327 research outputs found

    Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

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    Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

    Supervisory model predictive control of building integrated renewable and low carbon energy systems

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    To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

    Gas turbine transient performance simulation, control and optimisation

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    A gas turbine engine is a complex and non-linear system. Its dynamic response changes at different operating points. The exogenous inputs: atmospheric conditions and Mach number, also add disturbances and uncertainty to the dynamic. To satisfy the transient time response as well as safety requirements for its entire operating range is a challenge for control system design in the gas turbine industry. Although the recent design of engine control units includes some advanced control techniques to increase its control robustness and adaptability to the changing environment, the classic scheduling technique still plays the decisive role in determining the control values due to its better reliability under normal circumstances. Producing the schedules requires iterative experiments or simulations in all possible circumstances for obtaining the optimal engine performance. The techniques, such as scheduling method or linear control methods, are still lack of development for control of transient performance on most commercial simulation tools. Repetitive simulations are required to adjust the control values in order to obtain the optimal transient performance. In this project, a generalised model predictive controller was developed to achieve an online transient performance optimisation for the entire operating range. The optimal transient performance is produced by the controller according to the predictions of engine dynamics with consideration of constraints. The validation was conducted by the application of the control system on the simulated engines. The engines are modelled to component-level by the inter-component volume method. The results show that the model predictive controller introduced in this project is capable of providing the optimal transient time response as well as operating the engine within the safety margins under constant or varying environmental conditions. In addition, the dynamic performance can be improved by introducing additional constraints to engine parameters for the specification of smooth power transition as well as fuel economy

    In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion

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    [ES] La actual crisis climática ha instado a la comunidad investigadora y a los fabricantes a brindar soluciones para hacer que el sector del transporte sea más sostenible. De entre las diversas tecnologías propuestas, la combustión a baja temperatura ha sido objeto de una extensa investigación. La combustión premezclada dual-fuel es uno de los conceptos que abordan el compromiso de NOx-hollín en motores de encendido por compresión manteniendo alta eficiencia térmica. Esta combustión hace uso de dos combustibles con diferentes reactividades para mejorar la controlabilidad de este modo de combustión en un amplio rango de funcionamiento. De manera similar a todos los modos de combustión premezclados, esta combustión es sensible a las condiciones de operación y suele estar sujeta a variabilidad cíclica con gradientes de presión significativos. En consecuencia, se requieren estrategias de control avanzadas para garantizar un funcionamiento seguro y preciso del motor. El control en bucle cerrado es una herramienta eficaz para abordar los desafíos que plantea la combustión premezclada dual-fuel. En este tipo de control, para mantener el funcionamiento deseado, las acciones de control se adaptan y corrigen a partir de una retroalimentación con las señales de salida del motor. Esta tesis presenta estrategias de control basadas en la medición de la señal de presión en el cilindro, aplicadas a motores de combustión premezclada dual-fuel. En ella se resuelven diversos aspectos del funcionamiento del motor mediante el diseño de controladores dedicados, haciéndose especial énfasis en analizar e implementar estas soluciones a los diferentes niveles de estratificación de mezcla considerados en estos motores (es decir, totalmente, altamente y parcialmente premezclada). Inicialmente, se diseñan estrategias de control basadas en el procesamiento de la señal de presión en el cilindro y se seleccionan acciones proporcionales-integrales para asegurar el rendimiento deseado del motor sin exceder las limitaciones mecánicas del motor. También se evalúa la técnica extremum seeking para realizar una supervisión de una combustión eficiente y la reducción de emisiones de NOx. Luego se analiza la resonancia de la presión en el cilindro y se implementa un controlador similar a aquel usado para el control de knock para garantizar el funcionamiento seguro del motor. Finalmente, se utilizan modelos matemáticos para diseñar un modelo orientado a control y un observador que tiene como objetivo combinar las señales medidas en el motor para mejorar las capacidades de predicción y diagnóstico en dicha configuración de motor. Los resultados de este trabajo destacan la importancia de considerar el control en bucle cerrado para abordar las limitaciones encontradas en los modos de combustión premezclada. En particular, el uso de la medición de presión en el cilindro muestra la relevancia y el potencial de esta señal para desarrollar estrategias de control complejas y precisas.[CA] L'actual crisi climàtica ha instat a la comunitat investigadora i als fabricants a brindar solucions per a fer que el sector del transport siga més sostenible. D'entre les diverses tecnologies proposades, la combustió a baixa temperatura ha sigut objecte d'una extensa investigació. La combustió premesclada dual-fuel és un dels conceptes que aborden el compromís de NOx-sutge en motors d'encesa per compressió mantenint alta eficiència tèrmica. Aquesta combustió fa ús de dos combustibles amb diferents reactivitats per a millorar la controlabilitat d'aquest tipus de combustió en un ampli rang de funcionament. De manera similar a tots els tipus de combustió premesclada, aquesta combustió és sensible a les condicions d'operació i sol estar subjecta a variabilitat cíclica amb gradients de pressió significatius. En conseqüència, es requereixen estratègies de control avançades per a garantir un funcionament segur i precís del motor. El control en bucle tancat és una eina eficaç per a abordar els desafiaments que planteja la combustió premesclada dual-fuel. En aquesta mena de control, per a mantindre el funcionament desitjat, les accions de control s'adapten i corregeixen a partir d'una retroalimentació amb els senyals d'eixida del motor. Aquesta tesi presenta estratègies de control basades en el mesurament del senyal de pressió en el cilindre, aplicades a motors de combustió premesclada dual-fuel. En ella es resolen diversos aspectes del funcionament del motor mitjançant el disseny de controladors dedicats, fent-se especial èmfasi a analitzar i implementar aquestes solucions als diferents nivells d'estratificació de mescla considerats en aquests motors (és a dir, totalment, altament i parcialment premesclada). Inicialment, es dissenyen estratègies de control basades en el processament del senyal de pressió en el cilindre i se seleccionen accions proporcionals-integrals per a assegurar el rendiment desitjat del motor sense excedir les limitacions mecàniques del motor. També s'avalua la tècnica extremum seeking per a realitzar una supervisió d'una combustió eficient i la reducció d'emissions de NOx. Després s'analitza la ressonància de la pressió en el cilindre i s'implementa un controlador similar a aquell usat per al control de knock per a garantir el funcionament segur del motor. Finalment, s'utilitzen models matemàtics per a dissenyar un model orientat a control i un observador que té com a objectiu combinar els senyals mesurats en el motor per a millorar les capacitats de predicció i diagnòstic en aquesta configuració de motor. Els resultats d'aquest treball destaquen la importància de considerar el control en bucle tancat per a abordar les limitacions trobades en la combustió premesclada. En particular, l'ús del mesurament de pressió en el cilindre mostra la rellevància i el potencial d'aquest senyal per a desenvolupar estratègies de control complexes i precises.[EN] The current climate crisis has urged the research community and manufacturers to provide solutions to make the transportation sector cleaner. Among the various technologies proposed, low temperature combustion has undergone extensive investigation. Premixed dual-fuel combustion is one of the concepts addressing the NOx-soot trade-off in compression ignited engines, while maintaining high thermal efficiency. This combustion makes use of two fuels with different reactivities in order to improve the controllability of this combustion mode over a wide range of operation. Similarly to all premixed combustion modes, this combustion is nevertheless sensitive to the operating conditions and traditionally exhibits cycle-to-cycle variability with significant pressure gradients. Consequently, advanced control strategies to ensure a safe and accurate operation of the engine are required. Feedback control is a powerful approach to address the challenges raised by the premixed dual-fuel combustion. By measuring the output signals from the engine, strategies can be developed to adapt and correct the control actions to maintain the desired operation. This thesis presents control strategies, based on the in-cylinder pressure signal measurement, applied to premixed dual-fuel combustion engines. Various objectives were addressed by designing dedicated controllers, where a special emphasis was made towards analyzing and implementing these solutions to the different levels of mixture stratification considered in these engines (i.e., fully, highly and partially premixed). At first, feedback control strategies based on the in-cylinder pressure signal processing were designed. Proportional-integral actions were selected to ensure the desired engine performance without exceeding the mechanical constraints of the engine. Extremum seeking was evaluated to track efficient combustion phasing and NOx emissions reduction. The in-cylinder pressure resonance was then analyzed and a knock-like controller was implemented to ensure safe operation of the engine. Finally, mathematical models were used to design a control-oriented model and a state observer that aimed to leverage the signals measured in the engine to improve the prediction and diagnostic capabilities in such engine configuration. The results from this work highlighted the importance of considering feedback control to address the limitations encountered in premixed combustion modes. Particularly, the use of the in-cylinder pressure measurement showed the relevance and potential of this signal to develop complex and accurate control strategies.This thesis was financially supported by the Programa Operativo del Fondo Social Europeo (FSE) de la Comunitat Valenciana 2014-2020 through grant ACIF/2018/141.Barbier, ARS. (2022). In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18327

    Optimal generation scheduling for renewable microgrids using hydrogen storage systems

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    The topic of this thesis is the development of a tool for an optimal energy management strategy (EMS) of the generators and energy storage systems constituent microgrids, both grid-connected or isolated (stand-alone power system) powered by Renewable Energy Sources (RES). In particular, a novel control system is designed based on the resolution of the unit commitment problem. For each time step, the proposed control system compares the expected power produced by the renewable generators with the expected load demand and determines the scheduling of the different energy storage devices and generators for the next few hours, which minimizes the operating cost of the overall microgrid. To take into account for forecasting uncertainties, the generation of the different scenarios is carried out through a discretization of the probability distribution function of the forecasting errors for wind speed, solar radiation and load requests by a set of finite states. A set of various scenarios are therefore analyzed and compared by the control system to find the minimum operating costs. The proposed algorithm is firstly applied to a microgrid at LABH2FER (Sardegna Ricerche, Italy). Since the microgrid is under construction, the expected performance is evaluated through a simulation modeling, implemented in Matlab-Simulink. Furthermore, in order to highlight the benefits of including weather forecasts and operating costs in the EMS, a comparative analysis with a simpler EMS based on control states of storage devices is carried out. The results of the comparative study demonstrate that a reduction of almost 5-10% in the annual operating costs and energy losses is achieved thanks to the implementation of the proposed control system. Moreover, the proposed control strategy is implemented and tested to a microgrid present at the University of Seville. Experimental results demonstrate the feasibility and the actual functionality of the control system. Additional benefits are also observed, such as the reduction in power exchanged with the upstream grid thanks to a better management of the storage systems

    Voltage Rise Problem in Distribution Networks with Distributed Generation: A Review of Technologies, Impact and Mitigation Approaches

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    Energy demand has constantly been on the rise due to aggressive industrialization and civilization. This rise in energy demand results in the massive penetration of distributed generation (DG) in the distribution network (DN) which has been a holistic approach to enhance the capacity of distribution networks. However, this has led to a number of issues in the low voltage network, one of which is the voltage rise problem. This happens when generation exceeds demand thereby causing reverse power flow and consequently leading to overvoltage. A number of methods have been discussed in the literature to overcome this challenge ranging from network augmentation to active management of the distribution networks. This paper discusses the issue of voltage rise problem and its impact on distribution networks with high amounts of distributed energy resources (DERs). It presents different DG technologies such as those based on conventional and unconventional resources and other DERs such as battery storage systems and fuel cells. The study provides a comprehensive overview of approaches employed to curtail the issue of voltage increase at the point of common coupling (PCC), which includes strategies based on the network reinforcement methodology and the active distribution network management. A techno-economic comparison is then introduced in the paper to ascertain the similarities and dissimilarities of different mitigation approaches based on the technology involved, ease of deployment, cost implication, and their pros and cons. The paper provides insights into directions for future research in mitigating the impact of voltage rise presented by grid-connected DGs without limiting their increased penetration in the existing power grid

    Compressed Air Energy Storage in Offshore Grids

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    Advanced Neural Network Based Control for Automotive Engines

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    This thesis investigates the application of artificial neural networks (NN) in air/fuel ratio (AFR) control of spark ignition(SI) engines. Three advanced neural network based control schemes are proposed: radial basis function(RBF) neural network based feedforward-feedback control scheme, RBF based model predictive control scheme, and diagonal recurrent neural network (DRNN) - based model predictive control scheme. The major objective of these control schemes is to maintain the air/fuel ratio at the stoichiometric value of 14.7 , under varying disturbance and system uncertainty. All the developed methods have been assessed using an engine simulation model built based on a widely used engine model benchmark, mean value engine model (MVEM). Satisfactory control performance in terms of effective regulation and robustness to disturbance and system component change have been achieved. In the feedforward-feedback control scheme, a neural network model is used to predict air mass flow from system measurements. Then, the injected fuel is estimated by an inverse NN controller. The simulation results have shown that much improved control performance has been achieved compared with conventional PID control in both transient and steady-state response. A nonlinear model predictive control is developed for AFR control in this re- . search using RBF model. A one-dimensional optimization method, the secant method is employed to obtain optimal control variable in the MPC scheme, so that the computation load and consequently the computation time is greatly reduced. This feature significantly enhances the applicability of the MPC to industrial systems with fast dynamics. Moreover, the RBF model is on-line adapted to model engine time-varying dynamics and parameter uncertainty. As such, the developed control scheme is more robust and this is approved in the evaluation. The MPC strategy is further developed with the RBF model replaced by a DRNN model. The DRNN has structure including a information-storing neurons and is therefore more appropriate for dynamics system modelling than the RBF, a static network. In this research, the dynamic back-propagation algorithm (DBP) is adopted to train the DRNN and is realized by automatic differentiation (AD) technique. This greatly reduces the computation load and time in the model training. The MPC using the DRNN model is found in the simulation evaluation having better control performance than the RBF -based model predictive control. The main contribution of this research lies in the following aspects. A neural network based feedforward-feedback control scheme is developed for AFR of SI engines, which is performed better than traditional look-up table with PI control method. This new method needs moderate computation and therefore has strong potential to be applied in production engines in automotive industry. Furthermore, two adaptive neural network models, a RBF model and a DRNN model, are developed for engine and incorporated into the MPC scheme. Such developed two MPC schemes are proved by simulations having advanced features of low computation load, better regulation performance in both transient and steady state, and stronger robustness to engine time-varying dynamics and parameter uncertainty. Finally, the developed schemes are considered to suit the limited hardware capacity of engine control and have feasibility and strong potential to be practically implemented in the production engines

    TOWARDS OPTIMAL OPERATION AND CONTROL OF EMERGING ELECTRIC DISTRIBUTION NETWORKS

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    The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics

    Optimization and control of a dual-loop EGR system in a modern diesel engine

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    Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine
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