Improved parasitic speed controllers for space power systems
Design of parasitic speed controllers to regulate rotational speed and frequency of alternators in dynamic space power systems
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Design of parasitic speed controllers to regulate rotational speed and frequency of alternators in dynamic space power systems
The control of future power grids is migrating from a centralized to a distributed/decentralized scheme to enable a massive penetration of distributed energy resources with rising dependence on communication infrastructure. A common assumption made for most existing distributed/decentralized controllers is that local controllers would faithfully follow the designated controller dynamics based on the data received from communication channels. However, with increased probability of cyberattacks on Operational Technology infrastructure, such an assumption could be risky because proper execution of the controller dynamics is then built on trust in secure communication and computation. In this work, we leverage a cryptography technology known as zero-knowledge scalable transparent arguments of knowledge (zk-STARK) to verify the computational integrity of power grid control algorithms, with projected linear dynamics-based control schemes as the initial proof-of-concept test case. The method presented here converts the cybersecurity challenge of data integrity for grid control into a subset of computational integrity.
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A power control circuit connected between a solar array and an ion thruster receives voltage and current signals from the solar array. The control circuit multiplies the voltage and current signals together to produce a power signal which is differentiated with respect to time. The differentiator output is detected by a zero crossing detector and, after suitable shaping, the detector output is phase compared with a clock in a phase demodulator. An integrator receives no output from the phase demodulator when the operating point is at the maximum power but is driven toward the maximum power point for non-optimum operation. A ramp generator provides minor variations in the beam current reference signal produced by the integrator in order to obtain the first derivative of power.
For electric utilities, the ability to prevent or minimize lightning damage on personnel and power systems is of great importance. Therefore, the Swedish State Power Board, has been using data since 1983 from a nationwide lightning location system (LLS) for accurately locating lightning ground strikes. Lightning data is distributed and presented on color graphic displays at regional power network control centers as well as at the national power system control center for optimal data use. The main objectives for use of LLS data are: supervising the power system for optimal and safe use of the transmission and generating capacity during periods of thunderstorms; warning service to maintenance and service crews at power line and substations to end operations hazardous when lightning; rapid positioning of emergency crews to locate network damage at areas of detected lightning; and post analysis of power outages and transmission faults in relation to lightning, using archived lightning data for determination of appropriate design and insulation levels of equipment. Staff have found LLS data useful and economically justified since the availability of power system has increased as well as level of personnel safety.
Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.
The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.
System undergoing development uses distributed microprocessors to monitor and control photovoltaic power system. Equipment increases life expectancy and reliability of power system and decreases costs. Adapted to vehicular, residential, and commercial solar and nonsolar power systems. Equipment especially desirable in systems with overall power levels above few kilowatts.
The Space Station Module Power Management and Distribution (SSM/PMAD) bread-board is a test bed for the development of advanced power system control and automation. Software control in the SSM/PMAD breadboard is through co-operating systems, called Autonomous Agents. Agents can be a mixture of algorithmic software and expert systems. The early SSM/PMAD system was envisioned as being completely autonomous. It soon became apparent, though, that there would always be a need for human intervention, at least as long as a human interacts with the system in any way. In a system designed only for autonomous operation, manual intervention meant taking full control of the whole system, and loosing whatever expertise was in the system. Several methods for allowing humans to interact at an appropriate level of control were developed. This paper examines some of these intermediate modes of autonomy. The least humanly intrusive mode is simple monitoring. The ability to modify future behavior by altering a schedule involves high-level interaction. Modification of operating activities comes next. The coarsest mode of control is individual, unplanned operation of individual Power System components. Each of these levels is integrated into the SSM/PMAD breadboard, with support for the user (such as warnings of the consequences of control decisions) at every level.
Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.
Applications of boiling heat transfer in space can be found in the areas of thermal management, fluid handling and control, power systems, on-orbit storage and supply systems for cryogenic propellants and life support fluids, and for cooling of electronic packages for power systems associated with various instrumentation and control systems. Recent interest in exploration of Mars and other planets, and the concepts of in-situ resource utiliLation on Mars highlights the need to understand the effect of gravity on boiling heat transfer at gravity levels varying from 1>= g/g(sub e) >=10(exp -6). The objective of the proposed work was to develop a mechanistic understanding of nucleate boiling and critical heat flux under low and micro-gravity conditions when the velocity of the imposed flow is small. For pool boiling, the effect of reduced gravity is to stretch both the length scale as well as the time scale for the boiling process. At high flow velocities, the inertia of the liquid determines the time and the length scales and as such the gravitational acceleration plays little role. However, at low velocities and at low gravity levels both liquid inertia and buoyancy are of equal importance. At present, we have little understanding of the interacting roles of gravity and liquid inertia on the nucleate boiling process. Little data that has been reported in the literature does not have much practical value in that it can not serve as a basis for design of heat exchange components to be used in space. Both experimental and complete numerical simulations of the low velocity, low-gravity nucleate boiling process were carried out. A building block type of approach was used in that first the growth and detachment process of a single bubble and flow and heat transfer associated with the sliding motion of the bubble over the heater surface after detachment was studied. Liquid subcooling and flow velocity were varied parametrically. The experiments were conducted at 1 g(sub e), while varying the orientation of surface with respect to the gravity vector. In the laboratory experiments, holographic interferometry was used to obtain data on velocity and temperature fields associated with a bubble prior to, and after detachment and during sliding motion. A test rig for conducting experiments in the KC-135 was developed, but experiments could not be conducted due to the unavailability of the aircraft prior to completion of the project. Numerical simulations modeling the micro and macro regions of the bubble were carried out in three dimensions. The results of the experiments were used to validate analytical/numerical models.
An inverter-based resource (IBR) is a source of electricity that is asynchronously connected to the electrical grid via an electronic power converter. These power sources lack the intrinsic behaviors of the standard power plants, presenting specific challenges to system stability. These power sources lack the intrinsic behaviors of the standards power plants and their features are almost entirely defined by the control strategy used on them, presenting specific challenges to system stability as their penetration increases into the already fragile Bulk Power System (BPS). Utilizing renewable energy has a great upside but the electrical industry needs to understand the requirements like performing electromagnetic transient (EMT) simulation studies in planning and/or in interconnection studies. This paper presents the development of a high-fidelity EMT model based on commercial equipment and field parameters, which at the same time aims to study the increasing penetration of IBRs and their impact on the actual BPS. The open-source EMT model developed will be a benchmarking example that can be used to evaluate algorithms in the power grid (including evaluating grid-forming controls, power system monitoring and operation, etc.).
Software design specifications for developing environmental control and life support system (ECLSS) and electrical power system (EPS) programs into interactive computer programs are presented. Specifications for the ECLSS program are at the detail design level with respect to modification of an existing batch mode program. The FORTRAN environmental analysis routines (FEAR) are the subject batch mode program. The characteristics of the FEAR program are included for use in modifying batch mode programs to form interactive programs. The EPS program specifications are at the preliminary design level. Emphasis is on top-down structuring in the development of an interactive program.
The present conference discusses NASA mission planning for space nuclear power, lunar mission design based on nuclear thermal rockets, inertial-electrostatic confinement fusion for space power, nuclear risk analysis of the Ulysses mission, the role of the interface in refractory metal alloy composites, an advanced thermionic reactor systems design code, and space high power nuclear-pumped lasers. Also discussed are exploration mission enhancements with power-beaming, power requirement estimates for a nuclear-powered manned Mars rover, SP-100 reactor design, safety, and testing, materials compatibility issues for fabric composite radiators, application of the enabler to nuclear electric propulsion, orbit-transfer with TOPAZ-type power sources, the thermoelectric properties of alloys, ruthenium silicide as a promising thermoelectric material, and innovative space-saving device for high-temperature piping systems. The second volume of this conference discusses engine concepts for nuclear electric propulsion, nuclear technologies for human exploration of the solar system, dynamic energy conversion, direct nuclear propulsion, thermionic conversion technology, reactor and power system control, thermal management, thermionic research, effects of radiation on electronics, heat-pipe technology, radioisotope power systems, and nuclear fuels for power reactors. The third volume discusses space power electronics, space nuclear fuels for propulsion reactors, power systems concepts, space power electronics systems, the use of artificial intelligence in space, flight qualifications and testing, microgravity two-phase flow, reactor manufacturing and processing, and space and environmental effects.
Conventional control systems have traditionally been utilized for space-based power designs. However, the use of expert systems is becoming important for NASA applications. Rocketdyne has been pursuing the development of expert systems to aid and enhance control designs of space-based power systems. The need for integrated expert systems is vital for the development of autonomous power systems.
This presentation provides an overview of the laboratory evaluation of federated, hierarchical controls for distribution power system management.
Design and testing of 1200-Hz alternator and voltage and frequency controls for Brayton space power systems
Power and load priorty control concept for Brayton cycle power system providing speed control and field current control for alternator and load simulation which includes energy storage