Power equipment state estimation method and device based on improved LSTM
A technology of power equipment and state, which is applied in the direction of neural learning methods, prediction, biological neural network models, etc., can solve problems such as poor numerical quality, low value density of power equipment state data, and difficult to accurately predict the trend of the state of the equipment, so as to improve data quality. The effect of improving authenticity and prediction accuracy
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Embodiment 1
[0058] Specifically, the embodiment of the present application proposes an improved LSTM-based power equipment state estimation method, such as figure 1 shown, including:
[0059] 11. Import the power equipment status detection data into the gradient descent tree GBDT for data processing to obtain high-quality data sequences;
[0060] 12. Calculate the relative error between the power equipment detection data and the high-quality data sequence;
[0061] 13. Use the obtained relative error to improve the forgetting gate of LSTM;
[0062] 14. Import the high-quality data series into the improved LSTM to predict the state trend of power equipment.
[0063] In implementation, the technical solution proposed in the embodiment of this application includes two key points, data quality improvement and LSTM model improvement. The improvement of data quality includes using GBDT to fit the real trend distribution of data; the improvement of LSTM model is to improve the forgetting gate...
Embodiment 2
[0097] On the other hand, the embodiment of the present application proposes an improved LSTM-based power equipment state estimation device 3, such as image 3 shown, including:
[0098] The data processing unit 31 is used to import the power equipment state detection data into the gradient descent tree GBDT for data processing to obtain high-quality data sequences;
[0099] An error calculation unit 32, configured to calculate the relative error between the power equipment detection data and the high-quality data sequence;
[0100] The forget gate improvement unit 33 is used to improve the forget gate of LSTM by using the obtained relative error;
[0101] The trend prediction unit 34 is used to import high-quality data series into the improved LSTM to predict the state trend of electric equipment.
[0102] In implementation, the technical solution proposed in the embodiment of this application includes two key points, data quality improvement and LSTM model improvement. Th...
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