Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

• Published in: Energies (Basel) Vol. 15; no. 7; p. 2555
• Main Authors: Liu, Yingxiang, Ling, Wei, Young, Robert, Zia, Jalal, Cladouhos, Trenton T., Jafarpour, Behnam
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Ambient temperature; Cross correlation; Efficiency; Energy; Fault detection; geothermal operations; Geothermal power; Geothermal power plants; latent space dynamics; Monitoring; Multivariate analysis; neural network; Neural networks; Nuclear power plants; Physics; power plant; Power plants; Predictions; Principal components analysis; Simulation
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15072555

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.