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Embedded Fiber Bragg Grating (FBG) Sensors Fabricated by Ultrasonic Additive Manufacturing for High-Frequency Dynamic Strain Measurements
This article demonstrates high-frequency dynamic strain measurements using fiber Bragg grating (FBG) sensors embedded in metal parts. Using an ultrasonic additive manufacturing (UAM) process, FBGs inscribed in polyimide coated optical fibers were embedded in aluminum parts. An electromagnetic shaker was used to exert dynamic events on the embedded FBG sensors with frequencies from 1 to 10 kHz. The high-speed interrogation of FBG sensors was accomplished using a tunable vertical-cavity surface-emitting laser (VCSEL) and a high-speed interrogation system sampling at 120 kHz. The strain response measured by the FBG sensors was compared with real-time measurements using a laser velocimeter. A finite-element analysis (FEA) was performed to simulate responses to both static strain and high-frequency dynamic strain. In conclusion, results show that strains as small as 2.5$με$ can be resolved at frequencies up to 10 kHz.
High frequency dynamic pressure calibration technique
A high frequency dynamic calibration technique for pressure transducers has been developed using a siren pressure generator (SPG). The SPG is an inlet-area-modulated device generating oscillating waveforms with dynamic pressure amplitudes up to 58.6 kPa (8.5 psi) in a frequency range of 1 to 10 kHz. A description of the generator, its operating characteristics and instrumentation used for pressure amplitude and frequency measurements is given. Waveform oscillographs and spectral analysis of the pressure transducers' output signals are presented.
Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches
With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.
Loads and low frequency dynamics - An ENVIRONET data base
The loads and low frequency dynamics data base, part of Environet, is described with particular attention given to its development and contents. The objective of the data base is to provide the payload designer with design approaches and design data to meet STS safety requirements. Currently the data base consists of the following sections: abstract, scope, glossary, requirements, interaction with other environments, summary of the loads analysis process, design considerations, guidelines for payload design loads, information data base, and references.
Metal-embedded fiber optic sensor packaging and signal demodulation scheme towards high-frequency dynamic measurements in harsh environments
Optical fiber-based sensors are commonly used for industrial monitoring and control processes due to their small size, resistance to electromagnetic interference, and ability to perform a wide variety of high-precision measurements. However, implementing optical fiber sensors in harsh environments is challenging because they are small and fragile. Proper packaging of fiber-optic sensors could extend their use to harsh environments, including at high temperature and under high radiation. Furthermore, conventional fiber optic-based measurement systems often use computationally expensive signal processing algorithms which hinder their use in high-frequency dynamic applications. This work reports on the design of an optical fiber-pressure sensor system based on low-coherence interferometry that uses a metal-embedded optical fiber to provide a robust sensor package. Additionally, a novel phase demodulation scheme is proposed to extract length changes from the deformation of a thin diaphragm within a Fabry–Pérot cavity to measure pressure. The sensor has been tested to ±100 kPa, has a theoretical linear response over a 270 kPa dynamic range (24 dB maximum signal-to-noise ratio), and can resolve pressure transients up to 3910 kPa/s. Sampling at 100 kHz, the present sensor can resolve 2 kPa dynamic pressures at frequencies up to 2 kHz. Faster transients on the order of tens to hundreds of kHz can theoretically be resolved at the expense of decreasing the maximum resolvable amplitude. A methodology for designing a LCI pressure sensor for a given application is outlined based on the limitations imposed by the Nyquist criterion, the diaphragm's resonant frequency, the LCI optoelectronics, and the phase demodulation scheme. The sensor is the first to implement a low-coherence light source and a Fabry–Pérot interferometer designed to provide real-time high-fidelity pressure measurements using a metal-embedded optical fiber. The demonstrated sensor provides a platform for sensing in harsh conditions such as in nuclear and energy applications.
Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations
Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.
Dynamic frequency control in power networks
Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the processor is configured by the frequency control application to calculate a plurality of updated node operating parameters using a distributed process to determine the updated node operating parameter using the node operating parameters, where the distributed process controls network frequency in the power distribution network; and adjust the node operating parameters.
Evaluation of space shuttle main engine fluid dynamic frequency response characteristics
In order to determine the POGO stability characteristics of the space shuttle main engine liquid oxygen (LOX) system, the fluid dynamic frequency response functions between elements in the SSME LOX system was evaluated, both analytically and experimentally. For the experimental data evaluation, a software package was written for the Hewlett-Packard 5451C Fourier analyzer. The POGO analysis software is documented and consists of five separate segments. Each segment is stored on the 5451C disc as an individual program and performs its own unique function. Two separate data reduction methods, a signal calibration, coherence or pulser signal based frequency response function blanking, and automatic plotting features are included in the program. The 5451C allows variable parameter transfer from program to program. This feature is used to advantage and requires only minimal user interface during the data reduction process. Experimental results are included and compared with the analytical predictions in order to adjust the general model and arrive at a realistic simulation of the POGO characteristics.
Interactive Power to Frequency Dynamics Between Grid-Forming Inverters and Synchronous Generators in Power Electronics-Dominated Power Systems
With increased attention on grid-forming inverters as a power system stabilizing device during high shares of inverter-based resource operations, there is a present need for a transparent and methodical investigation of the inverted and direct power to frequency control capabilities and impacts of these devices on emerging power systems. Here, analysis of the frequency dynamics of the droop-controlled grid-forming inverter and the synchronous generator illuminates the inverted active power-frequency relationship and the frequency response order reduction, forming the basis for novel, nonlinear frequency control approaches. Device-level electromagnetic transient domain simulations corroborate the order-reduction findings, establish that a properly designed dc-side system has a negligible impact on power transfer and will not impede frequency regulation, and confirm the primary frequency response improvement with nonlinear control. Simulations of the 9- and 39-bus test systems validate the order reduction and associated decoupling of the nadir and rate of change of frequency in larger networks. Oscillatory mode analysis confirms the grid-forming benefit of increased damping; decreased damping is observed at shares above 80%, but not at 100%. Finally, simulations on a validated Maui power system model with a 96% of inverter-based resources model yield a trend toward a first-order response.
Multicolored microwave absorbers with dynamic frequency modulation
Microwave-absorbing materials are extensively used in intelligent electronic devices and stealth technologies where the ability to dynamically adjust microwave-absorbing capacity in response to specific requirements is vitally important. Herein, we report a new approach for constructing dynamically tunable microwave absorbers with ultrawide tunable frequency ranges that are simultaneously endowed with vibrant structural colors. Here, the methodology produces structural colors and adjusts absorption properties by constructing ZnO coatings with precisely adjustable thicknesses on a polypyrrole/melamine foam (PPy/MF) surface by atomic layer deposition (ALD) in conjunction with a pressure-driven strategy that regulates the compression ratio. The electrical conductivity, electromagnetic parameters, thickness, and pore size of the ZnO-coated PPy/MF (ZnO/PPy/MF) are effectively adjusted, and simply applying external pressure widens the tunable frequency range and the effective absorption bandwidth. As a result, the effective absorption frequency of ZnO/PPy/MF is dynamically adjustable from the S band to the Ku band, thereby covering 94.3% of the entire microwave spectrum. Moreover, the brilliant and uniform structural colors of ZnO/PPy/MF, which span various color categories, are precisely regulated by tuning the thickness of the ZnO coating by adjusting the number of ALD cycles. ZnO/PPy/MF is strongly hydrophobic, which endows it with remarkable self-cleaning properties. Therefore, ZnO/PPy/MF provides a conceptually novel platform for the development of next-generation smart microwave-absorbing materials due to its integrated dynamic frequency-regulating ability, brilliant structural coloration, and self-cleaning features.
Extracting dynamical frequencies from invariants of motion in finite-dimensional nonlinear integrable systems
Integrable dynamical systems play an important role in many areas of science, including accelerator and plasma physics. An integrable dynamical system with n degrees of freedom possesses n nontrivial integrals of motion, and can be solved, in principle, by covering the phase space with one or more charts in which the dynamics can be described using action-angle coordinates. To obtain the frequencies of motion, both the transformation to action-angle coordinates and its inverse must be known in explicit form. However, no general algorithm exists for constructing this transformation explicitly from a set of n known (and generally coupled) integrals of motion. In this study we describe how one can determine the dynamical frequencies of the motion as functions of these n integrals in the absence of explicitly known action-angle variables, and we provide several examples.
A scalable planning framework of energy storage systems under frequency dynamics constraints
As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.
High frequency dynamic engine simulation
A digital computer simulation of a mixed flow, twin spool turbofan engine was assembled to evaluate and improve the dynamic characteristics of the engine simulation to disturbance frequencies of at least 100 Hz. One dimensional forms of the dynamic mass, momentum and energy equations were used to model the engine. A TF30 engine was simulated so that dynamic characteristics could be evaluated against results obtained from testing of the TF30 engine at the NASA Lewis Research Center. Dynamic characteristics of the engine simulation were improved by modifying the compression system model. Modifications to the compression system model were established by investigating the influence of size and number of finite dynamic elements. Based on the results of this program, high frequency engine simulations using finite dynamic elements can be assembled so that the engine dynamic configuration is optimum with respect to dynamic characteristics and computer execution time. Resizing of the compression systems finite elements improved the dynamic characteristics of the engine simulation but showed that additional refinements are required to obtain close agreement simulation and actual engine dynamic characteristics.
Experimental Validation of a Diesel Genset Frequency Dynamics Model for Use in Remote Area Power Systems
Not Available
Impact of Transportation Electrification on the System's Dynamic Frequency Response
Transportation electrification is an integral component of the energy decarbonization transition. This paper investigates the impact of distributed energy resources (DERs), including distributed photovoltaics (DPV) and electric vehicles (EVs), in the primary frequency response of the power grid. Increasing DER adoption poses challenges to maintaining grid frequency stability. However, DERs' ability to provide fast frequency regulation services-primary frequency response (PFR) and secondary frequency response (SFR)-can be exploited to recover the frequency after an N-1 contingency event in the system. This paper also investigates the importance of a droop control strategy through dynamic models of DPV and EV to provide the primary frequency regulation services following the contingency event. A dynamic EV model, based on the PVD1 model Western Electricity Coordinating Council (WECC) introduced, has been used for the simulation. Further, DERs' primary frequency response is studied for five different cases of DER penetration levels after the system is exposed to the generator trip. Additionally, different frequency regulation capacities of EVs are analyzed. The studies show that an increment in DERs capacity providing effective PFR can improve the system frequency nadir and stabilize the frequency faster after the generation trip contingency.
Impact of Transportation Electrification on the System's Dynamic Frequency Response: Preprint
Transportation electrification is an integral component of the energy decarbonization transition. This paper investigates the impact of distributed energy resources (DERs), including distributed photovoltaics (DPV) and electric vehicles (EVs), in the primary frequency response of the power grid. Increasing DER adoption poses challenges to maintaining grid frequency stability. However, DERs' ability to provide fast frequency regulation services-primary frequency response (PFR) and secondary frequency response (SFR)-can be exploited to recover the frequency after an N-1 contingency event in the system. This paper also investigates the importance of a droop control strategy through dynamic models of DPV and EV to provide the primary frequency regulation services following the contingency event. A dynamic EV model, based on the PVDl model Western Electricity Coordinating Council (WECC) introduced, has been used for the simulation. Further, DERs' primary frequency response is studied for five different cases of DER penetration levels after the system is exposed to the generator trip. Additionally, different frequency regulation capacities of EVs are analyzed. The studies show that an increment in DERs capacity providing effective PFR can improve the system frequency nadir and stabilize the frequency faster after the generation trip contingency.
Light-Induced Dynamic Frequency Shifting of Microwave Photons in a Superconducting Electro-Optic Converter
Hybrid superconducting-photonic microresonators are a promising platform for realizing microwave-to-optical transduction. However, the absorption of scattered photons by the superconductors leads to unintended microwave resonance-frequency variation and line-width broadening. Here, we experimentally study the dynamics of this effect and its impact on microwave-to-optics conversion in an integrated lithium niobate–superconductor hybrid-resonator platform. Here, we unveil an adiabatic frequency shifting of the intracavity microwave photons induced by the fast photoresponses of the thin-film superconducting resonator. As a result, the temporal and spectral responses of electro-optics transduction are modified and well described by our theoretical model. This work provides insights into the light-induced conversion dynamics that must be considered in future designs of hybrid superconducting-photonic system.