The top oil temperature index of transformer is an important indicator of transformer load capacity and service life. In this paper, the grey prediction model is used to predict the transformer oil temperature initially, and then the autoregressive differential moving average model is used to fit and train the deviation sequence for its prediction deviation. Finally, the optimized prediction model is obtained. The obtained transformer top oil temperature value is divided into training set data and test set data, and divided according to corresponding proportion, so as to achieve accurate prediction of transformer top oil temperature. The experimental results show that the optimized grey-autoregressive differential moving average model prediction algorithm has good prediction effect.
Through the compatibility mode of HTML5 development and mobile native development, starting from the migration efficiency of native mobile applications, the security of power dispatching system and the convenience of data planning of power dispatching system, and through the cross platform characteristics of HTML5 and the multi-stage protection of network and native environment, this paper realizes a set of mobile application platform suitable for multi terminal integrated power dispatching.
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