The hybrid renewable energy resources (HRES) with islanding detection presented in this work are micro grid-based and wellstructured. HRES is made up of a battery system, a Doubly-Fed Induction Generator (DFIG)-equipped wind energy conversion system, and independently regulated solar panels. Because the result of PV is not constant, an adaptive neuro fuzzy inference system (ANFIS) based MPPT is used to manage the upgraded Single-Ended Primary-Inductance Converter (SEPIC) converter. PWM rectifier is used for AC-DC conversion in WECS with DFIG, and a PI controller is used to control the rectifier. Using an Artificial-Neural-Network (ANN), the battery's State of Charge (SOC) is assessed. Recurrent Neural Network (RNN) classifier recognizes islanding as the primary issue in distributed generation (DG). It is faster and accurate. Through simulation in MATLAB, the proposed methodology is confirmed, and it is seen that the source current THD of 4.72 percent is achieved while the islanding is discovered in 0.8ms

PV, Improved SEPIC, ANN, Islanding detection, RNN, ANFIS, WECS, HRES