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• W4382998700 abstract "Power system condition prediction is to predict key parameters such as voltage and current of its main components to ensure the reliability and safety of electrical system operation. However, as satellite missions and operating conditions become more complex, the state of electrical systems is affected by satellite loads and platforms. Therefore, it is difficult for offline-trained state prediction models to dynamically adapt to changing operating conditions and changing relationships between parameters. To overcome these problems, an online deep learning method, On-line Rules-limited Temporal Convolutional Network (O-R-TCN ), is proposed to improve the adaptability of the state prediction method. Firstly, a temporal convolutional network (TCN) online weight update method based on a recursive extreme learning machine is proposed, which realizes the self-learning of dynamic parameter relations under dynamic operation conditions to obtain higher prediction accuracy. Moreover, considering that anomalies within the monitored data may lead to undesired model updating, an association rule mining approach is proposed based on the Frequent Pattern-Growth (FP-Growth) fusion with trend sign aggregation approximation (TSAX). Avoid model updates caused by system exceptions or failures. Experiments are implemented based on the actual satellite power system telemetry data. The results illustrate that the proposed method can self-learn the varying relationship between the parameters and update the state prediction model during the working state change. At the same time, abnormal data can be effectively detected by the association rule, which ensures the effectiveness of the proposed method." @default.
• W4382998700 created "2023-07-04" @default.
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• W4382998700 date "2023-01-01" @default.
• W4382998700 modified "2023-10-15" @default.
• W4382998700 title "Satellite Power System State Prediction based on Online Learning with Parameter Association Rules" @default.
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• W4382998700 doi "https://doi.org/10.1109/tim.2023.3291798" @default.
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