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11 September 2023 Method of detecting power equipment data based on LSTM-LOF
Zhicheng Ma, Jinxiong Zhao, Hong Zhao, Guangyuan Zheng, Baohui Wang
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Proceedings Volume 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023); 127790H (2023) https://doi.org/10.1117/12.2688734
Event: Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 2023, Kunming, China
Abstract
In the production work of the State Grid, the power equipment is often in a high and low temperature environment. The timely detection of the fault of the power equipment is an important part of the stable development of the State Grid. This paper proposes a method to detect the quality of power equipment based on the measurement data of power equipment at room temperature. Based on the LSTM model, the working data of power equipment is predicted in the form of serialized time nodes during the experiment. Then, the feature data is extracted and dimensionality reduction according to the operating environment, and finally the anomaly detection is carried out according to the Local Outlier Factor (LOF) algorithm. The experimental results show that the prediction effect of the model prediction diagnosis results is ideal, and the LSTM-LOF combined with the model can be used to assist engineers in the quality diagnosis of power equipment.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Zhicheng Ma, Jinxiong Zhao, Hong Zhao, Guangyuan Zheng, and Baohui Wang "Method of detecting power equipment data based on LSTM-LOF", Proc. SPIE 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 127790H (11 September 2023); https://doi.org/10.1117/12.2688734
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KEYWORDS
Data modeling
Detection and tracking algorithms
Machine learning
Transformers
Education and training
Principal component analysis
Instrument modeling
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