<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.</p>
</abstract>

Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually...

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Publicado en línea: 23 ago 2023

Páginas: -

Recibido: 12 oct 2022

Aceptado: 03 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00235

Palabras clave
Big data, Decision tree algorithm, Fuzzy set theory, Automated substation, Maintenance system

© 2023 Xinyue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Publicado en línea: 23 ago 2023

Páginas: -

Recibido: 12 oct 2022

Aceptado: 03 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00235

Palabras claveBig data, Decision tree algorithm, Fuzzy set theory, Automated substation, Maintenance system

© 2023 Xinyue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Big data, Decision tree algorithm, Fuzzy set theory, Automated substation, Maintenance system