Prediction Method of Dissolved Gas in Transformer Oil Based on Firefly Algorithm - Random Forest

Dissolved gas in oil is an important parameter to reflect the operation state of the transformer. By analyzing the volume fraction of different fault characteristic gases, it can effectively judge the fault condition and fault type of transformer, while predicting the content of dissolved gas in transformer oil can make timely warning before further deterioration of the fault to avoid the occurrence of insulation breakdown. Therefore, a prediction model of dissolved gas concentration in transformer oil based on the firefly optimized support vector machine is proposed. In order to overcome the difficulty in parameter selection of traditional random forest model, the firefly algorithm is used to adjust the parameters in RF. The test results show that FA can effectively improve the prediction accuracy of RF, and the FA-RF model has higher prediction accuracy than existing prediction methods, which can better predict the change of gas volume fraction in oil and prevent serious faults.