KEYWORDS: Air temperature, Ice, Relative humidity, Random forests, Power grids, Decision trees, Machine learning, Education and training, Modeling, Wind speed
Plateau mountainous regions exhibit complex and diverse terrain characteristics, with varying altitudes and extensive land coverage. These areas are subject to a complex and diverse climate, particularly during the period from November to March, where the intricate microtopography in the plateau mountainous areas leads to highly complicated local weather conditions. Under adverse geographical and micro-meteorological environments, these regions are prone to transmission line icing disasters. The main factors influencing ice accretion on vulnerable microtopography differ according to different microtopographic types. This study focuses on the Guizhou region as the research object and divides it into different zones based on its geographical and climatic conditions. Within these zones, analysis and modeling are conducted for ridge lines, valleys, passes, and windward slopes. Finally, through cross-validation of the models and comprehensive analysis of the results, the key factors influencing ice accretion on various types of vulnerable microtopography in different zones are identified.
Icing of transmission lines has always been a pain point for grid companies. The economic and property losses caused by icing every winter are huge. How to make an effective prediction of transmission line icing is a difficult problem. Existing forecasting methods are often based on micro-meteorological and micro-topographic information. In the characteristic variables of micro-meteorology and micro-topography, there are often interdependencies and potential spatial correlations. However, existing icing prediction methods do not fully exploit the interactions among these characteristic variables. Therefore, this paper proposes a transmission line icing prediction model based on the feature map structure, which reveals the potential agnostic topological relationship between the feature variables by adaptively extracting the sparse adjacency matrix between the feature variables. In addition, while the dilated convolution can improve the receptive field, there is also a loss of information continuity due to the discontinuity of the convolution kernel of the dilated convolution. We propose a temporal capture module to improve the loss of information continuity through GRU and dilated convolution in parallel. End-to-end prediction is achieved by stacking a graph convolution module and a temporal capture module, and after conducting several experimental comparisons, the effective prediction of the proposed model is validated.
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