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Real-time fault monitoring and control of the Vacuum Assisted Resin Transfer Moulding production process requires knowledge of the position of the epoxy flow-front inside the mould. Therefore, a fast and accurate flow-front tracking system is highly prized. Physics-informed grey-box models deliver a good trade-off between high fidelity and data-driven black-box models for designing such a flow-front tracking system. In this paper, we propose stochastic differential equations (SDEs) based grey-box model of the flow-front dynamics in the case of missing sensor information. The proposed method uses the finite difference approximation of the spatial domain of the flow-front for estimating the spatial flow pattern of the epoxy. To accommodate for the missing sensor data, we utilize a modified version of the continuous-discrete extended Kalman filter based estimation framework for SDEs that takes into consideration the effective dimension of the measurement space during the identification process. The performance of the method is evaluated for common fault scenarios.

Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate flexibility provided. The stochastic nature of consumers' price response is also considered in this study using chance constrained (CC) programming. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The energy flexibility associated with energy consumption must be exploited to accommodate more fluctuating renewable energy. The only solution that enables this without violating privacy concerns is penalty-based control, where penalty signals are designed to give incentives for the consumers to adjust their demand according to the needs of the grid. Designing the penalty signals is a challenging task due to different flexibility potential offered by various energy consuming systems. In this paper, it is shown that the best utilisation of energy flexibility requires individual penalty signals tuned towards the energy flexibility of each consumer. Here, we present a very simple yet novel approach for designing such individual penalty signals for each consumer, such that the value of the energy flexibility is increased, for both the grid operators and the consumers. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

With increasing fluctuations in electricity production due to prioritization of renewable energy sources, new applications that can adjust quickly to changes in demand/supply will be needed. Wastewater treatment use a significant amount of electricity to reduce nutrients in wastewater before discharge. The treatment process demands electricity in some selected periods which can be controlled, and hence the time of consumption is changeable. Here we suggest a novel predictive control strategy which enhances the flexibility in electricity demand by accounting for electricity price and probability of up or down regulation. The strategy is demonstrated in simulation experiments, where the concept is illustrated and the potential savings are estimated. Furthermore flexibility is investigated as a function of regulating prices, and it is shown that when difference between electricity price and regulating price increases, so does flexibility of the system.

With the continuously increasing size of the wind turbine blades, the complexity of the casting process and the risk of failures has also increased. The IntegralBlades® vacuum assisted resin transfer moulding (VATRM) production process at the Siemens Gamesa Renewable Energy facility in Aalborg, Denmark, does not permit the visual inspection of the process. Hence a sensor system (possibly virtual) for process control and monitoring is highly prized. Furthermore, the effect of material handling, variations in permeability of the casting media and the material (epoxy) properties affect the outcome of the casting process. Therefore, it is necessary to analyse the effect of such variations at an early stage of the design process (e.g. during the simulations) of such a sensor system. Therefore, in this paper, we first describe an effective method to simulate the random changes in the permeability and viscosity in high-dimensional partial differential equations based model of the fluid flow. Next, a low-dimensional grey-box (cyber-physical) spatiotemporal model is proposed to capture the effect of random change in permeability and viscosity during the progression of the flow-front. Finally, a numerical case-study is presented demonstrating the effectiveness of the proposed methodology.

With more emphasis on the use of green energy, the size of the turbines and blades in the wind turbines is continuously increasing. With increasing blade size, the casting process becomes more complicated and the risk of faults increases. Production of such blades, made of fibre reinforced polymer composites, without the possibility of visual inspection of the infusion process calls for a sensor system (possibly virtual) for monitoring the process. Therefore, in this paper, we propose a stochastic spatio-temporal modelling approach to model the flow-front dynamics inside the vacuum assisted resin transfer moulding process, knowledge of which is essential for determining the current state of the infusion process.

The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated by the UKF are used for covariance estimation by MLE and CM. Then we apply the two covariance estimation methods on an example application. In the example, we identify the covariance of the measurement noise for a continuous glucose monitoring (CGM) sensor. The sensor measures the subcutaneous glucose concentration for a type 1 diabetes patient. The root-mean square (RMS) error and the computation time are used to compare the performance of the two covariance estimation methods. The results indicate that as the prediction horizon expands, the RMS error for the MLE declines, while the error remains relatively large for the CM method. For larger prediction horizons, the MLE provides an estimate of the noise covariance that is less biased than the estimate by the CM method. The CM method is computationally less expensive though.

This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood with extended Kalman filter and maximum likelihood with unscented Kalman filter. We perform the identification on a 24-hour simulation of a stochastic differential equation (SDE) version of the Medtronic Virtual Patient (MVP) model including process and output noise. We compare the fits with the actual CGM signal, as well as the short- and long-term predictions for each identified model. The numerical results show that the maximum likelihood-based identification techniques offer the best performance in terms of fitting and prediction. Moreover, they have other advantages compared to ODE-based modeling, such as parameter tracking, population modeling and handling of outliers.

This paper discusses the significance of the noise model for the performance of a Model Predictive Controller when operating in closed-loop. The process model is parametrized as a continuous-time (CT) model and the relevant sampled-data filtering and control algorithms are developed. Using CT models typically means less parameters to identify. Systematic tuning of such controllers is discussed. Simulation studies are conducted for linear time-invariant systems showing that choosing a noise model of low order is beneficial for closed-loop performance.

In patients with type 1 diabetes, the effects of meals intake on blood glucose level are usually mitigated by administering a large amount of insulin (bolus) at mealtime or even slightly before. This strategy assumes, among other things, a prior knowledge of the meal size and the postprandial glucose dynamics. On the other hand, administering the meal bolus during or after mealtime could benefit from the information provided by the postprandial meal dynamics at the expense of a delayed meal bolus. The present paper investigates different bolus administration strategies (at mealtime, 15 minutes after or 30 minutes after the beginning of the meal). We implement a continuous-discrete unscented Kalman filter to estimate the states and insulin sensitivity. These estimates are used in a bolus calculator. The numerical results demonstrate that administering the meal bolus 15 minutes after mealtime both reduces the risk of hypoglycemia in case of an overestimated meal and the time spent in hyperglycemia if the meal size is underestimated. Faster insulin and the use of glucagon will have the potential to encourage postprandial meal bolus administration and hence will not require to accurately estimate the meal size.

In this paper we compare the performance of five different continuous time transfer function models used in closed-loop model predictive control (MPC). These models describe the glucose-insulin and glucose-glucagon dynamics. They are discretized into a state-space description and used as prediction models in the MPC algorithm. We simulate a scenario including meals and daily variations in the model parameters. The numerical results do not show significant changes in the glucose traces for any of the models, excepted for the first order model. From the present study, we can conclude that the second order model without delay should provide the best trade-off between sensitivity to uncertainties and practical usability for in vivo clinical studies.