Power transmission line icing thickness prediction method based on CEEMDAN-QFOA-LSTM

Abstract

The invention discloses a power transmission line icing thickness prediction method based on CEEMDAN-QFOA-LSTM, and relates to the field of combination of power transmission line state evaluation anddeep learning. The method comprises the following steps o: (1) carrying out data acquisition and preprocessing; (2) carrying out CEEMDAN decomposition on an icing thickness historical data sequence (12); (3) optimizing hyper-parameters of the LSTM by a quantum drosophila melanogaster algorithm; (4) carrying out LSTM model training (14); and (5) predicting the icing thickness of a power transmission line and analyzing a result (15). According to the method, the CEEMDAN decomposition algorithm is used, a sequence which is difficult to directly predict is converted into a plurality of predictablecomponent sequences; a neural network can more accurately grasp the law of the sequence according to multi-dimensional feature information obtained through decomposition; a QFOA optimization algorithm is used for obtaining the hyper-parameters, a complex manual parameter adjustment process is avoided, and a network model is trained more effectively; the used LSTM neural network does not have theproblem of gradient disappearance of a general network, so that optimal convergence of the model is ensured, and the problem of short-term and long-term time sequence prediction is effectively solved.

Description

technical field [0001] The invention belongs to the field of the combination of transmission line state evaluation and deep learning, and in particular relates to a method for predicting the icing thickness of transmission lines based on CEEMDAN-QFOA-LSTM. Background technique [0002] With the rapid development of the smart grid, transmission line components have become the largest and most important part of the power grid. The safe and stable operation of the line is of great significance to the development of the national economy and the normal production and life of the people. With the continuous expansion of transmission lines, line corridors gradually extend to areas prone to ice disasters. At the same time, they are directly exposed to the external environment and face the threat of severe cold environmental factors. Therefore, it is necessary to design a method for predicting ice thickness. It provides a reliable reference basis for ice plan formulation and line mai...

AI Technical Summary

Problems solved by technology

[0004] At present, most of the methods are based on the state classification of the surface icing characteristics of transmission lines and other power transmission equipment, and the value of the icing thickness cannot be clearly obtained. The reason is that the icing thickness is affected by various factors. The data of all relevant factors cannot be obtained, and it is difficult to establish an accurate ice thickness prediction model on this data only by relying on traditional data mining or a single deep learning network

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