Abstract
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.
Highlights
Power transformers are important equipment in power systems; their operational conditions directly affect the security and stability of the power grid
To further verify the effectiveness of the proposed approach, the international electro technical commission (IEC) 60599 [16], Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), grid search support vector machines (GSSVM), and dropout based deep neural network (DADDNN) methods were performed with our four datasets
Dynamic Adam and dropout based deep neural network (DNN) is well-suited to the diagnosis problems
Summary
Power transformers are important equipment in power systems; their operational conditions directly affect the security and stability of the power grid. Various preventive tests including insulation dielectric spectrum analysis [3,4,5,6], partial discharge method [7,8], and dissolved gas analysis (DGA) can accurately reflect the performance and state of all aspects and parts of the power transformer to a certain extent In these testing items, the dissolved gas analysis is an important approach of transformer internal fault diagnosis. Examples include current transformer (CT) saturation classification using unsupervised learning [35], transformer fault diagnosis using deep belief network with non-code ratio [36], and vibration signals over cloud environment [37] These DNN based methods are progressive and useful for complex multi-category judgment problems in CT saturation classification and fault diagnosis, they fail to build an efficient model with dynamical and adaptive learning rates for different parameters.
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