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Preventive maintenance instructions for bonding and electrical grounding systems to prevent equipment damage by electrical faults, lightning, and radiation - Vol. 3
Analyzed, developed, and tested were high power microwave components for communications satellites systems. Included were waveguide and flange configurations with venting, a harmonic filter, forward and reverse power monitors, electrical fault sensors, and a diplexer for two channel simultaneous transmission. The assembly of 8.36 GHz components was bench tested, and then operated for 60 hours at 3.5 kW CW in a high vacuum. The diplexer was omitted from this test pending a modification of its end irises. An RF leakage test showed only that care is required at flange junctions; all other components were RF tight. Designs were extrapolated for 12 GHz and 2.64 GHz high power satellite systems.
This paper discusses an approach to the modeling and performance for the preliminary design phase of a large (6.2 MW) horizontal axis wind turbine generator (WTG). Two control philosophies are presented, both of which are based on linearized models of the WT mechanical and electrical systems. The control designs are compared by showing the performance through detailed non-linear time simulation. The disturbances considered are wind gusts, and electrical faults near the WT terminals.
The development of a floating frame grounding system (FFGS) for the 40- by 80-foot low speed wind tunnel facility at the NASA Ames Research Center National Full Scale Aerodynamics Complex is addresssed. When electrical faults are detected, the FFGS ensures a ground path for the fault current. In addition, the FFGS alerts the tunnel operator when a mechanical foul occurs.
NASA plans to orbit a permanently manned space station in the late 1990s, which requires development and assembly of a photovoltaic (PV) power source system to supply up to 75 kW of electrical power average during the orbital period. The electrical power requirements are to be met by a combination of PV source, storage, and control elements for the sun and eclipse periods. The authors discuss the application of power electronics and controls to manage the generation, storage, and distribution of power to meet the station loads, as well as the computer models used for analysis and simulation of the PV power system. The requirements for power source integrated controls to adjust storage charge power during the insolation period current limiting, breaker interrupt current values, and the electrical fault protection approach are defined. Based on these requirements, operating concepts have been defined which then become drivers for specific system and element design.
When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system.
The Electrical Systems Development Branch of the Power Technology Division at the NASA Lewis Research Center in Cleveland, Ohio is designing a Power Management and Distribution (PMAD) System for the Air Force's Integrated Solar Upper Stage (ISUS) Engine Ground Test Demonstration (EGD). The ISUS program uses solar-thermal propulsion to perform orbit transfers from Low Earth Orbit (LEO) to Geosynchronous Orbit (GEO) and from LEO to Molnya. The ISUS uses the same energy conversion receiver to perform the LEO to High Earth Orbit (HEO) transfer and to generate on-orbit electric power for the payloads. On-orbit power generation is accomplished via two solar concentrators heating a dual-cavity graphite-core which has Thermionic Diodes (TMD's) encircling each cavity. The graphite core and concentrators together are called the Receiver and Concentrator (RAC). The TDM-emitters reach peak temperatures of approximately 2200K, and the TID-collectors are run at approximately 1000K. Because of the high Specific Impulse (I(sup sp)) of solar thermal propulsion relative to chemical propulsion, and because a common bus is used for communications, GN&C, power, etc., a substantial increase in payload weight is possible. This potentially allows for a stepdown in the required launch vehicle size or class for similar payload weight using conventional chemical propulsion and a separate spacecraft bus. The ISUS power system is to provide 1000W(sub e) at 28+/-6V(sub dc) to the payload/spacecraft from a maximum TID generation capability of 1070W(sub e) at 2200K. Producing power with this quality, protecting the spacecraft from electrical faults and accommodating operational constraints of the TID's are the responsibilities of the PMAD system. The design strategy and system options examined along with the proposed designs for the Flight and EGD configurations are discussed herein.
Airlines are extremely sensitive to the amount of dollars spent on maintaining the external engine hardware in the field. Analysis reveals that many problems revolve around a central issue, reliability. Fuel and oil leakage due to seal failure and electrical fault messages due to wire harness failures play a major role in aircraft delays and cancellations (D&C's) and scheduled maintenance. Correcting these items on the line requires a large investment of engineering resources and manpower after the fact. The smartest and most cost effective philosophy is to build the best hardware the first time. The only way to do that is to completely understand and model the operating environment, study the field experience of similar designs and to perform extensive testing.
A system based on low-power radio transponders and associated analog and digital electronic circuitry has been developed for locating firefighters and other emergency workers deployed in a building or other structure. The system has obvious potential for saving lives and reducing the risk of injuries. The system includes (1) a central station equipped with a computer and a transceiver; (2) active radio-frequency (RF) identification tags, each placed in a different room or region of the structure; and (3) transponder units worn by the emergency workers. The RF identification tags can be installed in a new building as built-in components of standard fire-detection devices or ground-fault electrical outlets or can be attached to such devices in a previously constructed building, without need for rewiring the building. Each RF identification tag contains information that uniquely identifies it. When each tag is installed, information on its location and identity are reported to, and stored at, the central station. In an emergency, if a building has not been prewired with RF identification tags, leading emergency workers could drop sequentially numbered portable tags in the rooms of the building, reporting the tag numbers and locations by radio to the central station as they proceed.
The report details test and measurement flights to demonstrate autonomous UAV inspection of high voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensor logging software, measured ultraviolet emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace.
This report details test and measurement flights to demonstrate autonomous UAV (Unmanned Aerial Vehicle) inspection of high-voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensors and logging software, measured ultraviolet (UV) emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace. The method has practicality and relevance but not adequacy; either improved UAV position determination technology or increased sensor range is needed to enable broad deployment of this method.
Quantum computing (QC) and deep learning have shown promise of supporting transformative advances and have recently gained popularity in a wide range of areas. Here, this paper proposes a hybrid QC-based deep learning framework for fault diagnosis of electrical power systems that combine the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks. Computational challenges stemming from the complexities of such deep learning models are overcome by QC-based training methodologies that effectively leverage the complementary strengths of quantum assisted learning and classical training techniques. The proposed hybrid QC-based deep learning framework is tested on a simulated electrical power system with 30 buses and wide variations of substation and transmission line faults, to demonstrate the framework’s applicability, efficiency, and generalization capabilities. High computational efficiency is enjoyed by the proposed hybrid approach in terms of computational effort required and quality of diagnosis performance over classical training methods. In addition, superior and reliable fault diagnosis performance with faster response time is achieved over state-of-the-art pattern recognition methods based on artificial neural networks (ANN) and decision trees (DT).
This paper demonstrates a novel optimization-based approach to estimating fault states in a DC power system. Potential faults changing the circuit topology are included along with faulty measurements. Our approach can be considered as a relaxation of the mixed estimation problem. We develop a linear model of the circuit and pose a convex problem for estimating the faults and other hidden states. A sparse fault vector solution is computed by using 11 regularization. The solution is computed reliably and efficiently, and gives accurate diagnostics on the faults. We demonstrate a real-time implementation of the approach for an instrumented electrical power system testbed, the ADAPT testbed at NASA ARC. The estimates are computed in milliseconds on a PC. The approach performs well despite unmodeled transients and other modeling uncertainties present in the system.
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.
Future air vehicles will increasingly incorporate electrical powertrains that require very tight system level integration of power, propulsion, thermal, fault protection, and airframe technologies. This paper provides an overview of a new category of thermal energy conversion technology that can be used to enable a fully solid-state integrated thermal and fault management electric aircraft protection system, while synergistically managing and recycling both the low-grade waste heat from electrical components and the high-grade waste heat from engine components. This is achieved with exergy amplification of the powertrain waste-heat, a new class of fast flight-weight breakers, new class of long variable conductance heat pipe with multiple switchable condensers, new class of turbofan integrated heat exchangers and a gradient-based powertrain system optimizer.
This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.
Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.