Wind speed sensor calibration in thermal power plant using Bayesian inference

Mokhtari, Ali; Ghodrat, Maryam; Langroodi, Pooya Javadpoor; Shahrian, Azadeh

doi:10.1016/j.csite.2020.100621

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Using Bayesian inference to calibrate model parameters has been employed in fields from Structural Engineering [20], [21], [22] and Thermal Engineering [23], to Biology [24] and Finance [25]. In robotics, Bayesian inference has been used, for instance, to learn model parameters for the kinematic model of the arm of a robot [26], [27].…”

Section: A Related Workmentioning

confidence: 99%

An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

Unjhawala,

Hansen,

Zhang

et al. 2024

IEEE Access

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We present a Vehicle Model (VM) that has 17 degrees of freedom and includes nonlinear tire and powertrain subsystems. Implemented as a relatively small piece of C++ code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of 1 × 10 −3 s on a single core of a commodity CPU. When executed on the GPU, one can perform 300,000 vehicle simulations in real-time. These properties make the model a good candidate for reinforcement learning, model predictive control, model predictive path integral control, path planning, state estimation, and traffic simulation tasks. The model is expressive in that it can capture the dynamics of vastly different vehicles. This is demonstrated by first calibrating the model to mimic the dynamics of a 1/6 th scale vehicle called the Autonomy Research Testbed (ART) vehicle, which has a mass of approximately 5.8 kg. Subsequently, the model is calibrated to mimic the dynamics of a heavy-duty High Mobility Multipurpose Wheeled Vehicle (HMMWV), which has a mass of 2097 kg. The Bayesian calibration process discussed can (i) handle real-life measurement noise, and (ii) provide model parameter probability distributions. The vehicle model, which is open source and freely available in a public repository, can also be imported into Python owing to SWIG wrapping.

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Section: A Related Workmentioning

confidence: 99%

An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

Unjhawala,

Hansen,

Zhang

et al. 2024

IEEE Access

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“…In addition, BI has been applied to calibrate sensors in other building energy systems. Mokhtari et al [34] used Bayesian inference to calibrate the wind speed sensor in the cooling tower of a thermal power plant. The calibration model was constructed based on the equipment delivery instructions of the cooling tower.…”

Section: Summary Of Sensor Calibration Studies In Building Hvac Systemsmentioning

confidence: 99%

In-situ sensor calibration for building HVAC systems with limited information using general regression improved Bayesian inference

Xiong

Tang

et al. 2023

Building and Environment

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“…This method can realize online fault detection and calibration, effectively removing the adverse effects caused by sensor faults on the basis of uninterrupted system operation and effectively solving the problems existing in on-site calibration. Mokhtar et al [ 19 ] calibrated the wind speed sensor in a thermal power plant by the BI method. Yoon [ 20 ] proposed a virtual field sensor calibration method for building energy systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference

Gao

et al. 2022

Sensors

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Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

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Wind speed sensor calibration in thermal power plant using Bayesian inference

Cited by 10 publications

References 12 publications

An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

In-situ sensor calibration for building HVAC systems with limited information using general regression improved Bayesian inference

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference

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