Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results

Control of Proton Exchange Membrane Fuel Cell System

265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system

Fuel Cell Renewable Hybrid Power Systems

Climate change is becoming visible today, and so this book—through including innovative solutions and experimental research as well as state-of-the-art studies in challenging areas related to sustainable energy development based on hybrid energy systems that combine renewable energy systems with fuel cells—represents a useful resource for researchers in these fields. In this context, hydrogen fuel cell technology is one of the alternative solutions for the development of future clean energy systems. As this book presents the latest solutions, readers working in research areas related to the above are invited to read it

Multivariable robust control of a simulated hybrid solid oxide fuel cell gas turbine plant

This work presents a systematic approach to the multivariable robust control of a hybrid fuel cell gas turbine plant. The hybrid configuration under investigation built by the National Energy Technology Laboratory comprises a physical simulation of a 300kW fuel cell coupled to a 120kW auxiliary power unit single spool gas turbine. The public facility provides for the testing and simulation of different fuel cell models that in turn help identify the key difficulties encountered in the transient operation of such systems. An empirical model of the built facility comprising a simulated fuel cell cathode volume and balance of plant components is derived via frequency response data. Through the modulation of various airflow bypass valves within the hybrid configuration, Bode plots are used to derive key input/output interactions in transfer function format. A multivariate system is then built from individual transfer functions, creating a matrix that serves as the nominal plant in an Hinfinity robust control algorithm. The controller\u27s main objective is to track and maintain hybrid operational constraints in the fuel cell\u27s cathode airflow, and the turbo machinery states of temperature and speed, under transient disturbances. This algorithm is then tested on a Simulink/MatLab platform for various perturbations of load and fuel cell heat effluence.;As a complementary tool to the aforementioned empirical plant, a nonlinear analytical model faithful to the existing process and instrumentation arrangement is evaluated and designed in the Simulink environment. This parallel task intends to serve as a building block to scalable hybrid configurations that might require a more detailed nonlinear representation for a wide variety of controller schemes and hardware implementations

Study of closed-cycle gas turbine for application to small modular reactors (SMRs) and coal-fired power generation through modelling and simulation

Closed-cycle GT has the potential for improved efficiency of electricity generation, compact and simple design, and reduced CO2 emissions and therefore could complement conventional power conversion systems (PCSs). However, power generation from closed-cycle GT needs to be demonstrated to establish the integrity, operation and performance of the plant before commercial deployment can be realised. This thesis provides an understanding, through modelling and simulation, of the thermodynamic performance and component design parameters, and the dynamic behaviours, operation and control of closed-cycle GTs for the purpose of assessing their feasibility for near-term demonstration. A systematic, full-scope study was performed for nitrogen closed-cycle GT coupled to small modular sodium-cooled fast reactor (SM-SFR) and supercritical carbon dioxide (s-CO2) closed-cycle GT coupled to small modular pressurised water reactor (SM-PWR). The study included selection between alternative plant designs, steady state performance analysis, preliminary design of components, dynamic model development and simulation of plant transients, and design of control systems. Additionally, performance evaluation was performed for s-CO2 closed-cycle GT for application to coal-fired power generation integrated with solvent based PCC. Intercooled closed-cycle GT using nitrogen as working fluid and with a single shaft configuration has been one common PCS option for possible near-term demonstration of SFR. In this work, a new nitrogen cycle configuration was proposed to further simplify the design of the turbomachinery and reduce turbomachinery size without compromising the cycle efficiency. Mathematical models in Matlab were developed for steady state thermodynamic analysis of the cycles and for preliminary design of the heat exchangers, turbines and compressors. The study indicated that the new configuration has the potential to simplify the design of turbomachinery, reduce the size of turbomachinery and provide opportunity for improving the efficiency of the turbomachinery. Dynamic model of the new nitrogen cycle power plant was developed in Matlab/Simulink. Control schemes, which enables the plant to satisfy the operational requirements under load-following and loss-of-load conditions, were implemented. Inventory control is unable to keep the generator speed within the specified ±30 rpm of the synchronous speed during normal load-following operation. However, bypass valve control is able to maintain the generator speed within ±17 rpm of the synchronous speed. Maximum generator shaft overspeed is below 105% during sudden loss-of-load condition, which is below the 120% maximum limit. Hence, stable and controllable operation of the nitrogen GT power plant is possible. Matlab models were developed for thermodynamic performance analysis and preliminary design of components for s-CO2 closed-cycle GTs coupled to SM-PWR. Recompression s-CO2 layout is the most common configuration for s-CO2 cycle power plant. However, the performance assessment of the recompression s-CO2 cycle for application to PWR shows that temperature of the turbine exhaust is too low to allow any meaningful recuperation in the high temperature recuperator. Hence, a new layout is suggested. The efficiency of the new layout is comparable to that of the recompression cycle and higher than that of the simple recuperated cycle layout. Investigation of the impact of heat exchanger design on plant performance showed that the recompression cycles have higher pressure losses than the simple recuperated cycle. Therefore, if the heat exchanger design and pressure loss is considered in performance evaluation, the recompression cycles might not be that superior to the simple cycle. However, parametric analysis indicated that the new layout is the most promising for application to PWR. Dynamic modelling, simulation and control system design was also carried out for the new s-CO2 layout coupled to SM-PWR. Inventory/pressure control is not considered to avoid issues associated with the rapid variation of CO2 properties around the critical point. To effectively control the plant, flow split control and throttle valve were added to the normal control systems (bypass valve, control rod, coolant pump and cooling water control). The change in shaft speed during load-following operation is about ±27 rpm while shaft overspeed during loss-of-load is about 107% of the synchronous speed. These are all within the allowable shaft speed limit. Aspen Plus simulation was performed to evaluate the thermodynamic performance of cascaded s-CO2 cycles coupled to coal-fired furnace and integrated with 90% post-combustion CO2 capture. Three bottoming s-CO2 cycles were investigated: simple recuperated cycle, partial heating cycle and the newly proposed s-CO2 cycle. Results for a 290 bar and 593 0C power cycle without CO2 capture showed that the configuration with the new cycle as bottoming cycle has the highest plant net efficiency of 42.96% (HHV), followed by the simple recuperated, 42.46% and the partial heating, 42.44%. Integration of CO2 capture reduced the efficiencies of the new cycle, the simple recuperated and the partial heating configurations to 31.76%, 31.22% and 31.13% respectively. Without CO2 capture, the efficiencies of the coal-fired supercritical CO2 cycle plants were about 3.34-3.86% point higher than the reference steam cycle plant and about 0.68-1.31% point higher with CO2 capture. The findings so far underscored the promising potential of cascaded s-CO2 power cycles for coal-fired power plant application

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Modelling, simulation and performance evaluation: PEM fuel cells for high altitude UAS

A fuel cell is a device that converts energy in the fuel and reactant into electrical DC power. Fuel cell powered aircraft are generally characterised by a low power to weight ratio (W/kg). The propulsion system of an unmanned aircraft needs a large range of power and fast response to fulfil the requirements of different flight phases and to balance the variations in the load demand. A proton exchange membrane (PEM) fuel cell is considered as a potential power source for high altitude UAS (unmanned aircraft systems) operations. At altitudes in excess of 10 km, very low atmospheric temperatures and pressures, and unexpected variations in the load demand put severe stresses on the operation and performance of PEM fuel cells. A stable and robust controller and fuel supply system that can provide fast and sufficient flow of hydrogen and air/oxygen to the reaction of the fuel cell is one of the critical objectives. In this research, a simplified mathematical model of the PEM fuel cell stack system is developed and validated with the commercially available 1 kW PEM fuel cell stack (H-1000) developed by Horizon Fuel Cell Technologies. Matlab-Simulink is used to implement the necessary design and simulations under various operational conditions. The implications of high altitudes on the operation and performance of a PEM fuel cell stack are investigated, and a PID controller is adopted to efficiently optimise and provide a sufficient flow of hydrogen and air/oxygen to the stack, in particular maintaining the flow rates of the reactants was deemed most critical at high altitudes operation. Also, in order to store the required oxygen and hydrogen, the design of storage vessels is considered. This research presents a design of a PEM fuel cell power system for unmanned aircraft systems with an integrated approach that enables estimation of required power for high altitudes UAS operation which is then used to determine the size and weight of the combined power-plant of fuel cell stack with hydrogen and air/oxygen vessels and the propulsion system of the UAS. This approach takes into the consideration the power capacity of fuel cell stack and the flight endurance as two main factors in designing the size and weight of storage vessels, and hence determining the overall weight of the UAS, which is a key requirement in the preliminary aircraft design phase. One of the research outcomes shows a potential in extending the flying duration and altitude for more than five hours and a half, reaching up to 11 km altitude, for a UAS with an overall weight of 32 kg, including a payload capacity of 2 kg, based on a 1 kW PEM fuel cell propulsion system

On the development of power drive trains for hydrogen fuel cell electric vehicles

PhD ThesisThe world faces a major problem. Fossil fuel sources are finite and the economic and environmental cost of those that actually remain make finding an alternative one of the great technological challenges of our age. Nearly 70% of refined oil is used for transportation making it one of the key sectors where change could yield large-scale global benefits. Combustion engine passenger vehicle technology is after a long period of stagnation progressing at a pace. Hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs) are also starting to penetrate the mass market. Unfortunately, HEVs do not remove our dependency on oil and the prospects of battery technology advancing sufficiently to allow BEVs to progressively replace the entire oil fuelled vehicles are currently slim. Their limited range and long recharge times prohibit them being useful for most modes of driving. One solution to the problem may be hydrogen fuel cell electric vehicles (H2FCEVs) as they offer great promise, but realistically face many challenges. The fuel cell allowed man to voyage to the moon in the 1960s and recent material advances have enabled them to be packaged into motor vehicles, so providing a zero emission replacement for the internal combustion engine. However, substantial infrastructure and geopolitical changes are required to make hydrogen production and delivery economic but this gas potentially offers a clean and sustainable energy pathway to entirely replace fossil fuels in motor vehicles. Few reported studies have comprehensively examined the optimal method of building power drive train subsystems and integrating them into an architecture that delivers energy from a fuel cell into driven road wheels. This project investigated the optimisation on the most efficient drive train topology using critical analysis and computer modeling to determine a practical system. No single drivetrain was found suitable for all driving modes and worldwide markets as the current ones typically offered either optimal performance or optimal efficiency. Consequently, a new drivetrain topology was proposed, developed, tested with a simulation environment that yielded efficiency and performance gains over existing systems. Also analysed was the effect of wider vehicle design optimisation to the development of sustainable hydrogen powered passenger vehicles and this was set against the wider social, scientific and engineering challenges that fuel cell adoption will face

Development of controllers using FPGA for fuel cells in standalone and utility applications

In the recent years, increase in consumption of energy, instability of crude oil price and global climate change has forced researchers to focus more on renewable energy sources.Though there are different renewable energy sources available (such as photovoltaic and wind energy), they have some major limitations. The potential techniques which can provide renewable energy are fuel cell technology which is better than other renewable sources of energy. Solid oxide fuel cell (SOFC) is more efficient, environmental friendly renewable energy source. This dissertation focuses on load/grid connected fuel cell power system (FCPS) which can be used as a backup power source for household and commercial units. This backup power source will be efficient and will provide energy at an affordable per unit cost. Load/grid connected fuel cell power system mainly comprises of a fuel cell module, DCDC converter and DC-AC inverter. This thesis primarily focuses on solid oxide fuel cell (SOFC) modelling, digital control of DC-DC converter and DC-AC inverter. Extensive simulation results are validated by experimental results. Dynamic mathematical model of SOFC is developed to find out output voltage, efficiency, over potential loss and power density of fuel cell stack. The output voltage of fuel cell is fed to a DC-DC converter to step up the output voltage. Conventional Proportional-Integral (PI) controller and FPGA based PI controller is implemented and experimentally validated. The output voltage of DC-DC converter is fed to DC-AC inverter. Different pulse width modulation-voltage source inverter (PWM-VSI) control strategy (such as Hysteresis Current Controller (HCC), Adaptive-HCC, Fuzzy-HCC, Adaptive Fuzzy-HCC, Triangular Carrier Current Controller (TCCC) and Triangular Periodical Current Controller (TPCC)) for DC-AC inverter are investigated and validated through extensive simulations using MATLAB/SIMULINK. This work also focuses on number of fuel cells required for application in real time and remedy strategies when one or few fuel cells are malfunctioning. When the required numbers of fuel cells are not available, DC-DC converter is used to step up the output voltage of fuel cell. When there is a malfunction in fuel cell or shortage of hydrogen then a battery is used to provide backup power