KEYWORDS: Power grids, Data modeling, Mathematical optimization, Magnetism, Modal decomposition, Transformers, Feature extraction, Education and training, Statistical analysis, Solar processes
The geomagnetic induced current (GIC) generated by changes in the Earth's magnetic field caused by space weather activities such as solar wind poses a great threat to the safety of the power grid. The GIC prediction method is proposed based on an improved adaptive noise set empirical mode decomposition (ICEEMDAN) and an improved sparrow search algorithm (ISSA) to optimize the CNN-LSTM model, addressing the issues of complex and large errors in traditional GIC prediction processes. The results indicate that the ICEEMDAN-ISSA-CNN-LSTM model has more accurate prediction results.
Aiming at the high randomness and uncertainty of lightning trips in overhead transmission lines, the prediction accuracy and efficiency of lightning trips are low, and a prediction method of the lightning trip for overhead transmission lines based on CNN-GRU is proposed. Firstly, the feature set of lightning trip prediction influencing factors of overhead transmission lines is constructed based on the main characteristics influencing factors of overhead transmission lines and operating environment characteristics influencing factors. Secondly, the association rules are used to quantify the correlation between influencing factors and lightning trips. Finally, the Convolutional Neural Network (CNN) -gated Recurrent Unit (GRU) combined network is used to extract the high-dimensional internal connections between the influencing feature factors and line lightning tripping and train them. Combined with practical examples, the prediction results show that the CNN-URU prediction model proposed in this paper has higher prediction accuracy and efficiency than other prediction models.
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