Speed Estimation Accuracy Research Articles

Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment

Abstract. Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS120), 160 m (WS160), and 200 m (WS200). These heights go beyond the traditional wind mast limit of 100–120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS120, WS160, and WS200. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient R of 0.93 and a smaller RMSE of 1.09 m s−1, compared with the R of 0.89 and RMSE of 1.50 m s−1 for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS120, WS160, and WS200 from RF are then analyzed. The hourly WS120 is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS120 is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS160 and WS200 are similar to those of WS120. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.

An optimized sliding‐mode observer control algorithm for vehicle sensor‐less PMSM

AbstractAn optimized sliding‐mode observer control algorithm for permanent magnet synchronous motor (PMSM) is proposed to solve the problems in the conventional sliding‐mode observer control algorithms (e.g., system chattering and phase delay). The proposed algorithm adopts a new piecewise function as a switching function to reduce system chattering. The low pass filter in the conventional sliding‐mode observer is substituted with a complex coefficient filter in the proposed algorithm to solve the phase delay caused by the low pass filter. Moreover, the counter emf back obtained using the filtering of the complex coefficient filter is reintroduced into the sliding‐mode observer to decrease the sliding‐mode gain coefficient and reduce chattering in the system. the simulation results suggest that the proposed algorithm outperforms the conventional sliding‐mode observer control algorithm in reducing chattering, increasing the convergence speed, reducing torque pulsation, increasing speed estimation accuracy and coping with speed and load mutation.

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates.

Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicycle. Balance control has both a physics (mechanics) and a neurobiological component. The physics component pertains to the laws that govern the movements of the rider and his bicycle, and the neurobiological component pertains to the mechanisms via which the central nervous system (CNS) uses these laws for balance control. This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC). The central concept in this model is a computational system, implemented in the CNS, that controls a mechanical system outside the CNS. This computational system uses an internal model to calculate optimal control actions as specified by the theory of stochastic OFC. For the computational model to be plausible, it must be robust to at least two inevitable inaccuracies: (1) model parameters that the CNS learns slowly from interactions with the CNS-attached body and bicycle (i.e., the internal noise covariance matrices), and (2) model parameters that depend on unreliable sensory input (i.e., movement speed). By means of simulations, I demonstrate that this model can balance a bicycle under realistic conditions and is robust to inaccuracies in the learned sensorimotor noise characteristics. However, the model is not robust to inaccuracies in the movement speed estimates. This has important implications for the plausibility of stochastic OFC as a model for motor control.

Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management

Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring.

A New SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Low Speed

AbstractA novel sliding mode observer for speed estimation in sensorless induction motor drives at zero and low speed is proposed in this paper. First, aiming at the poor stability when the induction motor runs at low speed, analyze the reasons of the poor stability and a new feedback gains design method is proposed. Second, for the problem of ignored flux error in speed estimation which cause speed error at low speed, a new compensation method is put forward, which can improve the accuracy of speed estimation in the dynamic and steady state of the system. Meanwhile, a continuous function is given which can replace sign, to restrain the chattering problem of sliding mode observer. Detailed simulation results are given showing the operation at zero and low speed, the results prove the effectiveness of the proposed sliding mode observer. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

Accurate Estimation of Effective Wind Speed for Wind Turbine Control Using Linear and Nonlinear Kalman Filters

단축 가속도계의 Pseudo 바이어스 모델을 이용한 필터 기반 차량 속력 추정 알고리즘

This study presents a filter-based vehicle longitudinal speed estimation algorithm with improved accuracy by estimating the accelerometer bias using only a single-axis accelerometer in the section where the odometer reliability is low. Most vehicles use odometers to obtain speed information because odometers can measure speed with high accuracy under normal driving conditions. However, since the typical odometer has a characteristic that depends on the measured revolutions per minute of the vehicle wheel, if a slip situation occurs in which the wheel slides owing to a sudden start and sudden stop, then the odometer outputs an inaccurate speed measurement. To solve this problem, we redefine the sensor error model of the accelerometer attached to the vehicle and propose a method to estimate the attitude and self-bias component, which are error components that have large effects on the accelerometer, through an adaptive lowpass filter. In addition, we propose an approach to apply the zero-velocity update method to improve the accuracy of the estimated speed information by integrating the error-compensated acceleration value based on the filter. To evaluate the performance of the proposed algorithm, an actual vehicle experiment was performed, and the speed estimation results of the odometer disabled area were analyzed. The results confirmed that the accuracy of speed estimation was greatly improved.

Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge

In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspects of FORNN can be improved by learning certain physics-informed knowledge. Firstly, the fractional-order state feedback is achieved by introducing a fractional-order derivative in a forward propagation process. Secondly, the fractional-order constraint is constructed by a voltage partial derivative equation (PDE) deduced from the battery fractional-order model (FOM). Thirdly, both the fractional-order gradient descent (FOGD) and fractional-order gradient descent with momentum (FOGDm) methods are proposed by introducing a fractional-order gradient in the backpropagation process. For the proposed FORNN, the sensitivity of the added fractional-order parameters are analyzed by experiments under the federal urban driving schedule (FUDS) operation conditions. The experiment results demonstrate that a certain range of every fractional-order parameter can achieve better convergence speed and higher estimation accuracy. On the basis of the sensitivity analysis, the fractional-order parameter tuning rules have been concluded and listed in the discussion part to provide useful references to the parameter tuning of the proposed algorithm.

Vortex-electromagnetic-wave-based ISAR imaging for high-speed maneuvering targets

Vortex electromagnetic wave (VEMW) carrying orbital angular momentum (OAM), which is expected to introduce additional degrees of freedom in inverse synthetic aperture radar(ISAR) imaging. However, the current research about maneuvering targets is based on the "stop go" hypothesis, which does not apply to high-speed motion scenarios due to the intrapulse movement of the target. To improve the imaging quality, this letter proposes a VEMW-based high-speed maneuvering targets imaging method. Firstly, the ISAR imaging scenario of high-speed target is established. According to the spatial geometric relationship between radar and maneuvering target, the vortex echo is deduced and its characteristics are analyzed. Subsequently, a frequency modulation rate estimation method considering both calculation efficiency and high precision is proposed to realize the accurate estimation of target speed. Then, an adaptive azimuth image compensation method based on minimum entropy is proposed. Through the setting of threshold, the number of component signals in linear frequency modulation (LFM) signal is determined and compensated successively. Finally, the range profile and azimuth profile are combined to reconstruct the three-dimensional information. The simulation results demonstrate that this work can effectively eliminate the influence of high-speed motion on range and azimuth profile, also benefit the development of ISAR imaging technique of high-speed maneuvering targets.

Application of Microwave Space-Based Environmental Monitoring (SBEM) Data for Operational Tropical Cyclone Intensity Estimation at the Joint Typhoon Warning Center

Abstract The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) radiometers, and synthetic aperture radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the poststorm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the poststorm BT process, and provide examples of how these data influenced forecaster decision-making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new datasets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data-sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.

Speed Estimation of PMSM Based on a Super-Twisting Slide Mode Observer

To limit the high-frequency chattering originating from the conventional sliding mode observer in a PMSM sensorless control system of vacuum pumps and reduce the steady-state error caused by the phase lag of the coordinate system on the estimated speed, a speed estimation algorithm was proposed based on the super-twisting sliding mode observer. Then, the proposed algorithm was applied to observe an electromotive force. To this end, the Lyapunov function was established to analyze the stability of the current dynamic error equation. In this regard, the estimated phase lag of the rotating coordinate system was considered to study the influence of the speed estimation error. Moreover, a speed estimation algorithm considering the d-axis estimation of back electromotive force is proposed. The performed analyses show that the speed estimation algorithm based on a super-twisting sliding mode observer has promising estimation accuracy. It is concluded that the proposed algorithm can be effectively applied to suppress high-frequency chattering in the system, reduce the influence of phase lag in the estimation coordinate system on the speed estimation error, remove the low-pass filter and phase compensation module, and improve the accuracy of speed estimation.

Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 – 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Spectral estimator effects on accuracy of speed-over-ground radar

<p>Spectral estimation is a critical signal processing step in speed-over-ground (SoG) radar. It is argued that, for accurate speed estimation, spectral estimation should use low bias and variance estimator. However, there is no evaluation on spectral estimation techniques in terms of estimating mean Doppler frequency to date. In this paper, we evaluate two common spectral estimation techniques, namely periodogram based on Fourier transformation and the autoregressive (AR) based on burg algorithm. These spectral estimators are evaluated in terms of their bias and variance in estimating a mean frequency. For this purpose, the spectral estimators are evaluated with different Doppler signals that varied in mean frequency and signal-to-noise ratio (SNR). Results in this study indicates that the periodogram method performs well in most of the tests while the AR method did not perform as well as these but offered a slight improvement over the periodogram in terms of variance.</p>

Passive detection in water pipelines using ambient noise II: Field experiments

A passive detection method has been proposed in a prior paper to extract key parameters and detect faults using the ambient noise present in water pipeline networks. This paper presents field experiments and data processing results to provide systematic experimental validation of this method. Field experiments were carried out in operational water pipeline networks at the University of Canterbury campus and the Waimakariri District, New Zealand, during which ambient noise was measured by pairs of pressure sensors installed at selected hydrants on pipelines of different materials, network topologies and simulated faults. Auto-correlation and cross-correlation analysis of noise at a single sensor and sensor pairs were carried out to estimate the wave speed and to locate faults in the networks. Data processing results indicate that water usage generating pressure transients are the dominant sources of ambient noise in operational water pipeline networks. This type of ambient noise can also be utilized by the passive detection method to achieve similar wave speed estimation accuracy and fault detection performance as the conventional active pressure wave detection methods.

Induction Motor Speed Estimation Based on Airgap Flux Measurement Using Hilbert Transform and Fast Fourier Transform

This paper proposes the rotor speed estimation of a squirrel cage induction motor fed by a sinusoidal power supply or an inverter based on the use of a Hall effect sensor installed between two stator slots of the machine. As well known in literature, inverter-fed induction motors usually contains a series of current harmonics and in case of rotor faults, such as broken bars, this operational condition becomes even more severe. The speed estimation has been extensively investigated for its use in control schemes, to compute output power or machine efficiency and particularly for fault diagnosis using motor current signature analysis. In this method, it is necessary an accurate speed estimation to prevent false positives and false negative indications. Therefore, the present research has been carried out by applying the Fast Fourier Transform and Hilbert Transform in the airgap signal measured by the Hall sensor, in order to estimate the rotational speed of the induction motor in a healthy condition and with broken rotor bars. This approach has been compared to the same signal processing techniques using the stator current phase, usually used for speed estimation in the state of the art solutions. The efficiency of this approach was evaluated from simulation and experimental tests, for motor running at distinct load torques, at very low slip conditions and considering a damaged rotor cage. The simulation and experimental results have shown a better accuracy with the measured or simulated speeds, using the airgap estimations when compared to those obtained from stator current processing.

Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm

A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto Sparrow Search Algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers’ location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers’ position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.

Extremely low speed performance research of the speed sensorless vector controlled induction motor drive system

This paper proposes a new full-order adaptive observer algorithm to overcome the instability of speed sensorless vector control system of induction motor at low speed, especially at extremely low speed. The algorithm consists of the improved feedback matrix algorithm and the improved speed adaptive law algorithm. By establishing an observer state error equation considering the non-linear voltage error of the inverter and the stator resistance voltage drop, a feedback matrix was designed to reduce the effect of inverter's non-linear voltage error and stator resistance voltage drop on speed estimation. Meanwhile, this paper improves the traditional adaptive law. The basic idea is to add compensation amount equivalent to the non-linear voltage error, and the compensation amount is adaptive to the running speed and load to improve the motor speed estimation accuracy at low speed. Extensive experimental tests have been carried out to evaluate the performance of the proposed algorithm using a 2.2 kW induction motor experimental platform. The experimental results show that the algorithm presented in this paper can guarantee the stable operation of the motor under the rated-load and 180% rated-load at low, extremely low and 0 speed with good dynamic and static characteristics.

Real-Time Multitarget Tracking for Panoramic Video Based on Dual Neural Networks for Multisensor Information Fusion

A multitarget real-time tracking system for panoramic video with multisensor information fusion dual neural networks is studied and implemented by combining dual neural networks, fused geometric features, and deep learning-based target real-time tracking algorithms. The motion model of multisensor information fusion is analyzed, the dual neural network perturbation model is introduced, and the state variables of the system are determined. Combined with the data structure model of multisensor information fusion, a least-squares-based optimization function is constructed. In this optimization function, the image sensor is constructed as an observationally constrained residual term based on the pixel plane, the inertial measurement unit is constructed as an observationally constrained residual term based on the motion model, and the motion relationship between sensors is constructed as a motion-constrained residual term to derive a mathematical model for real-time multitarget tracking of panoramic video. The simulation experimental results show that the integrated position estimation accuracy of the multisensor information fusion dual neural network-based panoramic video multitarget real-time tracking fusion algorithm is 10.5% higher than that of the optimal distributed panoramic video multitarget fusion algorithm without feedback. The results of the simulation experiments show that the integrated position estimation accuracy of the multitarget real-time tracking fusion algorithm for panoramic video improves by 10.96%, and the integrated speed estimation accuracy improves by 6.32% compared with the optimal distributed multitarget fusion algorithm without feedback. The proposed panoramic video multitarget real-time tracking algorithm based on the dual neural network can effectively improve the target tracking accuracy of the model on degraded frames (motion blur, target occlusion, out-of-focus, etc.), and the stability of the algorithm for target location and category detection is effectively improved by multiframe feature fusion. The research in this paper provides better technical support and research theory for panoramic video multitarget real-time tracking.

Finite-Time Extended State Observer-Based Super-Twisting Sliding Mode Controller for PMSM Drives With Inertia Identification

This article designs a novel finite-time extended state observer (FTESO)-based super-twisting sliding mode (STSM) control scheme to promote the anti-disturbance property and tracking performance of permanent magnet synchronous motor (PMSM). First, a FTESO combined with generalized super-twisting technique is constructed to estimate the unknown lumped disturbance and compensate the estimated value to the STSM speed controller. Compared with the conventional extended state observer (ESO), the proposed FTESO can make the estimation error converge to the origin in finite time, which provides a faster convergence speed and higher estimation accuracy. Moreover, to tackle the problem of performance deterioration caused by inertia mismatch, particularly for PMSM speed control, an inertia identification method is employed to calculate the true value of inertia online to tune the parameters of FTESO and STSM controller automatically. Comprehensive simulation and experimental results demonstrate that the proposed control method can improve the robustness of PMSM system in the presence of load torque and inertia variations.