Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

Abstract

Hydroelectric energy storage, that is, pumped storage hydropower (PSH) is considered as the essential solution for grid reliability with high penetration of renewable power, due to its advantages of cost-effectiveness for grid energy storage as well as supporting ancillary services. However, the operation modes of the main transformer unit in PSH are way more complex than the conventional power transformer, which makes the condition monitoring and fault detection of PSH becoming a technical challenge. In this article, an operation status recognition model of main transformers in PSH based on artificial visualization of mechanical vibration signals and deep learning is proposed. The vibration signals on a series of 500 kV/360 MVA main transformers of PSH are monitored periodically by contacting sensor arrays. These vibration signals are processed into nephograms by using linear interpolation fitting and 1D to 2D data mapping. A deep learning method based on the convolutional neural network (CNN) is used to classify nephograms obtained under different operation modes, that is, no load, full load, DC bias, and short circuit. The proposed status prediction algorithm was trained and tested through 150 sets of vibration nephogram samples, which ensures the feasibility of the nephogram generation method and the performance of the classifier. The testing results show that the overall status prediction accuracy for the proposed algorithm achieves 89.7% when the network structure is optimized. It is indicated that the mechanical vibration of the main transformer has a pattern matching relationship with the operating state of PSH. In practice, the operating status of PSH can be diagnosed remotely by embedded IoT sensors; the health index of PSH can also be estimated by weighed analysis of the changing trend of vibration data obtained in the life cycle.