Temperature Control Using an Air Handling Unit Installed with Carel pCO5+ Controller

This dissertation reports the project work developed in the Thesis/Dissertation course during the 2nd year of the Master of Electrical and Computer Engineering in the field of Automation and Systems, Department of Electrical Engineering (DEE) at Instituto Superior de Engenharia do Porto (ISEP). The installation of an Air Handling Unit (AHU) in a work place or a hospital plays an important role in the treatment and maintaining the purity of air. The temperature control is focused in this dissertation. The AHU maintains the temperature of the room or office at a set temperature. The heating and cooling function are done automatically by taking in the reference temperature of the room also depending on the outdoor climate. The main purpose of the AHU is to ensure comfort to the patients, staffs and the employees. In case of the hospitals, the main function of AHU is air cleanliness in hygiene applications. It also includes supplying a sufficient amount of oxygen and removing the carbon dioxide and maintaining a comfortable room climate. They help protect patients and staff from infections. This dissertation will focus on the study of wide range of technologies which will work on the AHU with the Carel electronic controller whose main function is to control the temperature of an office. The unit was installed at Farfetch, Barco, Portugal. The study includes the working of selection criteria of the supply and return fans, inverters, recovery unit, probes, dampers and the controller

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful enough to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge of data driven FDD is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralised chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data- driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data- driven FDD: the rooms' temperature, TTCx, supplied chilled water temperature, TCHWS, supplied chilled water flow rate, VCHWS, and supplied cooled water temperature, TCWS. Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6% - 3.4% differed fromthe original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques