This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate are used to calculate the different learning rates for all parameters with stable gradient scaling. Meanwhile, the learning rate is dynamically attenuated according to the optimal interval. To prevent over-fitted, we exploit dropout technique to randomly reset some neurons and strengthen the information exchange between indirectly-linked neurons. Our proposed approach was utilized on four datasets to learn the faults diagnosis of oil-immersed power transformers. Besides, four benchmark cases in other fields were also utilized to illustrate its scalability. The simulation results show that the average diagnosis accuracies on the four datasets of our proposed method were 37.9%, 25.5%, 14.6%, 18.9%, and 11.2%, higher than international electro technical commission (IEC), Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), and grid search support vector machines (GSSVM), respectively.Energies 2019, 12, 995 2 of 16 commonly used fault diagnosis methods. However, these traditional methods have limitations. They may not be able to provide an interpretation to every possible combination of various ratios and may have excessively absolute coding boundary. Due to the objective uncertainty of transformer fault itself and the boundaries of the subjective judgment, it is difficult to meet the requirements of engineering application with the above ratio methods.Since the transformer faults are complex and concealed, simple and crude methods have difficulty performing effective diagnosis. It is essential to explore the principles, methods and means from various disciplines that are helpful in the fault diagnosis of transformers. With the rapid development of computer science and the rise of machine learning, multiple intelligent approaches such as artificial neural network [17][18][19], support vector machine (SVM) [20][21][22], fuzzy theory [23][24][25], extreme learning machine [26], and Bayesian network [27] have been applied in practice. A smart fault diagnostic approach based on integrating five interpretation methods using neural networks is proposed in [28]. Ma et al. [29] presented an intelligent framework for transformer condition monitoring and assessment. Within their framework, different intelligent algorithms can be effectively deployed. Peimankar et al. [30] first used multi-objective particle swarm optimization (PSO) algorithm to select the best subset of features corresponding to each fault class of power transformers. Then, they used ensemble learning systems to classify actual faults of transformers. Sherif et al. [31] utilized the thermodynamic theory to evaluate the severity based on the energy associated with each transformer fault type. These intelligent methods remedy the disadvantages of t...