In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, heavy medium coal preparation, ray projection coal preparation, etc. these methods can not separate coal and gangue under the condition of safety and speed at the same time. Therefore, to improve the recognition rate of coal gangue separation, a coal gangue recognition method based on improved Support Vector Machine is proposed in this paper. First, the images of the coal and gangue are preprocessed. Then, the gray and texture features of the coal and gangue are extracted from the preprocessed images. Finally, each feature vector is input into the Support Vector Machine model optimized by Fruit Fly for recognition and classification. The experimental results show that the accuracy is 96.33%.
Transmission line is related to the security and stability of the power system, and it is the basis of the ensuring power supply. It is very important to ensure the normal operation of the transmission line and timely find and repair the faulty transmission line. This requires accurate identification of the fault type and the distance when a fault occurs. Therefore, this paper comprehensively summarizes the transmission line fault diagnosis technology and its research status. And the fault analysis method, traveling wave method, intelligent positioning method and other methods are analyzed and combed. On this basis, the research and application prospects of the artificial intelligence positioning method based on deep learning are summarized and prospected.
To realize the fault diagnoses as power transmissions, this paper first introduces the online monitoring methods by using sensors. Then, the traveling wave methods and the Wavelet methods for the fault locations of transmission lines are summarized according to the real-time operational data and the environmental variation data. Furthermore, the neural networks and the genetic algorithms for fault recognition are summarized according to the online monitoring data. Finally, this paper discusses the principles, the advantages, and the shortcomings of different methods, which establish the foundations for intelligent diagnosis of power transmissions.
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