Parameter optimization-based method and system for predicting gas concentration in random forest oil
A random forest algorithm and concentration prediction technology, applied in the field of transformers, can solve the problems of poor practicability and robustness, ignore the problem of parameter values, affect the prediction results, etc., and achieve the effect of good applicability and feasibility
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Embodiment 1
[0046] This embodiment provides a method for predicting gas concentration in random forest oil based on parameter optimization, including:
[0047] Obtain the oil chromatographic data of the transformer;
[0048] According to the oil chromatographic data and the preset prediction model of the gas concentration in the oil, the gas concentration in the oil is obtained;
[0049] Wherein, the gas concentration prediction model in oil is obtained through random forest algorithm training; when predicting the gas concentration in oil, the number of decision trees in the random forest, the maximum depth of the decision tree, the minimum number of samples that can be divided into nodes, and the minimum number of leaf nodes The number of samples and the maximum number of leaf nodes are optimized for parameters, and then the optimized parameter values are brought into the random forest algorithm, and the concentration of dissolved gas in transformer oil is predicted.
[0050] In this ...
Embodiment 2
[0094] This embodiment provides a random forest oil gas concentration prediction system based on parameter optimization, including a data acquisition module and a concentration prediction module;
[0095] The data acquisition module is configured to: acquire the oil chromatographic data of the transformer;
[0096] The concentration prediction module is configured to: obtain the gas concentration in oil according to the oil chromatographic data and the preset gas concentration prediction model in oil;
[0097] Wherein, the gas concentration prediction model in oil is obtained through random forest algorithm training; when predicting the gas concentration in oil, the number of decision trees in the random forest, the maximum depth of the decision tree, the minimum number of samples that can be divided into nodes, and the minimum number of leaf nodes The number of samples and the maximum number of leaf nodes are optimized for parameters, and then the optimized parameter values ...
Embodiment 3
[0099] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method for predicting gas concentration in random forest oil based on parameter optimization described in Embodiment 1 are realized.
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