Thermal power generation fault diagnosis and prediction model based on deep learning and multimedia systems

In this research, we propose the thermal power generation fault diagnosis and prediction model based on deep learning and multimedia systems. The application of multimedia technology in the power dispatching communication system not only greatly enhances the stability and reliability of the power system, but also enriches the application of science and technology in the power system. It is one of the main directions for the development of power communication and information processing systems. The paper’s novelty and contribution are major reflected from the three aspects. First, we optimize the traditional neural network model to fix it more suitable for multimedia applications. We improve the forecasting accuracy; and then for each type of sample of B-neural network model, the up-front of meteorological data. Second, the deep neural network is optimized for better evaluation efficiency. The number of convolution kernels in each convolutional layer in the network is different. The more the number of the post-convolution kernels, the more efficient the model will be. Therefore, the multi-kernel structure is proposed. Third, we integrate the multimedia into the prediction scenario to visualize the data and results. The experiment result is conducted to validate the performance of the proposed method. Results compared with the other state-of-the-art models demonstrate the robustness of our method.