A prediction and early warning method and system for an overhead transmission line galloping risk
A technology for overhead transmission lines and risks, which is applied in the field of prediction and early warning methods and systems for galloping risks of overhead transmission lines, and can solve problems such as large differences in performance of prediction models and complicated training samples
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specific Embodiment 1
[0084] Such as figure 1 As shown, the embodiment of the present invention provides a method for predicting and early warning of galloping risks in overhead transmission lines, including:
[0085] collecting associated parameter data of galloping in the transmission line, wherein the associated parameter data includes ontology data and meteorological data;
[0086] Based on the ontology data and meteorological data, select the target historical data set as a training sample;
[0087] Establish a prediction model, and optimize the number of decision trees of the prediction model and the number of features in the split feature set;
[0088] Based on the screened target historical data set and the optimized prediction model, the galloping risk level of the line to be predicted is determined. For example, galloping risk levels can be set to four levels, and four galloping risk levels of A, B, C, and D are output after the line to be predicted is predicted.
[0089] The embodimen...
specific Embodiment 2
[0157] Such as image 3 As shown, the embodiment of the present invention provides a prediction and early warning system for galloping risks of overhead transmission lines, including:
[0158] A data collection unit 201, configured to collect associated parameter data of transmission line galloping, wherein the associated parameter data includes ontology data and meteorological data;
[0159] The training sample screening unit 202 is used to filter out the target historical data set as a training sample according to ontology data and meteorological data;
[0160] A prediction model optimization unit 203, configured to establish a prediction model, and optimize the number of decision trees of the prediction model and the number of features in the split feature set;
[0161] The galloping risk prediction unit 204 is configured to determine the galloping risk level of the line to be predicted based on the screened target historical data set and the optimized prediction model.
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