Design and simulation of a fuel cell hybrid emergency power system for a more electric aircraft : evaluation of energy management schemes

As the aircraft industries are moving toward more electric aircraft (MEA), the electrical peak load seen by the main and emergency generators becomes higher than in conventional aircraft. Consequently, there is a major concern regarding the aircraft emergency system, which consists of a ram air turbine (RAT) or air driven generator (ADG), to fulfill the load demand during critical situations; particularly at low aircraft speed where the output power is very low. A potential solution under study by most aircraft manufacturers is to replace the air turbine by a fuel cell hybrid system, consisting of fuel cell combined with other high power density sources such as supercapacitors or lithium-ion batteries. To ensure the fuel cell hybrid system will be able to meet the load demand, it must be properly designed and an effective energy management strategy must be tested with real situations load profile. This work aims at designing a fuel cell emergency power system of a more electric aircraft and comparing different energy management schemes (EMS); with the goal to ensure the load demand is fully satisfied within the constraints of each energy source. The fuel cell hybrid system considered in this study consists of fuel cell, lithium-ion batteries and supercapacitors, along with associated DC-DC and DC-AC converters. The energy management schemes addressed are state-of-the-art, most commonly used energy management techniques in fuel cell vehicle applications and include: the state machine control strategy, the rule based fuzzy logic strategy, the classical PI control strategy, the frequency decoupling/fuzzy logic control strategy and the equivalent consumption minimization strategy (ECMS). Moreover, a new optimal scheme based on maximizing the instantaneous energy of batteries/supercapacitors, to improve the fuel economy is proposed. An off-line optimization based scheme is also developed to ascertain the validity of the proposed strategy in terms of fuel consumption. The energy management schemes are compared based on the following criteria: the hydrogen consumption, the state of charges of the batteries and supercapacitors and the overall system efficiency. Moreover the stress on each energy source, which impacts their life cycle, are measured using a new approach based on the wavelet transform of their instantaneous power. A simulation model and an experimental test bench are developed to validate all analysis and performances. The main results obtained are as follows: the state machine control scheme provided a slightly better efficiency and stresses on the batteries and supercapacitors. The classical PI control and the proposed scheme had the lowest fuel consumption and more use of the battery energy. As expected, the lowest fuel cell stress and lowest use of the battery energy was achieved with the frequency decoupling and fuzzy logic scheme, but at the expense of more fuel consumption and lower overall efficiency. The DC bus or supercapacitor voltage was maintained nearly constant for all the schemes. Also, the proposed strategy performed slightly better than the ECMS in terms of efficiency and fuel consumption, with an increase in fuel economy by 3 %. The energy management scheme suitable for a MEA emergency system should be a multischeme EMS such that each scheme is chosen based on a specific criterion to prioritize. As an example, depending on the operating life of each energy source, the energy management strategy can be chosen to either minimise the stress on the fuel cell system, the battery system or supercapacitor system, hence maximizing the life cycle of the hybrid power system. Also if the target is to reduce the fuel consumption, the proposed or the classical PI strategies are better alternatives

Improved Wind Turbine Control Strategies for Maximizing Power Output and Minimizing Power Flicker

For reducing the cost of energy (COE) for wind power, controls techniques are important for enhancing energy yield, reducing structural load and improving power quality. This thesis presents the control strategies studies for wind turbine both from the perspectives of both maximizing power output and reducing power flicker and structural load, First, a self-optimizing robust control scheme is developed with the objective of maximizing the power output of a variable speed wind turbine with doubly-fed induction generator (DFIG) operated in Region 2. Wind power generation can be divided into two stages: conversion from aerodynamic power to rotor (mechanical) power and conversion from rotor power to the electrical (grid) power. In this work, the maximization of power generation is achieved by a two-loop control structure in which the power control for each stage has intrinsic synergy. The outer loop is an Extremum Seeking Control (ESC) based generator torque regulation via the rotor power feedback. The ESC can search for the optimal torque constant to maximize the rotor power without wind measurement or accurate knowledge of power map. The inner loop is a vector-control based scheme that can both regulate the generator torque requested by the ESC and also maximize the conversion from the rotor power to grid power. In particular, an ∞ controller is synthesized for maximizing, with performance specifications defined based upon the spectrum of the rotor power obtained by the ESC. Also, the controller is designed to be robust against the variations of some generator parameters. The proposed control strategy is validated via simulation study based on the synergy of several software packages including the TurbSim and FAST developed by NREL, Simulink and SimPowerSystems. Then, a bumpless transfer scheme is proposed for inter-region controller switching scheme in order to reduce the power fluctuation and structural load under fluctuating wind conditions. This study considers the division of Region 2, Region 2.5 and Region 3 in the neighborhood of the rated wind speed. When wind, varies around the rated wind speed, the switching of control can lead to significant fluctuation in power and voltage supply, as well as structural loading. To smooth the switch and improve the tracking, two different bumpless transfer methods, Conditioning and Linear Quadratic techniques, are employed for different inter-region switching situations. The conditioning bumpless transfer approach adopted for switching between Region 2 maximum power capture controls to Region 2.5 rotor speed regulation via generator torque. For the switch between Region 2.5 and Region 3, the generator torque windup at rated value and pitch controller become online to limit the load of wind turbine. LQ technique is posed to reduce the discontinuity at the switch between torque controller and pitch controller by using an extra compensator. The flicker emission of the turbine during the switching is calculated to evaluate power fluctuation. The simulation results demonstrated the effectiveness of the proposed scheme of inter-region switching, with significant reduction of power flicker as well as the damage equivalent load

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Design of RF/IF analog to digital converters for software radio communication receivers

Software radio architecture can support multiple standards by performing analogto- digital (A/D) conversion of the radio frequency (RF) signals and running reconfigurable software programs on the backend digital signal processor (DSP). A slight variation of this architecture is the software defined radio architecture in which the A/D conversion is performed on intermediate frequency (IF) signals after a single down conversion. The first part of this research deals with the design and implementation of a fourth order continuous time bandpass sigma-delta (CT BP) C based on LC filters for direct RF digitization at 950 MHz with a clock frequency of 3.8 GHz. A new ADC architecture is proposed which uses only non-return to zero feedback digital to analog converter pulses to mitigate problems associated with clock jitter. The architecture also has full control over tuning of the coefficients of the noise transfer function for obtaining the best signal to noise ratio (SNR) performance. The operation of the architecture is examined in detail and extra design parameters are introduced to ensure robust operation of the ADC. Measurement results of the ADC, implemented in IBM 0.25 µm SiGe BiCMOS technology, show SNR of 63 dB and 59 dB in signal bandwidths of 200 kHz and 1 MHz, respectively, around 950 MHz while consuming 75 mW of power from ± 1.25 V supply. The second part of this research deals with the design of a fourth order CT BP ADC based on gm-C integrators with an automatic digital tuning scheme for IF digitization at 125 MHz and a clock frequency of 500 MHz. A linearized CMOS OTA architecture combines both cross coupling and source degeneration in order to obtain good IM3 performance. A system level digital tuning scheme is proposed to tune the ADC performance over process, voltage and temperature variations. The output bit stream of the ADC is captured using an external DSP, where a software tuning algorithm tunes the ADC parameters for best SNR performance. The IF ADC was designed in TSMC 0.35 µm CMOS technology and it consumes 152 mW of power from ± 1.65 V supply

Artificial intelligence applied to speed sensorless induction motor drives

During the last two decades there has been considerable development of sensorless vector controlled induction motor drives for high performance industrial applications. Such control strategies reduce the drive's cost, size and maintenance requirements while increasing the system's reliability and robustness. Parameter sensitivity, high computational effort and instability at low and zero speed can be the main shortcomings of sensorless control. Sensorless drives have been successfully applied for medium and high speed operation, but low and zero speed operation is still a critical problem. Much recent research effort is focused on extending the operating region of sensorless drives near zero stator frequency. Several strategies have been proposed for rotor speed estimation in sensorless induction motor drives based on the machine fundamental excitation model. Among these techniques Model Reference Adaptive Systems (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. Rotor flux-MRAS is the most popular MRAS strategy and significant attempts have been made to improve the performance of this scheme at low speed. Artificial Intelligence (AI) techniques have attracted much attention in the past few years as powerful tools to solve many control problems. Common AI strategies include neural networks, fuzzy logic and genetic algorithms. The mam purpose of this work is to show that AI can be used to improve the sensorless performance of the well-established MRAS observers in the critical low and zero speed region of operation. This thesis proposes various novel methods based on AI combined with MRAS observers. These methods have been implemented via simulation but also on an experimental drive based around a commercial induction machine. Detailed simulations and experimental tests are carried out to investigate the performance of the proposed schemes when compared to the conventional rotor fluxMRAS. Various schemes are implemented and tested in real time using a 7.5 kW induction machine and a dSP ACE DS 1103 controller board. The results presented for these new schemes show the great improvement in the performance of the MRAS observer in both open loop and sensorless modes of operation at low and zero speed.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, Arab Republic of EgyptGBUnited Kingdo

Adaptive monitoring and control framework in Application Service Management environment

The economics of data centres and cloud computing services have pushed hardware and software requirements to the limits, leaving only very small performance overhead before systems get into saturation. For Application Service Management–ASM, this carries the growing risk of impacting the execution times of various processes. In order to deliver a stable service at times of great demand for computational power, enterprise data centres and cloud providers must implement fast and robust control mechanisms that are capable of adapting to changing operating conditions while satisfying service–level agreements. In ASM practice, there are normally two methods for dealing with increased load, namely increasing computational power or releasing load. The first approach typically involves allocating additional machines, which must be available, waiting idle, to deal with high demand situations. The second approach is implemented by terminating incoming actions that are less important to new activity demand patterns, throttling, or rescheduling jobs. Although most modern cloud platforms, or operating systems, do not allow adaptive/automatic termination of processes, tasks or actions, it is administrators’ common practice to manually end, or stop, tasks or actions at any level of the system, such as at the level of a node, function, or process, or kill a long session that is executing on a database server. In this context, adaptive control of actions termination remains a significantly underutilised subject of Application Service Management and deserves further consideration. For example, this approach may be eminently suitable for systems with harsh execution time Service Level Agreements, such as real–time systems, or systems running under conditions of hard pressure on power supplies, systems running under variable priority, or constraints set up by the green computing paradigm. Along this line of work, the thesis investigates the potential of dimension relevance and metrics signals decomposition as methods that would enable more efficient action termination. These methods are integrated in adaptive control emulators and actuators powered by neural networks that are used to adjust the operation of the system to better conditions in environments with established goals seen from both system performance and economics perspectives. The behaviour of the proposed control framework is evaluated using complex load and service agreements scenarios of systems compatible with the requirements of on–premises, elastic compute cloud deployments, server–less computing, and micro–services architectures