery life. In this paper, a superconducting magnetic energy storage and battery hybrid energy storage system is proposed, which is beneficial in reducing battery short term power cycling and high discharge currents. To demonstrate system performance, a representative off-grid wind power system model is described in detail which incorporates turbulent wind variations, load variations and energy storage systems. To estimate battery lifetime improvement, a novel battery lifetime model is described, which quantifies the impact of both the number of charge/discharge cycles and also the effect rate of discharge. The model is validated using previously reported data. This work advances previous studies by describing the estimated improvement in terms of battery life in a wind energy conversion application by use of superconducting energy storage and by presenting a novel method for doing so. In addition, the proposed battery lifetime model can be potentially used in other applications.

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Lactobacillus reuteri FN041 prevented HFD induced over weight gain, fat accumulation, endotoxaemia and mucosal-barrier damage, which is related to modulation of diurnal variation of serum lipid, hepatic Fas gene expression, diversity and metabolic activity of gut microbiota.