The invention discloses a transformer state parameter data prediction method based on fruit fly algorithm optimization. The transformer state parameter data prediction method based on the fruit fly algorithm optimization comprises the following steps: S100, obtaining transformer state parameter data in a period of time, and converting the transformer state parameter data into a transformer state parameter matrix in a matrix form, wherein the transformer state parameter includes related data of the transformer state parameter; S200, constructing a transformer state parameter data prediction model, obtaining a hyper-parameter of the prediction model based on the fruit fly algorithm, and training the prediction model based on the transformer state parameter matrix; and S300, predicting the transformer state parameter data based on the transformer state parameter data prediction model trained by the step S200. The transformer state parameter data prediction method based on the fruit fly algorithm optimization can avoid the hyper-parameter selection falling into local convergence, thereby improving the training efficiency of the prediction model and ensuring the higher prediction accuracy and reliability of the transformer state parameter data. In addition, the invention also discloses a corresponding transformer state parameter data prediction system based on the fruit fly algorithm optimization.

Transformer state parameter data prediction method and system based on fruit fly algorithm optimization

The invention discloses a transformer state quantity data prediction method based on kernel principal component analysis optimization; the method comprises the following steps: S100: acquiring transformer state quantity data within a period of time, and converting the transformer state quantity data into a transformer state quantity matrix in a matrix form, wherein the transformer state quantity comprises relevant data of the transformer state quantity; S200: screening the transformer state quantity matrix X based on the kernel principal component analysis algorithm, and reserving the main state quantity to obtain a transformer main state quantity matrix; S300: constructing a transformer state quantity data prediction model and training the prediction model based on the transformer main state quantity matrix; and S400: predicting the state quantity data of the transformer based on the transformer state quantity data prediction model trained in the step S300. According to the method, the model training complexity can be reduced, and therefore high prediction accuracy of the transformer state quantity data is guaranteed. In addition, the invention further discloses a corresponding prediction system.

Transformer state quantity data prediction method and system based on kernel principal component analysis optimization

The invention discloses a prediction method for concentration of dissolved gas in transformer oil. The method comprises the following steps: besides original gas concentration, taking a specific valueof various gas concentrations as a reference vector; selecting a main parameter therein as an input training GRNN network according to a core principal element analyzing method. In order to solve theproblem that the conventional model is relatively low in prediction capability, a smooth factor parameter of the network is selected by using an improved fruit fly optimization algorithm in a construction process of the network. The effective information of an input vector is fully utilized and association among variables is considered, and the complex degree of calculation is also simplified, the manual intervene process is minimized and the prediction accuracy is improved.

Prediction method for concentration of dissolved gas in transformer oil

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Prediction method for concentration of dissolved gas in transformer oil