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In this paper, first an analytical model of a synchronous generator with a pulsed constant power load (CPL) is developed and numerically compared with a detailed simulation model. The analytical model is shown to possess good predictive abilities, thus enabling its use for control purposes. Second, the generator has a proportionalintegral (PI) control inner-loop, whose task is to regulate the generator’s output voltage to a desired reference. A novel outer-loop predictive reference governor (PRG) is designed and tested via simulation. The PRG uses the analytical model to predict the output behavior of the generator over a short time window, and continuously modifies the reference given to the inner-loop in order to maintain stringent steady-state requirements, in spite of demanding power requirements at the CPL. Simulation results illustrate the significant performance advantages of using the PRG versus using the inner-loop PI controller alone.

Experimental Hardware-in-the-loop (xHIL) testing utilizing signal and/or power emulation imposes a hard real-time requirement on models of emulated subsystems, directly limiting their fidelity to what can be achieved in real-time on the available computational resources. Most real-time simulators are CPU-based, for which the overhead of an instruction-set architecture imposes a lower limit on the simulation step size, resulting in limited model bandwidth. For power-electronic systems with high-frequency switching, this limit often necessitates using average-value models, significantly reducing fidelity, in order to meet the real-time requirement. An alternative approach emerging recently is to use FPGAs as the computational platform, which, although offering orders-of-magnitudes faster execution due to their parallel architecture, they are more difficult to program and their limited fabric space bounds the size of models that can be simulated.

Detailed machine models are, and will continue to be, a critical component of both the design and validation processes for engineering future aircraft, which will undoubtedly continue to push the boundaries for the demand of electric power. This paper presents a survey of experimental testing procedures for typical synchronous machines that are applied to brushless synchronous machines with rotating rectifiers to characterize their operational impedances. The relevance and limitations of these procedures are discussed, which include steady-state drive stand tests, sudden short-circuit transient (SSC) tests, and standstill frequency response (SSFR) tests. Then, results captured in laboratory of the aforementioned tests are presented.

As technology for both military and civilian aviation systems mature into a new era, techniques to test and evaluate these systems have become of great interest. To achieve a general understanding as well as save time and cost, the use of computer modeling and simulation for component, subsystem or integrated system testing has become a central part of technology development programs. However, the evolving complexity of the systems being modeled leads to a tremendous increase in the complexity of the developed models. To gain confidence in these models there is a need to evaluate the risk in using those models for decision making. Statistical model validation techniques are used to assess the risk of using a given model in decision making exercises. In this paper, we formulate a transient model validation challenge problem for an air cycle machine (ACM) and present a hardware test bench used to generate experimental data relevant to the model.

Cost and performance requirements are driving military and commercial systems to become highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits of those complex controls and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop plant models and determine the uncertainty of those models to predict performance and stability of the control algorithms. A process of model and control algorithm validation for a dc-dc boost converter circuit based on acceptance sampling is presented here. The validation process described in this paper is based on MIL-STD 3022 with emphasis on requirements settings and the testing process. The key contribution of this paper is the process for model and control algorithm validation, specifically a method for decomposing the problem into model and control algorithm validation stages.

The cost and complexity of aircraft power systems limit the number of integrated system evaluations that can be performed in hardware. As a result, evaluations are often performed using emulators to mimic components or subsystems. As an example, aircraft generation systems are often tested using an emulator that consists of a bank of resistors that are switched to represent the power draw of one or more actuators. In this research, consideration is given to modern wide bandwidth emulators (WBEs) that use power electronics and digital controls to obtain wide bandwidth control of power, current, or voltage. Specifically, this paper first looks at how well a WBE can emulate the impedance of a load when coupled to a real-time model. Capturing the impedance of loads and sources is important for accurately assessing the small-signal stability of a system.

Cost and performance requirements are driving military and commercial systems to highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop models and determine the uncertainty of those models so as to predict performance and stability. A process of model validation for a boost circuit based on acceptance sampling is presented here. The validation process described in this paper includes the steps of defining requirements, performing a screening and exploration of the system, completing a system and parameter identification, and finally executing a validation test. To minimize the cost of experimentation and simulation, design of experiments is used extensively to limit the amount of data taken without losing information.

Preliminary investigations of nonlinear modeling of aircraft synchronous generators using neural networks are presented. Aircraft synchronous generators with high power density tend operate at current-levels proportional to the magnetic saturation region of the machine's material. The nonlinear model accounts for magnetic saturation of the generator, which causes the winding flux linkages and inductances to vary as a function of current. Finite element method software is used to perform a parametric sweep of direct, quadrature, and field currents to extract the respective flux linkages. This data is used to train a neural network which yields current as a function of flux linkage. The neural network is implemented in a Simulink synchronous generator model and simulation results are compared with a previously developed linear model. Results show that the nonlinear neural network model can more accurately describe the responsiveness and performance of the synchronous generator.

With the advent of modern parallel computing systems, larger and more accurate simulation models have been developed to simulate real-world hardware. These models require verification and validation (V&V), the latter using data acquired from representative hardware to ascertain the uncertainty of the model. An understanding of the errors introduced by the measurement system into the validation assessment allows for the model assessor to attribute errors to the measurement system as opposed to the model or experimental setup. Once the model(s) have been through the validation process, decision makers can better understand the risk associated with using these models. This paper describes one possible procedure to quantify the uncertainty of the data acquisition (DAQ) system.

Advancements in electrical, mechanical, and structural design onboard modern more electric aircraft have added significant stress to the electrical systems. An electrical system level analysis tool has been created in MATLAB/Simulink to facilitate rapid system analysis and optimization to meet the growing demands of modern aircraft. An integratated model of segment level models of an electrical system including a generator, electrical accumulator unit, electrical distribution unit and electromechanical actuators has been developed. Included in the model are mission level models of an engine and aircraft to provide relevant boundary conditions. It is anticipated that the tracking of the electrical distribution through numerical integration of these various subsystems will lead to more accurate predictions of the bus power quality. In this paper the tool is used to evaluate two architectures using two different load profiles.

Next generation aircraft will require more electrical power, more thermal cooling, and better versatility. To attain these improvements, technologies will need to be integrated and optimized at a system-level. The complexity of these integrated systems will require considerable analysis. In order to characterize and understand the implications of highly-integrated aircraft systems, the effects of pulsed-power, highly-transient loads, and the technologies that drive system-stability and behavior, an approach will be taken utilizing integrated modeling and simulation with hardware-in-the-loop (HIL). Such experiments can save time and cost and increase the general understanding of electrical and thermal phenomena as it pertains to aircraft systems before completing an integrated ground demonstration. As a first step toward completing an integrated analysis, a dynamometer “drive stand” was characterized to assess its performance.