Detection method of abnormal operation state of electrical equipment based on PLC technology

Li Gao

doi:10.1117/12.3024837

13 May 2024 Detection method of abnormal operation state of electrical equipment based on PLC technology

Li Gao

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131595L (2024) https://doi.org/10.1117/12.3024837
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

At present, the detection nodes of abnormal operation state of electrical equipment are mostly set as independent structures, and the detection range of operation state is small, which leads to the increase of false detection times. Therefore, the design and verification analysis of abnormal operation state detection method of electrical equipment based on PLC technology are proposed. According to the current test requirements and the changes of standards, firstly, the characteristics of abnormal detection of electrical equipment are extracted, and the actual operating state detection range is expanded by multi-objective method, and multi-objective dynamic detection nodes are deployed. Based on this, the abnormal operating state detection model of PLC electrical equipment is constructed, and the abnormal detection of equipment is realized by two-stage cross-correction. The final test results show that the final number of false detections can reach more than 20 times for the selected eight electrical equipments, which shows that with the help of PLC technology, the designed method for detecting abnormal operation state of electrical equipment is more efficient and stable, and has practical application value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Gao "Detection method of abnormal operation state of electrical equipment based on PLC technology", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131595L (13 May 2024); https://doi.org/10.1117/12.3024837

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