Research on Load Forecasting and Early Warning System of Distribution Network Equipment Based on Artificial Intelligence Technology

In this paper, the load prediction and early warning system of distribution network equipment is constructed by using remote monitoring technology, power network fault pattern recognition, high-speed system diagnosis technology and power network monitoring and evaluation system. Then, a heavy overlo...

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Bibliographic Details
Published in2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) pp. 1104 - 1110
Main Authors Yang, Kaiwen, Lin, Ke, Chen, Runhui
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.02.2023
Subjects
Alarm systems
Artificial Intelligence
Data visualization
Distribution Network
Distribution networks
Electric Power Equipment
Load Warning
Prediction algorithms
Software
Systems operation
Transformers
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Summary:In this paper, the load prediction and early warning system of distribution network equipment is constructed by using remote monitoring technology, power network fault pattern recognition, high-speed system diagnosis technology and power network monitoring and evaluation system. Then, a heavy overload warning framework is established to screen out the components of the distribution network with short term heavy overload risk. The fuzzy monitoring method is used to control the overflow of abnormal data in power dispatching system. Secondly, this paper carries out load distribution prediction and determines the user load superposition result based on the user cluster analysis results. By analyzing the main functions and software and hardware of the intelligent scheduling visualization system, the visualization display based on SVG technology is realized. This paper analyzes the implementation scheme of the early warning system, the prediction of the power network state and the real-time monitoring method of the power network fault. It illustrates the experimental results of system operation.
DOI:10.1109/EEBDA56825.2023.10090582