Condition monitoring and fault warning of a ground network of hydropower station based on power internet of things

This study proposed the Power Internet of Things and Deep Learning-assisted Condition Monitoring and Fault Detection Model (PIoT-DL-CMFD) for effectively monitoring faults in the ground network of hydropower stations. Data segmentation is used first to reconstruct the raw vibration data, which may enhance training efficiency. Secondly, Long-Short-Term Memory (LSTM) can train the reconstruction data efficiently and adaptively under diverse operational conditions and fault factors. LSTM may then use network inference to detect the information fault classifications. Using the IoT, users can monitor storage conditions and control the devices by sending commands from any place in the world. The numerical findings illustrate that the recommended PIoT-DL-CMFD model enhances the fault prediction rate of 96.8%, accuracy ratio of 98.5%, overall performance ratio of 95.6%, water flow monitoring ratio of 94.5% and energy generation ratio of 97.2% compared to other popular methods.