The geothermal power plants in Japan have a low plant capacity factor. In the geothermal power plant under study, a problem is that the pressure of the steam production wells frequently drops suddenly. The plant operators have recovered the steam production well to a stable state through the valve operation based on their experience when the well is unstable. However, the same operation, such as opening a valve, may result in a decrease in reservoir pressure which can be accelerated depending on the reservoir recharge conditions. The objective of this study was to examine wellhead valves operation to prevent sudden pressure drop. This study developed a machine learning (ML) model to predict time-series of steam enthalpy from inputs such as valve openings using Temporal Fusion Transformer (TFT) architecture. The ML model was confirmed to function as a simulator to predict the behavior of the steam enthalpy when the valve openings are varied. The ML model was also able to detect the rapid enthalpy decrease from 25 hours before the happened. Furthermore, we confirmed that the ML model can sequentially evaluate whether changing the valve openings stabilized the reservoir conditions. Future studies should validate and extend these findings from diverse perspectives.
Adaptive output feedback control based on almost strictly positive real (ASPR) properties of the system with an adaptive predictive control as a feedforward input has been proposed for linear continuous-time systems. The designed control system can guarantee stability and keep robust and higher control performance with input limitations even though the systems are uncertain. This paper proposes an adaptive predictive control strategy, which maintains the stability of the control system without imposing the input limitation. In the proposed method, we can obtain a stable control system for uncertain systems by estimating the unknown parameters. The stability of the proposed method is analyzed and its effectiveness is confirmed through numerical simulations.
A lot of research on path-planning technology for self-driving vehicles have focused on safety, time efficiency and the comfort of driving. The next needed step in the researches considering optimal behavior of vehicles is a control that takes into account the optimal behavior of the passenger. This paper considers a model predictive control method how to plan a path that restrains the passenger’s behavior. We evaluate ride comfort based on passenger’s motion perception characteristics.
In recent web conference systems, it has become possible to express the user’s gaze and attention through image processing. However, many users feel uncomfortable turning on the camera, and the sense of looked at and listened to worsens when the camera is turned off. Therefore, in this study, we investigated whether it is possible to improve the sense of gaze and listening by using thought bubbles even when the camera is off and there is no face information of the interlocutor. We have developed a video conferencing system that displays a pictogram image of a person with a thought bubble. The thought bubble displays the user’s face image. In addition, it is possible to extract the words that are the focal points of the user’s utterances and display them in a thought bubble. As a result, it was confirmed that the interlocutor’s sense of being looked at was enhanced by the presence of a thought bubble even when the interlocutor shows pictogram images of people. This result also suggests that thought bubbles can effectively show an interlocutor’s attention.
When using digital twins to create a simulation in the cyber space, their plant models must sequentially predict physical phenomena such as energy and water. However, increase in calculation costs renders their execution in real time challenging in case of a large scale, and they must be executed with high accuracy. Employing the mainstream technique of reducing the computational cost involves data-driven modeling, which suffers limited extrapolation accuracy as it does not have a physical structure. This study derived the simultaneous physical equations of the microgrid electric power network for three-phase voltage imbalance of AC electric power. Further, simulations were performed based on the equations. Using this power network model, we demonstrated that reduced-order modeling techniques with physical structure can offer better accuracy than models without a physical structure.
