Traditional transformer fault diagnosis methods have low accuracy and cannot be accurately classified. In order to improve the accuracy of transformer fault diagnosis, a SMOTE-RF-IHPO-DBN transformer fault diagnosis model is established. Firstly, the synthetic minority oversampling technique is used to balance the data. Then the random forest is used to reduce the dimensionality of the data to extract important information in the data and improve efficiency. Next, IHPO is used to optimize DBN, and finally use DBN to diagnose and classify fault types. The simulation results show that the accuracy of the proposed method is improved by 13.4%, 7.08%, 4.14% compared with the DBN, HPO-DBN and IHPO-DBN, respectively, which proves the effectiveness of the model.
KEYWORDS: Modeling, Switching, Design and modelling, Circuit switching, 3D modeling, Data modeling, Copper, Algorithm development, 3D applications, Visualization
In recent years, with the construction of electric energy and its related supporting infrastructure, the product iteration and update speed of the low-voltage complete switchgear industry has been gradually accelerated. Against the background of growing market demand year by year, adjusting product models according to customer requirements has gradually become the main production mode of low-voltage switchgear. In order to better improve the traditional production process, domestic and foreign research mainly revolves around the secondary development of various types of 3D CAD software. Although this can achieve a certain degree of design preview, it still lacks an effective interaction mechanism and intuitive modeling effect for the whole business flow. This paper is based on the framework of OpenCascade to parametrically model the low-voltage switchgear cabinet and key components. Combined with Qt as the front-end interaction, it provides a new solution for the customized production of low-voltage switchgear.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.