Speed Estimation Research Articles

Comparison of REWS and LIDAR-based feedforward control for fatigue load mitigation in wind turbines

While optimizing wind energy harvesting has always been the principal goal of wind turbine control, extensive research has been conducted on the use of advanced control methodologies aiming to maximize the turbine’s power generation while also achieving secondary performance objectives, such as fatigue load minimization. Some recent studies have investigated the application of a classical control methodology known as exact output regulation for improved wind turbine control. This control method is designed to achieve the rejection of known input disturbances, while also ensuring the system output tracks a desired reference signal. These studies all assumed wind preview information from LIDAR measurements was available to support the controller design.In this paper we investigate the use of exact output regulation for turbine control, using wind speed estimates obtained from rotor speed measurements. Simulated performance comparisons conducted over a wide range of mean wind speeds show that the exact output regulation methodology using wind speed estimates derived from rotor measurements can deliver similar reductions in fatigue loads to those obtainable with LIDAR measurements, without compromising power generation.

Quantifying Multifrequency Ocean Altimeter Wind Speed Error Due to Sea Surface Temperature and Resulting Impacts on Satellite Sea Level Measurements

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation.

Limitations of Curl and Directional Filters in Elastography

In the approaches to elastography, two mathematical operations have been frequently applied to improve the final estimate of shear wave speed and shear modulus of tissues. The vector curl operator can separate out the transverse component of a complicated displacement field, and directional filters can separate distinct orientations of wave propagation. However, there are practical limitations that can prevent the intended improvement in elastography estimates. Some simple configurations of wavefields relevant to elastography are examined against theoretical models within the semi-infinite elastic medium and guided waves in a bounded medium. The Miller–Pursey solutions in simplified form are examined for the semi-infinite medium and the Lamb wave symmetric form is considered for the guided wave structure. In both cases, we examine simple but practical wave combinations that can prevent the curl and directional filter operations from directly providing an improved measure of shear wave speed and shear modulus. Additional factors including signal-to-noise and the support of filters also restrict the applicability of these strategies for improving elastographic measures. Thus, some implementations of shear wave excitations applied to the body and to bounded structures within the body are shown to involve waves that are not easily resolved by the vector curl operator and directional filters. These limitations may be overcome by more advanced strategies or simple improvements in baseline parameters including the size of the region of interest and the number of shear waves propagated within.

Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation

Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.

Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning

This paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm was used to create the speed estimation model. The model was based on regression trees algorithms, with base regressors forming a final voting ensemble. The model was trained, validated, and tested using a database containing results from full-scale crash tests and numerical simulations. The developed machine learning model had a mean absolute error of 6.76 km/h with a standard deviation of 1.01 km/h on the cross-validation set, and a coefficient of determination, R2, of 0.85. This model was used to estimate the impact speed of the vehicle in three real road accidents with the W-beam barrier, and then the determined speeds were used in additional simulations to verify the results. A good quantitative and qualitative agreement between the simulation and accident outcomes was achieved, and this confirmed that the proposed method and the developed ML models combined with numerical simulations and full-scale crash tests can be effective tools for estimating the speed of the vehicle at impact with a roadside barrier.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or low penetration of mobile sensors, speeds detected are incomplete and far from network-wide use. In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy. These drawbacks call for effective techniques to recover credible estimates from the incomplete data. In this work, we first identify the issue as a spatiotemporal kriging problem and propose a Laplacian enhanced low-rank tensor completion (LETC) framework featuring both low-rankness and multi-dimensional correlations for large-scale traffic speed kriging under limited observations. To be specific, three types of speed correlation including temporal continuity, temporal periodicity, and spatial proximity are carefully chosen and simultaneously modeled by three different forms of graph Laplacian, named temporal graph Fourier transform, generalized temporal consistency regularization, and diffusion graph regularization. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging. By performing experiments on two public million-level traffic speed datasets, we finally draw the conclusion and find our proposed LETC achieves the state-of-the-art kriging performance even under low observation rates, while at the same time saving more than half computing time compared with baseline methods. Some insights into spatiotemporal traffic data modeling and kriging at the network level are provided as well.

Evaluation of Nakagami and Birnbaum-Saunders probability distribution for wind speed and power estimation

This study compares three probability density functions (PDFs) for understanding and estimating wind power based on wind patterns: the commonly-used Weibull distribution, the relatively-new Birnbaum-Saunders distribution, and the Nakagami distribution. The wind profile of Jumla, Nepal was analyzed using data from 2004 to 2014. The Nakagami distribution performed similarly to the Weibull distribution in terms of understanding wind patterns. However, for estimating wind power, the Nakagami distribution was found to be more effective than the Weibull distribution in most cases. The Birnbaum-Saunders distribution was found to be the least effective of the three PDFs compared.

Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines

Abstract. Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation, and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be understood. In this work, we investigate the influence of floater motions on wind speed measurements from forward-looking nacelle-based lidar systems mounted on floating offshore wind turbines (FOWTs) and suggest approaches for correcting motion-induced effects. We use an analytical model, employing the guide for the expression of uncertainty in measurements (GUM) methodology and a numerical lidar simulation for the quantification of uncertainties. It is found that the uncertainty of lidar wind speed estimates is mainly caused by the fore–aft motion of the lidar, resulting from the pitch displacement of the floater. Therefore, the uncertainty is heavily dependent on the amplitude and the frequency of the pitch motion. The bias of 10 min mean wind speed estimates is mainly influenced by the mean pitch angle of the floater and the pitch amplitude. We correct motion-induced biases in time-averaged lidar wind speed measurements with a model-based approach, employing the developed analytical model for uncertainty and bias quantification. Testing of the approach with simulated dynamics from two different FOWT concepts shows good results with remaining mean errors below 0.1 m s−1. For the correction of motion-induced fluctuation in instantaneous measurements, we use a frequency filter to correct fluctuations caused by floater pitch motions for instantaneous measurements. The correction approach's performance depends on the pitch period and amplitude of the FOWT design.

Movedesign: Shiny R app to evaluate sampling design for animal movement studies

Abstract Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space‐use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner. We developed ‘movedesign’, a user‐friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration). Leveraging the ‘ctmm’ R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous‐time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade‐off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘movedesign’ has broad applications for researchers and decision‐makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade‐off associated with these choices.

Perceiving animacy from kinematics: visual specification of life-likeness in simple geometric patterns.

Since the seminal work of Heider and Simmel, and Michotte's research, many studies have shown that, under appropriate conditions, displays of simple geometric shapes elicit rich and vivid impressions of animacy and intentionality. The main purpose of this review is to emphasize the close relationship between kinematics and perceived animacy by showing which specific motion cues and spatiotemporal patterns automatically trigger visual perceptions of animacy and intentionality. The animacy phenomenon has been demonstrated to be rather fast, automatic, irresistible, and highly stimulus-driven. Moreover, there is growing evidence that animacy attributions, although usually associated with higher-level cognition and long-term memory, may reflect highly specialized visual processes that have evolved to support adaptive behaviors critical for survival. The hypothesis of a life-detector hardwired in the perceptual system is also supported by recent studies in early development and animal cognition, as well as by the issue of the "irresistibility" criterion, i.e., the persistence of animacy perception in adulthood even in the face of conflicting background knowledge. Finally, further support for the hypothesis that animacy is processed in the earliest stages of vision comes from recent experimental evidence on the interaction of animacy with other visual processes, such as visuomotor performance, visual memory, and speed estimation. Summarizing, the ability to detect animacy in all its nuances may be related to the visual system's sensitivity to those changes in kinematics - considered as a multifactorial relational system - that are associated with the presence of living beings, as opposed to the natural, inert behavior of physically constrained, form-invariant objects, or even mutually independent moving agents. This broad predisposition would allow the observer not only to identify the presence of animates and to distinguish them from inanimate, but also to quickly grasp their psychological, emotional, and social characteristics.

A Precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Diagnosis and Efficiency Estimation of Induction Motors

Efficiency estimation and diagnosis via MCSA require precise knowledge of speed. In an industrial environment, speed must be obtained with a non-invasive, automatic and general method. Recent studies have shown that Sensorless Speed Estimation techniques based on detecting Rotational Frequency Sideband Harmonics (RFSHs) or Rotor Slot Harmonics (RSHs) are best suited to these purposes. RFSHs-based methods are easier to apply as they only depend on the number of poles. RSHs-based are much more accurate due to their wider bandwidth. Yet, their use is not trivial as they require to identify the RSHs family, assign to each RSH its order of the current harmonic ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula> ) and determine the number of rotor slots ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> ), a rarely known parameter. This paper ends with this trade-off between accuracy and applicability by proposing a novel RSHs-based technique that, for the first time in technical literature, eliminates the need to estimate the number of rotor slots and provides a reliable and automatic procedure to locate the RSHs family and determine their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula> indices. Finally, the method is validated under all types of conditions and motor designs, by simulations, lab tests and with 105 industrial motors, highlighting its high accuracy (errors below 0.05 rpm), and applicability.

Refraction-Based Speed of Sound Estimation in Layered Media: an Angular Approach.

Speed of sound estimation in ultrasound imaging is a growing modality with several clinical applications such as hepatic steatosis stages quantification. A key challenge for clinically-relevant speed of sound estimation is to obtain repeatable values independent from superficial tissues and available in real-time. Recent works have demonstrated the feasibility to achieve quantitative estimations of the local speed of sound in layered media. However, such techniques require a high computational power and exhibit instabilities. We present a novel speed of sound estimation technique based on an angular approach of ultrasound imaging in which plane-waves are considered in transmit and in receive. This change of paradigm allows us to rely on refraction properties of plane-waves to infer local speed of sound values directly from the angular raw-data. The proposed method robustly estimates the local speed of sound with only few ultrasound emissions and with a low computational complexity which makes it compatible with real-time imaging. Simulations and in vitro experimental results show that the proposed method outperforms state-of-the-art approaches with biases and standard deviations lower than 10ms-1, 8 times less emissions and 1000 times lower computational time. Further in vivo experiments validate its performance for liver imaging.

A Hierarchical Identification Method for Lithium-Ion Battery SOC Based on the Hammerstein Model

Two-input one-output Hammerstein model consists of two parallel nonlinear static blocks followed by a linear dynamic part. By using Hammerstein structure to map relation between a battery State of Charge (SOC) and its terminal voltage/current, a hierarchical stochastic gradient algorithm is studied to estimate parameters of Hammerstein SOC model, so as to predict battery SOC. Firstly, the Hammerstein model is transformed into a bilinear parameter system with the least number of required parameters. Then, a hierarchical stochastic gradient algorithm with a forgetting factor is used to update the two sets of model parameters of the bilinear parameter system, so as to realize SOC estimation. Furthermore, the experiment platform of lithium-ion battery was built and the data of the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile were collected. Finally, the MATLAB simulation results show that the proposed parameter optimized method based Hammerstein model has the advantages of fast convergence speed and high SOC estimation accuracy.

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking.

Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this paper presents a data-driven, phase-based controller for variable-task walking that uses continuously-variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N=2) show that our data-driven controller 1) features highly-linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation

Global navigation satellite system reflectometry (GNSS-R) has shown a capability in recent years to be applied as a novel remote sensing technique to retrieve ocean wind speeds. The combination of GNSS-R observable delay-Doppler maps (DDMs) and deep learning algorithms provides the possibility to build an end-to-end pipeline for improving wind speed estimations. Recent studies have proven that data-driven approaches can be applied to generate enhanced estimation products. However, these are usually trained with convolutional neural networks (CNNs), which include inductive bias throughout the models. The inbuilt translation equivariance in CNNs represents an inexactitude for the feature extraction on DDMs. To address this issue, we propose Transformer-based models, named DDM-Former and DDM-Sequence-Former (DDM-Seq-Former), to exploit delay-Doppler correlation within and between DDMs, respectively. The advantages of our methods over conventional retrieval algorithms and other deep learning-based approaches are presented based on the Cyclone GNSS (CYGNSS) version 3.0 dataset. In addition, a comprehensive study on the attention mechanism for our models is demonstrated. The proposed DDM-Former yields the best overall performance with a root mean square error (RMSE) of 1.43m/s and a bias of −0.02m/s over the nine months test period. Moreover, with an RMSE of 2.89m/s and a bias of −1.88m/s, the proposed DDM-Seq-Former can promisingly improve the estimations in the cases with wind speeds higher than 12m/s. There are still opportunities for further enhancements in creating more robust models that could perform well in all wind regimes given a non-uniform wind distribution. We will make our code publicly available.

Electron–Ion Temperature Ratio in Astrophysical Shocks

Collisionless shock waves in supernova remnants and the solar wind heat electrons less effectively than they heat ions, as is predicted by kinetic simulations. However, the values of T e /T p inferred from the Hα profiles of supernova remnant shocks behave differently as a function of Mach number or Alfvén Mach number than what is measured in the solar wind or predicted by simulations. Here we determine T e /T p for supernova remnant shocks using Hα profiles, shock speeds from proper motions, and electron temperatures from X-ray spectra. We also improve the estimates of sound speed and Alfvén speed used to determine Mach numbers. We find that the Hα determinations are robust and that the discrepancies among supernova remnant shocks, solar wind shocks, and computer-simulated shocks remain. We discuss some possible contributing factors, including shock precursors, turbulence, and varying preshock conditions.

Evaluation of compliant walking locomotion models for civil engineering applications

This study aims to evaluate the suitability of the spring-loaded inverted pendulum (SLIP) model and three of its upgrades for modelling pedestrians in civil engineering applications. Model equations in Cartesian coordinates have been derived for the four SLIP-based models and they have been presented in ready-to-use format. The input parameter space that produces the stable periodic gait has been identified and relevant gait parameters (step frequency, average speed, and dynamic load factor) have then been evaluated against the measured parameter space characterising the pedestrian population. The results indicate that the original SLIP model has multiple drawbacks. Adding roller feet was found to increase the estimates of average walking speed when compared with the SLIP model. Adding damping brings more substantial benefits of improved coverage of the step frequency and dynamic load factor parameter space. Dimensional analysis was found to be a useful means of modelling a new pedestrian having either a new body mass or leg length. In summary, the analysis suggests that inclusion of leg damping in the model is crucial for achieving realistic estimates of the step frequency and dynamic load factor, while modelling foot geometry brings improved estimates of the walking speed.