As the main protection of transformer, gas protection usually operates by mistake under outer zone fault of transformer, affecting safe and reliable operation of the transformer and power system seriously. Accurate identification of inner and outer zone faults of a transformer plays a crucial role in guaranteeing the reliability of a gas protection. Firstly, this paper presents a PSCAD model of a transformer that is applicable to study the behavior of a transformer under outer or inner zone fault, and verifies the consistency between the proposed model and the original model. The features extraction method for transformer under inner and outer zone faults is developed based on wavelet packet analysis. The characteristics of differential currents for inner and outer zone faults are analyzed by comparing individual wavelet packet energy. Furthermore, inner and outer zone fault identification method of transformer is proposed by using neural network according to differences of wavelet packet energies. Simulation results are provided to validate the correctness and validity of the proposed method.