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This book presents an islanding detection method based on frequency drifting method. This technigue based on sandia frequency shift (SFS) method that depends on perturbing the system with a distorted current waveform. When the grid is connected, the stability of the grid prevents any change although the inverter control tries to vary the frequency. When the grid is disconnected, the frequency error increases and the frequency of PCC changes and hence, UFP/OFP sends a trip signal to disconnect the inverter of the DG. SFS has a feedback gain in order to detect the island in a very short time. SFS performance depends on its parameter. These parameters must be controlled to improve the performance of SFS method that includes reducing non detection zone (NDZ), reducing total harmonic distortion (THD) and reducing the islanding detection time (IDT). To improve the performance of this method an artificial intelligence method based artificial immune system (AIS) applies to SFS method. AIS is an optimization method used to obtain an optimal value of SFS parameters. The proposed method is applied and tested to a three cases study and the results is compared with the conventional method.