3 research outputs found
Control-oriented modeling of a LiBr/H2O absorption heat pumping device and experimental validation
Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and
chillers) are devices that use thermal energy instead of electricity to
generate heating and cooling, thereby facilitating the use of waste heat and
renewable energy sources such as solar or geothermal energy. Despite this
benefit, widespread use of AHPDs is still limited. One reason for this is
partly unsatisfactory control performance under varying operating conditions,
which can result in poor modulation and part load capability. A promising
approach to tackle this issue is using dynamic, model-based control strategies,
whose effectiveness, however, strongly depend on the model being used. This
paper therefore focuses on the derivation of a viable dynamic model to be used
for such model-based control strategies for AHPDs such as state feedback or
model-predictive control. The derived model is experimentally validated,
showing good modeling accuracy. Its modeling accuracy is also compared to
alternative model versions, that contain other heat transfer correlations, as a
benchmark. Although the derived model is mathematically simple, it does have
the structure of a nonlinear differential-algebraic system of equations. To
obtain an even simpler model structure, linearization at an operating point is
discussed to derive a model in linear state space representation. The
experimental validation shows that the linear model does have slightly worse
steady-state accuracy, but that the dynamic accuracy seems to be almost
unaffected by the linearization. The presented new modeling approach is
considered suitable to be used as a basis for the design of advanced,
model-based control strategies, ultimately aiming to improve the modulation and
part load capability of AHPDs
Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence
Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%