This paper presents fault detection

classification

and location for a PV-Wind-based DC ring microgrid in the MATLABI/SIMULINK platform. Initially

DC fault signals are collected from local measurements to examine the outcomes of the proposed system. Accurate detection is carried out for all faults

(i.e.

cable and arc faults) under two cases of fault resistance and distance variation

with the assistance of primary and secondary detection techniques

i.e. second-order differential current derivative <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\left(\frac{d^2 / 3}{d t^2}\right)$ and sliding mode window-based Pearson's correlation coefficient. For fault classification a novel approach using modified multifractal detrended fluctuation analysis (M-MFDFA) is presented. The advantage of this method is its ability to estimate the local trends of any order polynomial function with the help of polynomial and trigonometric functions. It also doesn't require any signal processing algorithm for decomposition resulting and this results in a reduction of computational burden. The detected fault signals are directly passed through the M-MFDFA classifier for fault type classification. To enhance the performance of the proposed classifier

statistical data is obtained from the M-MFDFA feature vectors

and the obtained data is plotted in 2-D and 3-D scatter plots for better visualization. Accurate fault distance estimation is carried out for all types of faults in the DC ring bus microgrid with the assistance of recursive least squares with a forgetting factor (FF-RLS). To verify the performance and superiority of the proposed classifier

it is compared with existing classifiers in terms of features

classification accuracy (CA)

and relative computational time (RCT).