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Aiming at nonlinear and non-stationary of the fault signal of the photovoltaic grid inverter,a method of faults diagnosis was proposed based on empirical mode decomposition and sample en-tropy.Firstly, the original signal was decomposed with empirical mode decompose-tion(EMD) on the basis of theScharacteristics ofSadaptiveSmulti-resolution and a series ofSintrinsicSmode functions were obtained. The intrinsic mode functions containing the most information were chosen to extract fault information.Secondly, the sample entropy of the intrinsic mode functions containing the most information was calculated as the eigenvalues of normal signal .At last,the characteristic vectors were input BP neural network for pattern recognition to achieve the goal of fault diagnosis.The simulation results showed that the proposed method could extract fault feature effectively and the intrinsic mode functions and the sample entropy values were markedly different among different operating conditions.The feasibility and effectiveness of the method were proved by ComparedSwith theStraditionalSwaveletSpacket transform.