Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresente...Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in theevaporator. To demonstrate its applicability, data with one-minute sampling resolution from ten evaporators ina supermarket in Otterup (Denmark) was used. The model parameters were estimated using a Kalman filter andthe maximum likelihood method. Since the dynamical properties of the cabinets constantly change as goodsare added and removed, the parameters were re-estimated for each night, over a period of approximately 2.5years. The model is validated through a statistical analysis of the residuals and the importance of the ongoingre-estimation of parameters is highlighted. Furthermore, the physical meaning of the estimated parameters isdiscussed and potential applications for characterization and classification of cabinets are demonstrated, byshowing how they can be differentiated as either open- or closed cabinets or rooms, using only the estimatedheat transfer coefficients and heat capacities. For a selected case it is shown that the estimated parametervalues are close to physics derived values, and that the accuracy measured by the standard errors of theestimates is approximately ±10% relative to the estimated values. The analysis demonstrates that the modelis robust, accurate and reliable in terms of estimating physically meaningful parameters and it is thereforeappropriate for large-scale implementation.展开更多
基金This document is the results of the research projects Digital twins for large-scale heat pumps and refrigeration systems(EUDP 64019-0570)Flexible Energy Denmark(FED)(IFD 8090-00069B).
文摘Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in theevaporator. To demonstrate its applicability, data with one-minute sampling resolution from ten evaporators ina supermarket in Otterup (Denmark) was used. The model parameters were estimated using a Kalman filter andthe maximum likelihood method. Since the dynamical properties of the cabinets constantly change as goodsare added and removed, the parameters were re-estimated for each night, over a period of approximately 2.5years. The model is validated through a statistical analysis of the residuals and the importance of the ongoingre-estimation of parameters is highlighted. Furthermore, the physical meaning of the estimated parameters isdiscussed and potential applications for characterization and classification of cabinets are demonstrated, byshowing how they can be differentiated as either open- or closed cabinets or rooms, using only the estimatedheat transfer coefficients and heat capacities. For a selected case it is shown that the estimated parametervalues are close to physics derived values, and that the accuracy measured by the standard errors of theestimates is approximately ±10% relative to the estimated values. The analysis demonstrates that the modelis robust, accurate and reliable in terms of estimating physically meaningful parameters and it is thereforeappropriate for large-scale implementation.
