Speed Estimation Research Articles

Rotor Speed and Position Estimation Analysis of Interior PMSM Machines in Low and Medium-High Speed Regions Adopting an Improved Flux Observer for Electric Vehicle Applications

This paper proposes a nonlinear flux linkage observer for the PMSM speed controls without motion sensors, introducing the deviation among the real stator flux linkage and an estimated stator flux linkage to suppress feedback and integral flux drift. In the position detection of an interior PMSM without a speed sensor, the traditional back EMF integration method uses a pure integrator, or LPF, to estimate the stator flux. Its inherent defects inevitably lead to inaccurate flux estimation, which directly affects the estimation of the motor mover position, resulting in the decline in motor control operation and the distortion of phase current. This paper uses an improved integrator with adaptive compensation. The projected value of the stator flux linkage has been derived from the estimated value of the rotor permanent magnetic flux linkage position angle and the algebraic model (m-model) of the stator flux linkage, along with a synchronous coordinate system. The IPMSM stator coil flux linkage obtained from the stator coil current and integral voltage models in the static coordinate system is compared to form a feedback closed-loop to suppress the integral drift, and using the cross-product approach of the actual and estimated flux linkage yields the projected value of the IPMSM rotor speed and position through a PLL. Compared with the existing motion-sensorless observers, the methodology proposed in this article is simple and exhibits better dynamic and static estimation performance. Extensive and comprehensive MATLAB computer simulation and experimental findings validate the proposed motion-sensorless control mechanism.

Availability of healthy foods, fruit and vegetable consumption, and cognition among urban older adults

Background. Although prior studies have examined the associations between neighborhood characteristics and cognitive health, little is known about whether local food environments, which are critical for individuals’ daily living, are associated with late-life cognition. Further, little is known about how local environments may shape individuals’ health-related behaviors and impact cognitive health. The aim of this study is to examine whether objective and subjective measures of healthy food availability are associated with ambulatory cognitive performance and whether behavioral and cardiovascular factors mediate these associations among urban older adults.Methods. The sample consisted of systematically recruited, community-dwelling older adults (N = 315, mean age = 77.5, range = 70–91) from the Einstein Aging Study. Objective availability of healthy foods was defined as density of healthy food stores. Subjective availability of healthy foods and fruit/vegetable consumption were assessed using self-reported questionnaires. Cognitive performance was assessed using smartphone-administered cognitive tasks that measured processing speed, short-term memory binding, and spatial working memory performance 6 times a day for 14 days.Results. Results from multilevel models showed that subjective availability of healthy foods, but not objective food environments, was associated with better processing speed (estimate= -0.176, p = .003) and more accurate memory binding performance (estimate = 0.042, p = .012). Further, 14~16% of the effects of subjective availability of healthy foods on cognition were mediated through fruit and vegetable consumption.Conclusions. Local food environments seem to be important for individuals’ dietary behavior and cognitive health. Specifically, subjective measures of food environments may better reflect individuals’ experiences regarding their local food environments not captured by objective measures. Future policy and intervention strategies will need to include both objective and subjective food environment measures in identifying impactful target for intervention and evaluating effectiveness of policy changes.

Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan

Wind energy production mainly depends on atmospheric conditions. The atmospheric stability can be described through different parameters, such as wind shear, turbulence intensity, bulk Richardson number, and the Monin–Obukhov length. Although they are frequently used in micrometeorology and the wind industry, there is no standard comparison method. This study describes the atmospheric stability of a coastal region of Yucatan, Mexico, using these four parameters. They are calculated using six-month data from a meteorological mast and a marine buoy to determine atmospheric stability conditions and compare their results. The unstable atmospheric condition was predominant at the site, with an 80% occurrence during the measurement period, followed by 12% in neutral and 6% in stable conditions. Wind speed estimations were performed for each atmospheric stability scenario, and the variation in the energy produced was derived for each case. Unstable atmospheric conditions deliver up to 8% more power than stable conditions, while neutral conditions deliver up to 9% more energy than stable conditions. Therefore, considering a neutral state may lead to a considerably biased energy production estimation. Finally, an example calculation indicates that atmospheric stability is a crucial parameter in estimating wind energy production more accurately.

Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems

Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08.

An enhanced instantaneous angular speed estimation method by multi-harmonic time–frequency realignment for wind turbine gearbox fault diagnosis

Gearboxes are one of the most critical transmission devices in wind turbines (WTs). Order tracking (OT) of variable-speed WT gear is a powerful means to carry out fault diagnosis. Since it is difficult to install a tachometer or encoder for gearboxes with limited structural space, it is impossible to obtain the instantaneous angular speed (IAS) of the gearbox. In recent years, many tacholess techniques have been presented to estimate instantaneous phase from vibration signals for OT. However, their performance needs to be further improved to get a more exact diagnosis result. To solve this issue, this paper presents an enhanced IAS estimation approach for WT tacholess order tracking (TLOT), which is available for accurate IAS estimation of the rotating shaft, so that TLOT can be more effectively conducted under conditions of large speed variation and background noise. An improved cost function algorithm is designed to extract the IAS from the vibration signal. A Vold–Kalman filter and Hilbert transform are combined to calculate the instantaneous phase. Afterwards, the raw non-stationary vibration signal is resampled at an equal angle increment, and the envelope order spectrum is calculated from the resampled signal for fault diagnosis of the WT gearbox. The main innovating idea of this approach is to improve the traditional cost function ridge-extraction method by introducing gradient information based on probability and multi-harmonic time–frequency realignment, which makes the IAS estimation process more accurate and overcomes the limitations of traditional IAS estimation techniques. The effectiveness of the presented approach is demonstrated by using a vibration signal measured from real-world WT gearboxes, and the validation results demonstrate that the presented approach surpasses some state-of-the-art methods in IAS estimation accuracy and efficiency.

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

AbstractWith the increasing availability of location‐aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona‐based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24‐h period.

Sensorless Adaptive control of VSI-Fed Induction Motor Drive with Optimized MLP-Neural Network

ABSTRACT Multilayer-Perceptron Neural Network (MLP-NN)-based sensorless speed control of adaptive Indirect Field-Oriented Control (IFOC) strategy is implemented for online parameter estimation of Induction Motor Drive (IMD) fed from Common mode voltage injection Space vector PWM (CVSVPWM) based Voltage Source Inverter. Harris Hawks Optimization (HHO) is implemented in this work, to train the MLP-NN model by choosing the optimal weight and biases for the estimation of accurate parameters and speed of IMD. The objective of optimal MLP-NN is to improve the IMD reliability and response fast during dynamic operation. The model performances are evaluated by employing statistical metrics of MSE, RMSE, MAE, MAPE, and R for training and testing. These are reported for testing to be 0.000602064, 0.0245, 0.4015, 0.25474, and 0.9997 which indicates the best-fitted prediction model and proves the minimized error. The results reveal that an optimized MLP-NN accomplishes promising performance in estimating the parameters and speed with the least errors such as rs is 3.82%, rr is 4.19%, ls is 0.41%, lr is 0.72%, lm is 0.21%, and strongly tracking of reference speed. In addition, HHO is also employed to evolve the gains of the PI-controller in adaptive-IFOC for generation of reference signals by reducing the computational effort.

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Discrete Sliding-Mode-Based MRAS for Speed-Sensorless Induction Motor Drives in the High-Speed Range

By combining the high robustness of sliding-mode observer (SMO) with the adaptability of model reference adaptive system (MRAS), the sliding-mode-based MRAS can improve the speed estimation performance of speed-sensorless induction motor (IM) drives. However, the analysis of this paper shows that there exists a mismatch problem between the fixed-value of SMO gains and the variable stability boundary which changes with IM operation speed, resulting in speed estimation performance degradation or even instability in high-speed range. To cope with this problem, a discrete SMO is proposed to expand the effective operation range of sliding-mode-based MRAS. First, a discrete terminal SMO with modified observer structure is designed for rotor flux estimation. Then, a variable gain is designed to ensure the SMO stability in high-speed range, and avoid the chattering caused by gain value overestimation. Compared with existing sliding-mode-based MRAS, the studied method can improve the stability and accuracy of speed estimation in high-speed range. Finally, the experimental results from a 3.7kW IM test bench can confirm the validness of studied speed estimation scheme.

Adaptive Iterative Learning Kalman Consensus Filtering for High-Speed Train Identification and Estimation

In this study, a data-driven adaptive iterative learning Kalman consensus filtering (DD-AILKCF) method is designed for high-speed trains to address the parameter identification and speed consistent optimal estimation problem. The nonlinear train dynamics model is transformed into a linear-like state-space model by using the Full Form Dynamic Linearization (FFDL) technique. Meanwhile, four types of sensors are used to obtain different kinds of datasets to implement the multi-sensor system. The method proposed in this paper consists of two steps. First, an adaptive iterative learning Kalman filtering (AILKF) algorithm is proposed to estimate the fast-time varying train parameter in the iteration domain. Then, based on the identified parameter, a distributed multi-source heterogeneous network consensus filtering (MHN-CF) algorithm is proposed for the speed estimation of high-speed trains. The convergence of the proposed algorithm is derived based on the Lyapunov Function. The proposed method is compared with existing methods by numerical simulations, and the results indicate that the proposed method achieves good effectiveness in improving the accuracy of high-speed train speed estimation.

Wind and Turbulence Observations With the Mars Microphone on Perseverance

AbstractWe utilize SuperCam's Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. To do so, we first demonstrate the sensitivity of the microphone signal level to wind speed, yielding a power law dependence. We then show the relationship between the microphone signal level and pressure, air and ground temperatures. A calibration function is constructed using Gaussian process regression (a machine learning technique) taking the microphone signal and air temperature as inputs to produce an estimate of the wind speed. This provides a high rate wind speed estimate on Mars, with a sample every 0.01 s. As a result, we determine the fast fluctuations of the wind at Jezero crater which highlights the nature of wind gusts over the Martian day. To analyze the turbulent behavior of this wind speed estimate, we calculate its normalized standard deviation, known as gustiness. To characterize the behavior of this high frequency turbulent intensity at Jezero crater, correlations are shown between the evaluated gustiness statistic and pressure drop rates/sizes, temperature and energy fluxes. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales.

Feedback Loop Adaptation Speed Feedback Systems for PMSMs via Angle Filtering and Acceleration Stabilization Techniques

This article presents an advanced solution to the speed tracking problem of permanent magnet synchronous motors (PMSMs) subject to two practical concerns: uncertain motor parameter values, load variations, and current feedback dependence level. The results present a few contributions for industrial application: (a) a parameter-independent angle filter (providing speed and acceleration estimates) by modifying the structure of the conventional extended state observer, (b) the angle filter-based pole-zero cancellation acceleration stabilizer removing the <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-axis current dependence of the speed loop to secure the current sensor fault tolerance, and (c) the adaptive speed loop in the analytic form to improve the tracking performance by boosting feedback gain during only the transient periods. Using a dynamo testbed adopting a 700-W PMSM as the test motor, this experimental study verifies the effectiveness of the proposed technique under various operating modes.

A workflow for including atmospheric stability effects in wind resource and yield assessment and its evaluation against wind measurements and SCADA

Atmospheric stability conditions are known to impact the wind resource and yield assessments. However, they are too barely or not correctly taken into account in the industry due to several reasons such as limitations of commercial software or the relative inertia in updating industrial processes. This paper proposes a simplistic approach to improve wind resource and yield assessment certainty while keeping very similar software and industry processes. Two test cases are considered. First, the wind speed estimates made using a CFD software (Meteodyn 5.3) for different stability classes are compared to measurements obtained on a site with four met masts. Second, the wake losses obtained with a commercial yield assessment solver (WindPRO 3.5) considering different wake decay constant definitions are compared to SCADA data. In both cases, it appears that dividing the timeseries in “stability classes” and using corresponding stability parameters in the CFD and in the wake model parameters enable a reduction of the uncertainty. In the case of the energy estimation compared to SCADA, an improvement of 1.25% is obtained compared to the conventional approach.

Evaluation of Start-Up Performance of Induction Motor Speed Sensorless Vector Control Using Adaptive Flux Observer Considering Inverter Voltage Error

Speed sensorless vector control for induction motors using an adaptive flux observer makes it difficult to output high and stable torque at a standstill condition when the output voltage error mainly due to dead-time of the inverter. This voltage error leads to a speed estimation error. Consequently, axis misalignment occurs, and a torque value that tracks the reference command value cannot be output. A method has been proposed to compensate for this voltage error by using a current command value; however, the compensation accuracy is limited because of the forward voltage drop and nonlinear characteristics of power switching devices. This study applies the conventional dead-time compensation to the model reference adaptive system, and the effect of varying the amount of compensation, instead of investigating the true dead-time length, on the start-up is evaluated using a 2.2 kW experimental system and through dynamic analysis.

YOLO 및 DeepSORT를 활용한 학교안전보행 모니터링 시스템 개발

In order to prevent safety accidents while walking in schools, a monitoring system using object recognition technology was developed for corridors and stairs. Using a fixed webcam and computer, the function of detecting and tracking the human body in real time and calculating the movement speed of the student was implemented. The object detection model was trained using COCO dataset and YOLOv7 network training architecture. Using the object tracking model and Python, it created an object speed estimation system through real-time video analysis. A custom model for human body detection was developed, and the accuracy of the model was mAP_0.5: 0.7387, mAP_0.5_0.95: 0.4314.

Experimental and numerical comparison of currently available reaction mechanisms for laminar flame speed in 70/30 (%vol.) NH3/H2 flames

To achieve net zero carbon emissions, ammonia is gaining traction as a promising alternative fuel. However, the combustion characteristics of ammonia need further investigation. The current study aims to analyze the laminar flame speed, a fundamental physio-chemical property of any combustible mixture, through experimental measurements and kinetic reaction mechanism analysis. The laminar flame speed of 70/30 (%vol) NH3/H2 at atmospheric pressure and ambient temperature across a wide range of equivalence ratios (0.6–1.4) was studied experimentally and compared to the performance of 36 kinetic reaction mechanisms to appraise their performance concerning laminar flame speed prediction for the measured NH3/H2 mixture. The absolute percentage error (APE) formula has been adopted for preliminary estimation based on the experimental measurements of the present study and numerical data. The study found that Duynslaegher et al. 2012 model shows good performance speed across lean and stoichiometry conditions with an APE value between 0%-6%. The mechanism of Nakamura et al., 2017 and Gotama et al., 2022 demonstrates a good estimation of laminar flame speed under rich conditions. The sensitivity analysis revealed that the reactions H+O2=O+OH, NH2+NH2=N2H2+H2, and OH+H2=H+H2O are the most crucial reaction with considerable effect in promoting the laminar flame speed at all conditions, while the reactions of H+O2(+M)=HO2+M, NH2+H=NH+H2, and NH2+O=HNO+H play an essential role in the retardation of laminar flame speed at all conditions. The effect of the aforementioned reactions varies for the equivalence ratio, mainly due to changes in adiabatic flame temperature.

The dynamics of traveling wavefronts for a model describing host tissue degradation by bacteria

In this paper, we mainly investigate the dynamics of traveling wavefronts for a model describing host tissue degradation by bacteria. We first establish the existence of spreading speed, and show that the spreading speed coincides with the minimal wave speed of traveling wavefronts. Moreover, a lower bound estimate of the spreading speed is given. Then, we prove that the traveling wavefronts with large speeds are globally exponentially stable, when the initial perturbation around the traveling wavefronts decays exponentially as [Formula: see text], but the initial perturbation can be arbitrarily large in other locations. The adopted methods are the weighted energy and the squeezing technique.

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

Implementation of hybrid wind speed prediction model based on different data mining and signal processing approaches

Accurate estimation of wind speed (WS) data, which greatly influences meteorological parameters, plays a vital role in the safe operation and optimization of the power system and water resource management. The study's main aim is to combine artificial intelligence and signal decomposition techniques to improve WS prediction accuracy. Feed-forward back propagation neural network (FFBNN), support vector machine (SVM) and Gaussian processes regression (GPR) models, discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were used to forecast the WS values ​​1 month ahead in Burdur meteorology station. Statistical criteria such as Willmott's index of agreement, mean bias error, mean squared error, determination coefficient, Taylor diagram and regression analysis, and graphical indicators were used to evaluate the prediction success of the models. As a result of the study, it was determined that both wavelet transform and EMD signal processing increased the WS prediction performance of the stand-alone ML model. The best performance was obtained with the hybrid EMD-Matern 5/2 kernel GPR with test (R2:0.802) and validation (R2:0.606). The most successful model structure was obtained using input variables with a delay of up to 3 months. The study's results contribute to wind energy-related institutions in terms of practical use, planning, and management of wind energy.

Slope synchronous chirplet transform and its application to tacho-less order tracking of rotating machineries

Chirplet transform (CT) has been widely used in time–frequency analysis (TFA) of rotating machinery vibration signals. However, due to the smear effect and time–frequency resolution limitation, CT and most of its improved algorithms cannot achieve sufficient refined time–frequency representation (TFR) for the instantaneous frequency (IF) close-spaced and IF trajectory slope mutation signals. Aiming at the aforementioned shortcomings, a novel TFA method named slope synchronous chirplet transform (SSCT) is proposed to address this problem. The proposed SSCT algorithm can adaptively adjust the window lengths according to the slope variation of the IF trajectory, thereby higher time–frequency resolution can be obtained for the IF close-spaced components. Besides, the proposed method utilizes a high-order phase operator to accurately capture the trend of IF trajectory slope, so that the time–frequency (TF) energy of the IF trajectory can be better concentrated, thereby the weak TF energy of the IF trajectory can be uncovered. The effectiveness of the proposed method is verified by simulated signals and an aero-engine vibration signal measured under a transient operating mode, the validation results indicate the proposed method outperforms some latest reported TFA algorithms and is available for accurate estimation of instantaneous angular speed for tacho-less order tracking of rotating machineries.