Low-frequency Model Research Articles

Multi-Observer Fusion Based Minimal-Sensor Adaptive Control for Ship Dynamic Positioning Systems

This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.

Characterisation of Harmonic Resonance Phenomenon of Multi-Parallel PV Inverter Systems: Modelling and Analysis

Solar PV inverters require output filters to reduce unwanted harmonics in their output, where LCL filters are a more economical choice than larger inductance-only filters. A drawback of these filters is that they can introduce power quality disturbances, especially at higher frequencies (above 2 kHz). This paper investigates and characterises the resonance phenomenon introduced by different filter types, i.e., LC or LCL, and identifies their behavioural change when combined with multiple parallel grid-tied PV inverter systems. MATLAB/Simulink modelling aspects of PV inverter systems related to resonance phenomenon are presented, including establishing resonance at a specific frequency where potentially large variations in the parameter selection across manufacturers may exist. In addition, a method is developed to establish output filter frequency response through measurements, which is used to develop validated solar PV harmonic models for high-frequency analysis. The low-frequency harmonic models can be used up to the resonant frequency where the current flowing through the filter capacitor is insignificant compared to the current flowing into the electricity network.

Seismic post-stack impedance inversion using geophysics-informed deep learning neural network

Traditionally, seismic post-stack impedance inversion is implemented using linear optimization algorithms. Recently, deep learning neural networks have been successfully used to estimate the impedance from seismic data. First, we demonstrate the general workflow of seismic post-stack impedance inversion using supervised neural network (SNN). Next, we propose to compute seismic impedance using geophysics-informed neural network (GINN). Similarly with linear optimization algorithms, the inputs of GINN include real seismograms, a wavelet, and a low frequency model. The loss function of GINN is designed to minimize the difference between real seismograms and synthetic seismic seismograms that are computed from estimated impedance and input wavelet. To avoid lateral discontinuity of estimated impedance, the weights of GINN are trained using the seismograms of all seismic traces. We use synthetic and real seismic data to discuss the advantages and limitations of GINN, SNN, and traditional linear optimization algorithms. Not surprisingly, signal-to-noise ratio (SNR) of seismic data and the phase of seismic wavelet are the most important factors that affect the accuracy of impedance estimated using GINN. The accuracy and resolution of impedance estimated using GINN is higher than linear optimization and SNN if the seismic data has a high signal-to-noise ratio. The synthetic examples demonstrate that the accuracy of impedance calculated using SNN increases with the number of available training wells and linear optimization algorithms are more robust to noise than GINN and SNN.

The effect of time-varying characteristics of shallow-sea waveguides on low-frequency acoustic signal transmission

The time-varying characteristic of shallow sea channel plays one of the most important role in the exploration of ocean. However, there is still lack of estimation model to describe the time-varying characteristics of shallow water low-frequency acoustic channels. This paper proposes a low-frequency channel model based on wave equation and ray theory to estimate the time-varying characteristics of shallow sea. Firstly, for the actual characteristics of shallow sea, suitable boundary conditions are designed to solve the wave equation. Secondly, the constraint conditions of shallow sea wave equation are optimized by combining ray theory. Then, based on the physical characteristics of shallow sea, time-delay variation and phase variation are solved by shallow wave equation. Combining bellhop propagation model with ocean disturbance model, the simulation is designed to verify the time-varying characteristic model of low-frequency shallow sea in this paper. Finally, with different propagation distance measurement experiment of sea, estimation value of time-delay variation and phase variation are confirmed, which is significant to shallow sea channel.

Characterization of Natural Gas Hydrate Constrained by Well and Seismic Data in Qiongdongnan Basin

This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin.

Experimental Study of Omnidirectional Scattering Characteristics of Complex Scale Targets Based on Coded Signals

To investigate the omnidirectional geometric scattering characteristics of an underwater vehicle and the target detection performance of phase coded (BPSK) signals, acoustic scattering tests were carried out in an anechoic chamber using the Suboff scale model. To mitigate the overlapping interference of the direct wave on the scattering wave in the limited test space, physical suppression with an “anechoic cloak” and direct wave cancellation were proposed. Target echo and reflection wave tests at different offset angles were carried out, and the accuracy of the BPSK signal in acquiring highlight features and the feasibility of anechoic chamber tests were verified through comparison with theoretical range profiles. A series of echo and omnidirectional scattering characteristics were obtained through the experiment and simulation, which verified the effectiveness of the low-frequency submarine model detection (there were still strong scattering waves at the dimensionless frequency ka = 1.88). Comparison tests of CW, LFM, and BPSK signals were carried out, and the measured data proved that the BPSK signal had the advantages of low sidelobe, high resolution, and noise resistance in target detection. The acoustic scattering test method designed in this study and the omnidirectional scattering characteristics obtained can be used as a reference for semi-physical target acoustic scattering simulations and practical multistatic detection.

Introducing the DI and PFSDI Methods: New Inversion Methods that Do Not Need the Low Frequency Model (LFM)

Introducing the DI and PFSDI Methods: New Inversion Methods that Do Not Need the Low Frequency Model (LFM)

Tuning effect in time-lapse seismic inversion for CO2 plume monitoring at Sleipner field

Over 20 million tons of CO2‌ have been injected into the sandy Utsira formation of the Sleipner field in the North Sea basin. The thin layering of sands, CO2-saturated sands, and intra-formation thin shales cause interference effects in the seismic response of the monitor surveys. Initial analysis of the amplitude change suggests over 60 % change in the relative acoustic impedance of the reservoir between the 1996 and 2010 surveys. This study follows a multi-stage inversion scheme applied to time-lapse seismic monitoring of the Sleipner field. At each stage, the errors and uncertainties caused by noise and tuning are deeply analyzed. Time-shift estimation from seismic data shows spurious features caused by tuning, specifically using the window-based methods. The time-strain inversion builds a low-frequency initial model for the subsequent model-based inversion but is contaminated by remnants of noise and interference. The synthetic wedge modelling and analysis provides the origins and severity of the tuning error in time-shift and time-strain estimations at the Sleipner field. The model-based inversion removes noise and injects high-frequency components into the results, improving the outcome of time-strain inversion. However, it fails to fully eliminate the tuning imprints and leaves strong traces of error on the results. Afterward, the time-lapse Bayesian seismic inversion slightly adjusts the outcome and shows how deep the influence of interference effect on the time-lapse inversion is. In addition, the complementary discussion on rock physical models in estimating the saturation changes highlights how the tuning error can lead to flawed quantitative interpretation.

Application of the high-resolution layer-stripping constrained seismic inversion method in thin reservoirs: a case study of Triassic reservoir prediction in Lunnan, Tarim Basin, NW China

The Lunnan Oilfield’s Triassic reservoir is a braided river delta deposition, exhibiting notable characteristics such as rapid lateral sand body changes, thin reservoir thickness, and deep reservoir burial. However, the conventional seismic inversion technique used in this geological context is hindered by the low accuracy of the low-frequency model, resulting in inversion outcomes with limited vertical resolution. Consequently, accurately characterizing the reservoir distribution becomes challenging, posing a significant obstacle for the subsequent exploration and development. In this study, we introduce an enhanced seismic inversion approach using high-resolution layer-stripping constraints. The process begins with the creation of a fine-grained layer sequence grid on wells through Integrated Prediction Error Filter Analysis (INPEFA) technology. Subsequently, this grid aids in interpreting high-resolution sequence layers on the seismic volume, obtained through a frequency division RGB (Red, Green, and Blue) fusion technique. Finally, a novel nonlinear stochastic inversion method is formulated, utilizing the high-resolution sequence to refine both the initial model and inverted results. This innovative seismic inversion technique significantly enhances accuracy, particularly in predicting thin reservoirs, approximately 3 meters thick. The application of this method in a case study on Triassic reservoir prediction in Lunnan, Tarim Basin, NW China, demonstrates its promising future applications.

Modeling and Control Research of Fractional-Order Cascaded H-Bridge Multilevel STATCOM

Abstract: This paper introduces fractional-order capacitors and fractional-order inductors into the conventional integer-order cascaded H-bridge multilevel static compensator (ICHM-STATCOM), thereby constructing the main circuit of the fractional-order cascaded H-bridge multilevel static compensator (FCHM-STATCOM). Mechanism-based modeling is employed to establish switching function models and low-frequency dynamic models for the FCHM-STATCOM in the three-phase stationary coordinate system (a-b-c). Subsequently, fractional-order rotating coordinate transformation is introduced to establish the mathematical model of the FCHM-STATCOM in the synchronous rotating coordinate system (d-q). Additionally, a fractional-order proportional-integral (FOPI)-based fractional-order dual closed-loop current decoupling control strategy is proposed. Finally, this paper validates the correctness of the established mathematical models through digital simulation. Moreover, the simulation results demonstrate that by appropriately selecting the order of fractional-order capacitors and fractional-order inductors, the FCHM-STATCOM exhibits superior dynamic and static characteristics compared to the conventional ICHM-STATCOM, and the FCHM-STATCOM provides a more flexible reactive power compensation solution for power systems.

A MODELING OF RF PROPAGATION FOR LOW FREQUENCY IN NATURAL CAVES WITH 3D MODEL SIMULATION

The study of the RF propagation model within natural caves remains challenging. In the past, the propagation model in the tunnel was compared using waveguide theory because of the electrical conductivity of tunnel walls. However, waveguide models have limitations because a cut-off frequency from the tunnel dimension does not allow frequencies lower than the cut-off frequency to propagate. Although the frequency wave lower than the cut-off frequency cannot theoretically propagate, in practice, it can propagate through the cave. Therefore, this paper presents the modelling of RF propagation at low frequencies in natural caves using empirical methods by measuring the waves propagated within cave passages at the Chiang Dao Cave (limestone) and the Patihan Cave (sandstone). The measurement will focus on analyzing and comparing factors, propagation mechanisms, and behavior, which cover the frequencies at 300, 1000, 1650, 2325, and 3000 kHz, to develop the low-frequency attenuation model within the cave proposed in this paper. The 3D cave models obtained from a LiDAR scanner were used to calculate the physical factors of the cave walls, and then the path losses were calculated using our proposed model and compared to the experimental results. In addition, the path losses calculated using 1) CST simulation software and 2) the waveguide model were also compared with the results from the proposed model. The results obtained from the proposed model provide attenuation values similar to those of the experimental results. Finally, the concept of the low-frequency attenuation model can be used for the prediction and analysis of the propagation performance of low-frequency waves in caves, tunnels, or underground mines and lead to the development of cave communication technology and electromagnetic knowledge in the future.

Comparison of techniques based on frequency response analysis for state of health estimation in lithium-ion batteries

Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy.

A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Low-frequency conductivity of low wear high-entropy alloys

High-entropy alloys (HEAs) provide new research avenues for alloy combinations in the periodic table, opening numerous possibilities in novel-alloy applications. However, their electrical characteristics have been relatively underexplored. The challenge in establishing an HEA electrical conductivity model lies in the changes in electronic characteristics caused by lattice distortion and complexity of nanostructures. Here we show a low-frequency electrical conductivity model for the Nb-Mo-Ta-W HEA system. The cocktail effect is found to explain trends in electrical-conductivity changes in HEAs, while the magnitude of the reduction is understood by the calculated plasma frequency, free electron density, and measured relaxation time by terahertz spectroscopy. As a result, the refractory HEA Nb15Mo35Ta15W35 thin film exhibits both high hardness and excellent conductivity. This combination of Nb15Mo35Ta15W35 makes it suitable for applications in atomic force microscopy probe coating, significantly improving their wear resistance and atomic-scale image resolution.

Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

Comparison of statistical low-frequency earthquake activity models

Slow earthquakes are slow fault slip events. Quantifying and monitoring slow earthquake activity characteristics are important, because they may change before large earthquakes occur. Statistical seismicity models are useful for quantifying seismicity characteristics. However, no standard statistical model exists for slow earthquake activity. This study used a high-quality catalog of low-frequency earthquakes (LFEs), a type of slow earthquake, in the Nankai subduction zone from April 2004 to August 2015 and conducted the first comparison of existing statistical LFE activity models to determine which model better describes LFE activity. Based on this comparison, this study proposes a new hybrid model that incorporates existing model features. The new model considers the LFE activity history in a manner similar to the epidemic-type aftershock sequence (ETAS) model and represents the LFE aftershock rate (subsequent LFE occurrence rate) with a small number of model parameters, as in the Omori–Utsu aftershock law for regular earthquakes. The results show that the proposed model outperforms other existing models. However, the new model cannot reproduce a feature of LFE activity: the sudden cessation of intense LFE bursts. This is because the new model superimposes multiple aftershock activities and predicts extremely high seismicity rates during and after the LFE bursts. I suggest that reproducing and successfully predicting the sudden cessation of intense LFE bursts is critical for the further improvement of statistical LFE activity models. In addition, the empirical equations formulated in this study for the LFE aftershock rates may be useful for future statistical and physical modeling of LFE activity.Graphical

Changes in the stratigraphic architecture as the response to the Upper Cretaceous tectonic and climatic interplay in the Sanfranciscana Basin, Brazil

During the Cretaceous, the Brazilian Intracratonic Sanfranciscana Basin was characterised by fluvial and aeolian deposition, whose facies, palaeosols, and stratigraphic architecture indicate significant changes in tectonics and climate. This work proposes a low-frequency sequence stratigraphic model, using the facies and palaeosols distribution as the stratigraphic proxy for identifying variations in the accommodation space, sediment supply, and their relationship with the tectonic and climatic events. The Unconformity U-0 was generated by the extensional tectonic processes during the Gondwana breakup in the Early Cretaceous and marked the initial continental sedimentation. The Lower Cretaceous Sequence SF1 overlies this unconformity, constituted by fluvial and lacustrine deposits. From south to north, the unconformity U-1 truncates the top of the Lower Cretaceous Sequence SF1 and the Precambrian basement, respectively. Overlaying Unconformity U-1 are the Upper Cretaceous volcanic rocks, fluvial, and aeolian deposits of the Sequence SF2. Because of the vertical and lateral changes, Sequence SF2 is divided into two sequences, SF2A and SF2B, separated by the regional Unconformity U-2, representing climatic and tectonic changes in the basin. During the late stage of sedimentation of Sequence SF1, the ratio between accommodation (A) and sediment supply (S) was positive in the south and negative in the basin's north. The evolution of Sequence SF2A records a negative A/S ratio, followed by an abrupt change to a positive A/S ratio in Sequence SF2B, leading to the genesis of the regional erosive surface of Unconformity U-2. Sequence SF2B also records increased humidity from arid climatic conditions, marked by the 3D regional distribution of facies and palaeosols, linked to the climatic changes detected in South America for the Late Cretaceous.

Flexible Quadrotor Unmanned Aerial Vehicles: Spatially Distributed Modeling and Delay-Resistant Control

This paper introduces an analytical framework for the derivation of distributed-parameter equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, to facilitate the controller design. A delay-resistant low-frequency adaptive controller is employed, which prevents excessive oscillations due to flexible dynamics, compensates uncertainties, and addresses the inherent time delay. In addition to this, a delay-dependent stability condition for the overall system dynamics is obtained including the human operator with reaction time delay, the adaptive controller, and the flexible quadrotor dynamics with input delay. With comparative simulation studies, it is demonstrated that the flexible arm tip oscillations are significantly reduced when the low-frequency delay-resistant closed-loop reference model adaptive controller is used, compared to a closed-loop reference model adaptive controller and a conventional model reference adaptive controller.

Prestack sparse envelope seismic inversion method adopting the L0−L2-norm regularization

The high drilling costs limit the possibility of obtaining reliable low-frequency constrained models in marine environments or areas with sparse well-log data, making it difficult to constrain lateral continuity and deep data. To enhance stability and accuracy in prestack seismic inversion in these areas, an elastic parameter estimation approach using sparse envelope inversion with [Formula: see text]-norm regularization is proposed. The method combines signal sparse representation and modulation theories to derive a new formula for sparse envelope extraction at lower frequencies. By applying [Formula: see text]-norm regularization to the sparse envelope, we obtain parameter inversion results with smoothed trends, augmenting low-wavenumber information for improved model constraints. In addition, taking the envelope inversion results obtained by the [Formula: see text] norm as the model constraint, the sparsest inversion results with obvious block-like characteristics are obtained by regularizing the inversion equation with the [Formula: see text] norm. Notably, the new method effectively suppresses wavelet side lobes, resulting in stable and accurate inversions without the need for initial low-frequency models. A synthetic example is used to illustrate the feasibility and stability of proposed approach and further demonstrate its practicality in reservoir parameter estimation through a field case study of gas exploration.

Predicting the thermal maturity of source rock from well logs and seismic data in basins with low-degree exploration

Vitrinite reflectance (Ro) is a significant geological index that evaluates the thermal maturity of source rock. However, measured Ro data and suitable evaluation methods are lacking, making it difficult to predict the thermal maturity of source rock in basin, especially in those with a low exploration degree. In this study, we propose a novel method for estimating Ro from well logs and seismic data using seismic velocity inversion and the vitrinite reflectance-mudstone porosity (Ro-ϕ) model. First, using a wavelet neural network, acoustic transit time curve was reconstructed from spontaneous potential, gamma rays, resistivity, and density logs, and new curve was used as inputs in a colored inversion to obtain seismic relative velocity. Second, a low-frequency model was established to counteract critical frequency and sequence framework constraints. Third, the seismic absolute velocity was merged by relative and low-frequency velocity components. Finally, Ro distributions were calculated using the Ro-ϕ model. Our findings indicate that the inversion effect improved with low-frequency supplement; compared with the graphic and geochemical method, the Ro-ϕ model predicted Ro in poor-data areas more accurately, with a relative error of <9.5205%. We also propose an explanation for the appearance of highly mature source rocks distributed from dispersion to aggregation in depression-rift transitions: the thick stratum in the downthrow wall of boundary faults promote source rock maturity in the rift period; hence, in an open depression, the source rocks in large subsidence areas are highly mature, particularly near the downthrow wall of a boundary fault, which has the highest maturity owing to the superimposed subsidence of the overlying rifted basin.