Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations
This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of exogenous variables, such as control adjustment and ambient temperature. In the LSDNN model, an encoder–decoder architecture was designed to capture cross-correlation among different measured variables. In addition, a latent space dynamic structure was proposed to propagate the dynamics in the latent space to enable prediction. The prediction power of the LSDNN was utilized for monitoring a geothermal power plant and detecting abnormal events. The model was integrated with principal component analysis (PCA)-based process monitoring techniques to develop a fault-detection procedure. The performance of the proposed LSDNN model and fault detection approach was demonstrated using field data collected from a geothermal power plant.
- Research Organization:
- Univ. of Southern California, Los Angeles, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- EE0008765
- OSTI ID:
- 1860536
- Alternate ID(s):
- OSTI ID: 1981133; OSTI ID: 2320262
- Journal Information:
- Energies, Journal Name: Energies Vol. 15 Journal Issue: 7; ISSN 1996-1073
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- Switzerland
- Language:
- English
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