Ms. Xiang Chen Profile

Xiang Chen

at Wuhan University of Technology

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Proceedings Article | 7 September 2022 Paper

Research on abnormal power loss identification method of distribution transformer based on BP neural network

Xiang Chen

Proceedings Volume 12329, 123293C (2022) https://doi.org/10.1117/12.2646775

KEYWORDS: Transformers, Neural networks, Data modeling, Humidity, Signal processing, Neurons, Error analysis, Sensors

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As one of the most crucial electrical equipment in the power system, the reliable and stable operation of distribution transformer plays an important role. For the condition detection of the transformers, all the existing research methods are based on sensors to collect electrical data. In this paper, a new detection method of abnormal power loss identification, basing on BP Neural Network is proposed. Using this new method, the operating load, environmental parameters and the loss values of distribution transformer are collected to establish the BP neural network algorithm, which can simulate the complex relationship between load, environmental parameters and transformer loss. The trained neural network model can input characteristic parameters, output the predicted loss value and determine whether the measured loss is abnormal through hypothesis test, which is used to predict the operation fault of distribution transformer. Finally, a practical example is used to analyze the effectiveness of this method.

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