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.
Highlights
The carbon neutrality target by countries worldwide has raised the demand in combining the power system with energy storage units, in order to buffer the system instability brought by high penetration of the renewable energy system (Hunt et al, 2020; Feng et al, 2021)
This study aims at encapsulating the operation status of main transformers in pumped storage hydropower (PSH) from visualized vibration data by using advanced deep learning methods
The data are from six main transformers of a pumped storage power station, all of which have been running for more than 10 years
Summary
The carbon neutrality target by countries worldwide has raised the demand in combining the power system with energy storage units, in order to buffer the system instability brought by high penetration of the renewable energy system (Hunt et al, 2020; Feng et al, 2021). PSH is one of the most costeffective utility-scale option for grid energy storage (Hou et al, 2018), with the advantages of providing clean and affordable ways of storing and deploying electricity, as well as supporting ancillary services such as network frequency control and reserve generation (Zhao et al, 2021). The excitation state of the main transformer in PSH might change intensely in a few hours; the system stability of the main transformer is essential for the operation reliability of PSH. The development of the AC/DC hybrid power grid and the application of large power electronic equipment have made it possible for the main transformer to withstand various operating overvoltage, excitation inrush current, and the resulting electric stress and thermal stress of winding under DC bias and high frequency harmonic, such that it is closely combined with regional rail transportation network transformer which is affected by high-frequency harmonics. The application of a large number of power electronic devices makes the electromagnetic environment in the transformer more complicated
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