Speed Fluctuations Research Articles

Humans plan for the near future to walk economically on uneven terrain

Humans experience small fluctuations in their gait when walking on uneven terrain. Thefluctuations deviate from the steady, energy-minimizing pattern for level walking and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serves some an objective purpose, such as maintaining constant speed or reducing energy expenditure, that is also attainable within finite planning capacity. Here, we show that humans do plan and perform optimal control strategies on uneven terrain. Rather than maintaining constant speed, they make purposeful, anticipatory speed adjustments that are consistent with minimizing energy expenditure. A simple optimal control model predicts economical speed fluctuations that agree well with experiments with humans (N= 12) walking on seven different terrain profiles (correlated with model[Formula: see text] , [Formula: see text] all terrains). Participants made repeatable speed fluctuations starting about six to eight steps ahead of each terrain feature (up to ±7.5cm height difference each step, up to 16 consecutive features). Nearer features matter more, because energy is dissipated with each succeeding step's collisionwith ground, preventing momentum from persisting indefinitely. A finite horizon of continuous look-ahead and motor working space thus suffice to practically optimize for any length of terrain. Humans reason about walking in the near future to plan complex optimal control sequences.

Sensorless Control Based on the Uncertainty and Disturbance Estimator for IPMSMs With Periodic Loads

The research object in this paper is a single rotor compressor based on the interior permanent magnet synchronous motors (IPMSM) with sensorless control, whose load fluctuates periodically with the mechanical angle. The periodically fluctuating loads can cause obvious periodic fluctuations of motor speed. Meanwhile, the traditional phase-locked loop (PLL) can lead to obvious phase shift and amplitude attenuation. Therefore, the controller is designed from two aspects to make the motor run stably at low speed under the periodic load. Firstly, a speed control based on the uncertainty and disturbance estimator (UDE) is proposed to suppress speed fluctuations caused by the periodic load. Secondly, the phase-locked loop based on the resonance control (R-PLL) is proposed to compensate for the periodic angle and speed errors caused by the phase shift coming from the PLL under the periodic load. The experimental results show that the sensorless control based on the R-PLL and UDE can effectively improve the estimated angle accuracy and suppress the speed fluctuations, making the motor run stably at low speed under periodic loads.

Distribution Characteristics of Wind Speed Relative Volatility and Its Influence on Output Power

The stochastic fluctuations of wind speed and wind power curve modeling are complex tasks due to fluctuations in the difference between actual and theoretical power output, leading to a reduction in the accuracy of wind-power curve models. To address this issue, this paper proposes a normal distribution-modeling method based on relative volatility, which extracts the wind-speed variation patterns from the onsite SCADA (Supervisory Control And Data Acquisition) data, analyzes the correlation between wind-speed relative volatility and power relative volatility, and establishes a wind-power volatility-curve model to provide a basis for evaluating the efficiency of wind turbines. First, the definitions of relative volatility and probability vectors are provided, and a probability vector volatility-assessment function is designed to calculate the volatility-assessment index of the probability vector. Then, the relative volatility and probability vectors of wind speed are modeled, and features extracted from the onsite SCADA data, and characteristic parameters such as mean, standard deviation, and confidence interval of wind-speed relative volatility are statistically analyzed, as well as the wide-window coefficient, volatility-assessment index, attribute features (volatility center and volatility boundary), normal distribution features (mean and standard deviation) of the probability vectors of wind-speed relative volatility with different periods. The visualization descriptions of six typical probability vector distributions show that there is a correlation between the volatility assessment index of the probability vector based on relative volatility and the standard deviation of its distribution. Finally, the correlation between wind-speed relative volatility and power relative volatility is analyzed: in the maximum wind-energy tracking area, the derivative of power is linearly related to the derivative of wind speed, while in the constant power area, the derivative of the wind-energy utilization coefficient is linearly related to the derivative of wind speed. The conclusions obtained in this paper will provide a method reference for data processing to mine the parameter variation patterns and interrelationships of wind farm SCADA data and provide a basis for evaluating the power generation efficiency of wind turbines.

On the impact of waves and turbulence on the power fluctuations and wake structure of a tidal-stream turbine

Anticipating the power output of tidal-stream turbines (TSTs) is relevant for its technological advance and success. Therefore, it is important to clarify the behavior of TSTs under tidal-stream environments, which include both waves and currents. In this study, a scale-model horizontal-axis TST was examined in turbulent conditions and in the presence of waves. The impact of waves on the wake structure and output power fluctuations were analyzed in terms of integral length scales, the turbine's power statistics, and spectral density. Due to wave–current interactions, the distribution of integral length scales is asymmetric at downstream positions behind the rotor's lateral tips. The fluctuations of the wave speed in the wake lead to a homogenized but reduced length scale. In the spectral domain, waves mix with the turbulent cascade, effectively reducing the energy decay from f 0 to f−1 in the low-frequency region and from f−5/3 to f−11/3 in the intermediate-decay region. The bandwidth of the mechanical noise in the high-frequency range, along with the periodic components, intensifies with increasing wave height and length. Although they are preliminary, these findings will prove useful for understanding the disruption of turbine flow and power output in wave–current flows. This is a first step toward assessing the damage caused to ecology and the structural parts of TSTs in real-world conditions.

Observations of the space/time scales of Beaufort sea acoustic duct variability and their impact on transmission loss via the mode interaction parameter.

The Beaufort duct (BD) is a subsurface sound channel in the western Arctic Ocean formed by cold Pacific Winter Water (PWW) sandwiched between warmer Pacific Summer Water (PSW) and Atlantic Water (AW). Sound waves can be trapped in this duct and travel long distances without experiencing lossy surface/ice interactions. This study analyzes BD vertical and temporal variability using moored oceanographic measurements from two yearlong acoustic transmission experiments (2016-2017 and 2019-2020). The focus is on BD normal mode propagation through observed ocean features, such as eddies and spicy intrusions, where direct numerical simulations and the mode interaction parameter (MIP) are used to quantify ducted mode coupling strength. The observations show strong PSW sound speed variability, weak variability in the PWW, and moderate variability in the AW, with typical time scales from days to weeks. For several hundreds Hertz propagation, the BD modes are relatively stable, except for rare episodes of strong sound speed perturbations. The MIP identifies a resonance condition such that the likelihood of coupling is greatest when there is significant sound speed variability in the horizontal wave number band 1/11<kh<1/5 km-1. MITgcm ocean model results are used to estimate sound speed fluctuations in this resonance regime.

Performance Evaluation Of Permanent Magnet Synchronous Generator (PMSG) On Tidal Power Generation Optimization

In a tidal current energy generation system, optimization of power generation can be done through turbine design, system design, and control of mechanical transmission from the turbine to the generator, as well as from the aspect of electronic generator output control. This research aims to analyze the performance of the PMSG generator from a prototype of the tidal current energy power plant. Turbine rotation is simulated using an ac motor equipped with an ac drive that supports separate speed and torque control. The hydraulic transmission system consists of a pump and a hydraulic motor that transfers the rotation of the turbine to the generator, where the power output is observed with variations in turbine speed and torque. The results indicate that the transmission system has worked well in transmitting the turbine’s mechanical power and increasing the rotational speed. The decrease in speed with increasing load and low average efficiency (less than 20%) occurs, mainly due to the characteristics of the generator being tested. The generator has a large rated torque requirement of 32 Nm, which is much higher than the main drive capacity of the turbine simulator. The design of a tidal energy conversion system, in which the tidal current speed fluctuates, requires careful selection of the generator, not only from the aspect of power capacity and rated rotational speed, but also from the initial torque value and required rated torque.

Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations.

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.

Development of the method of constructing the expander turbine rotation speed regulator

The study is devoted to the expander turbine rotation speed regulator, considering the possibility of implementing this regulator on microprocessor automation tools. The use of expander-generator units in general improves energy saving indicators, and the ability to maintain the turbine shaft rotation speed within the specified limits, in turn, directly affects the indicators of the quality of the generated electricity. The expander turbine, as a control object, is described by non-linear equations, which determines the possibility of using regulators of different designs, and requires the selection of the most suitable one according to certain criteria. As part of the study, based on the tasks of practical implementation of the regulator on microprocessor devices, the expediency of reducing the transfer function of the model in the process of identifying the control object was confirmed. As a result of research on an experimental setup, it is shown that the use of a three-position relay regulator allows for regulation dynamics at the level of a classic PID regulator. An important result of the research is the stabilization of the turbine rotation speed, which affects the parameters of the electricity generated by the generator. The description of the control object was linearized by constructing a family of transfer functions for the operating points of the control range. For the construction of the turbine rotation speed regulator, the criterion of "minimum fluctuation of the parameter when changing its set value" is proposed. A regulator for a non-linear object with oscillatory features is built, which has a simple implementation and a cycle time of 1 ms. It makes it possible to reduce rotation speed fluctuations to 5 % and minimize the impact of rotation process disturbances

ASMADO‐based refined anti‐disturbance low‐speed control of PMSM

AbstractPermanent magnet synchronous motors (PMSMs) have expectant low‐speed servo performance. However, complex nonlinear disturbances restrict the performance and lead to speed fluctuations, especially for small inertia PMSMs where the current‐loop cannot be used in special operating conditions. This paper divides complex disturbances into periodic determinable disturbances and other indeterminate disturbances according to their characteristics. A refined anti‐disturbance control (RADC) method is proposed to enable the inner loop to compensate for disturbances in targeted manners. Then ideal low‐speed servo can be achieved using a simple outer loop controller. The proposed RADC consists of two parts. One is a backpropagation neural network based periodic disturbances compensator that is trained using the data from an iterative learning controller. The other is an adaptive sliding‐mode‐assisted disturbance observer that rapidly observes and compensates the residual disturbances. The convergence of the overall algorithm is analyzed. The effectiveness of the proposal is also verified by experiments.

Combined forecasting tool for renewable energy management in sustainable supply chains

Effective managing sustainable supply chain is on the most cutting-edge position of various organizations in the process of delivering products. There are many optimization technologies that have been applied in sustainable supply chain management, aiming to decrease the carbon emission level of production process. Among these methods, designing reliable renewable energy forecasting approaches is conductive to power management and optimization in sustainable and circular supply chain. However, irregular and non-stationary fluctuations of wind speed is a major obstacle to optimize the applications of renewable energy in sustainable supply chain management. There are various approaches for wind speed prediction, yet most of them ignore the significance of hyperparametric selection and combined forecasting strategy, resulting in unsatisfactory prediction results. To remedy the drawbacks, a combined prediction framework is proposed, which uses grid search to select suited hyperparameters of sub-models and employs an improved intelligent optimization algorithm (ranking-based adaptive cuckoo search algorithm) to calculate the optimal weighting coefficient of sub-models. Nine datasets are collected to validate the proposed model and 15 benchmark models. The simulations revel that the proposed model yields the satisfactory prediction level, which precedes comparative models based on statistical test results. Hence, it is a valuable tool for decision makers to provide key reference information in sustainable supply chain management and optimization.

A Novel Method for Estimating the Position and Speed of a Winding Segmented Permanent Magnet Linear Motor

This paper presents a novel means of sensorless drive of the winding segmented permanent magnet linear motor (WS-PMLM). In order to solve the problem of the deviation of the position and speed estimation of the mover caused by the sharp changes of the inductance, flux linkage, and back electromotive force (EMF) between the segments of the WS-PMLM, a method for position estimation by the compound back-EMF is proposed to eliminate the blind spot of intersegment position estimation. In terms of speed estimation, a full-order speed observer method based on the dynamics equation is proposed, the configuration of the feedback gain of the observer is analyzed, and a simulation is used to verify that the full-order state observer (FSO) method can effectively reduce the estimated speed fluctuation in the intersegment region and that the speed estimation accuracy is ultimately improved.

Estimating inputs for dispersion modeling in mobile platform applications

Mobile monitoring platforms (MMP) are popular in air quality studies. One application of MMP is in estimating pollutant emissions from area sources. The MMP is used to measure concentrations of the relevant species at several locations around the area source, while the associated meteorological information is measured at the same time. Emissions from the area source are inferred by fitting the measured concentrations to estimates from dispersion models. These models require meteorological inputs, such as the kinematic heat flux and the surface friction velocity, that are best computed with measurements of time resolved velocity and temperature made with 3-D sonic anemometers. Because the setting up and dismantling of a 3-D sonic anemometer is not compatible with the necessary mobility of the MMP, it is useful to use alternative instrumentation and methods that provide accurate estimates of these inputs. In this study, we demonstrate such a method based on measurements of horizontal wind speed and temperature fluctuations at a single height. The method was evaluated by comparing methane emissions from a dairy manure lagoon inferred from a dispersion model that uses modeled meteorological inputs to those inferred from measurements with 3-D sonic anemometers. The emission estimates from the modeled meteorological inputs were close to those based on measurements made with 3-D sonic anemometers. We then demonstrate how this approach can be adapted for mobile platform applications by showing that winds measured using a 2-D sonic anemometer and temperature fluctuations measured with a bead thermistor, which can all be carried or mounted on a MMP, yields results that are close to those from a 3-D sonic anemometer.

Robust Adaptive Resonant Controller for PMSM Speed Regulation Considering Uncertain Periodic and Aperiodic Disturbances

This paper studies a robust adaptive resonant controller (RARC) for the speed loop of permanent magnet synchronous motor (PMSM) drives to attenuate periodic speed fluctuation of uncertain frequency. The conventional adaptive resonant controller is susceptible to the aperiodic disturbances (e.g., load torque, parameter variation, and friction torque), so it has poor robustness to the aperiodic disturbance. To solve this problem, a linear extended state observer is applied to estimate the aperiodic disturbances, and then the estimated disturbances are adapted to the adaptive resonant controller to enhance its robustness. Thus, the proposed scheme can suppress the uncertain periodic and aperiodic disturbances simultaneously. Also, the stability of the closed-loop system with the proposed scheme is analyzed by the singular perturbation theory and the averaging theorem, which can guide the tuning of control parameters. Therefore, even if there exist aperiodic disturbances, the studied RARC can also stably operate to suppress the periodic disturbance of uncertain frequency. The effectiveness of the studied RARC is validated on a 2.2-kW PMSM drive platform.

Vibration Suppression for Two-Inertia System With Variable-Length Flexible Load Based on Neural Network Compensation Sliding Mode Controller and Angle-Independent Method

The two-inertia system with variable-length flexible load (TSVFL) is a typical dynamic model uncertain system. It is affected by transmission flexibility, flexible load length variation, and inaccurate friction torque, in which the flexible load will vibrate during rotation. In this study, a neural network compensation sliding mode control (SMC) strategy mixed with the angle-independent method (AIM) is proposed to suppress the vibration of the TSVFL. Among them, the AIM is proposed to design the fluctuating desired input of the motor. The vibration of the flexible load is offset by the speed fluctuation of the motor. The RBF neural networks are proposed to recognize the uncertain term in the TSVFL's dynamic model. First, the nonlinear mathematical model of the TSVFL is deduced based on the assumed mode method. Then, the Lyapunov stability theorem is proposed to design the weight coefficients’ adaptive law in neural networks and the robust term in the control law. Finally, simulation and physical experiments on output speed control are implemented. The results show that the proposed strategy is able to effectively decrease the error and weaken the vibration.

Motion stability analysis of cage of rolling bearing under the variable-speed condition

The fluctuation of the cage rotating speed will lead to the strong instability of the cage movement of the rolling bearing, which will seriously affect the operation performance and service life of the bearing. In this paper, a nonlinear dynamic model of a cylindrical roller bearing is established considering the dynamic contact relationship between the rollers, the raceways, and the cage. The variation of cage motion characteristics and its internal mechanism under the uniform, acceleration, deceleration, and harmonic speed fluctuation are discussed, respectively. The effects of angular acceleration, angular deceleration, amplitude, and period of fluctuation on cage stability are analyzed. The results show the unstable motion of the cage mainly happens at the initial stage of the acceleration process and the later stage of the deceleration process. Appropriate increases in angular acceleration and deceleration can help to improve the stability of the cage movement, but excessive speed fluctuations should be avoided to adversely affect the stability of the cage. The guiding force between the cage and the outer raceway plays an important role in the stability of the cage under variable-speed conditions. The results of this paper can provide a theoretical basis for the design and failure analysis of the cage.

An Ultrashort-Term Wind Power Prediction Method Based on a Switching Output Mechanism

The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model.

Passability and Internode Mechanics Analysis of a Multisection Micro Pipeline Robot

In submarine oil and gas pipelines, the movement of a differential pressure multisection pipeline robot mainly relies on the front and rear driving pressure difference of the fluid and the friction between the cup and the pipe wall. The passability of the pipeline is a key point to guarantee success in scanning and detecting the inner wall of a pipeline by robot. When the multisection pipeline robot moves, the force of the internode connection points changes the degree of freedom of the robot. The existence of the connection points causes speed fluctuations in the robot during the movement process which, in turn, affects the detection accuracy of the pipeline. Consequently, a systematic analysis of the connection point movement is of great importance. In this paper, a rigid–flexible, coupled, multibody, dynamic motion system is established, where a multisection micro pipeline robot is built. The cup of the robot is set as a flexible body. The motion law of the differential pressure multisection pipeline robot is analyzed through simulation, and the robot’s motion speed and internode stress of the different cabin sections are explored jointly in practice. Taking the transportation of an oil and gas pipeline into full consideration, the motion law and force of the multisection pipeline robot are analyzed by changing the bulge and turning the radius of the inner wall of the pipeline. A corresponding experimental bench is built to explore the effects of different numbers of cups and lengths of the cabin sections on the turning characteristics of the robot. Simulations and experiments results are highly similar and within the error range. To this end, the presented work provides significant information for the model selection of multisection micro pipeline robots in the fields of submarine oil and gas pipelines.

A novel hybrid distance guided domain adversarial method for cross domain fault diagnosis of gearbox

Distance-based domain adaptation methods have received extensive application in the transfer learning field. Different domain distances have different characteristics due to various data processing principles. Therefore, choosing appropriate domain distance can accomplish transfer tasks more efficiently. Domain adversarial neural networks can extract domain invariant features through game confrontation, but it is not capable of extracting hidden features of gear under speed fluctuations, and only using the adversarial mechanism for domain feature alignment is prone to gradient collapse. To solve the above problems, a novel hybrid distance guided domain adversarial fault diagnosis method of gear is proposed. First, stacked sparse autoencoders is employed in the model to extract the hidden features from the domain data, and the extracted features are input into the corresponding feature classifier and domain discriminator. Then, a mixture of maximum mean discrepancy (MMD) and Wasserstein distance is utilized to reduce the distribution difference. Finally, the domain adversarial mechanism is used to conduct adversarial training for feature alignment. Through two verification experiments of planetary gearboxes, it is verified that the proposed a Wasserstein and MMD distance guided Domain Adversarial model has excellent fault diagnosis performance under gear fluctuating conditions. In addition, the model has higher prediction accuracy and better fault feature extraction ability compared with other methods.

Cyclic Variability Correlations to Operating Conditions for Spark-Ignition Engines

Variations of successive combustion cycles are associated with power losses, vibration and fluctuations in the engine speed, torque and work done. Previous studies declared that the elimination of cycle-to-cycle variation (CCV) could improve the engine power by 10 %. This work aims at correlating cyclic variability to engine operating conditions to determine the relevant operating conditions that affect the CCV and help designers to predict it. Experimental work was carried out on spark ignition, 4-stroke, 4-cylinder, 2.2 liters, with an 8.85:1 compression ratio and a maximum power of 85 kW at 5,000 rpm. The cyclic variability of many indicators is calculated from the measured in-cylinder pressure data. Results have been used to introduce new correlations considering the effect of normalized speed and load on cyclic variability with acceptable fit. These correlations could be a useful tool in early design calculations, CCV modeling and control.

Reducing Speed Ripples and Vibrations in Permanent Magnet Synchronous Motors with Unbalanced Loads

Permanent Magnet Synchronous Motors (PMSM) have important features such as high torque, high power density, high acceleration, small volume, low noise and high efficiency. With these features, is widely used in the industry. One of the negativities that occur during the operation of electric motors is speed fluctuations and vibrations due to the unbalanced loads. Unbalanced loading can be caused by the character of the load, as well as the poor mounting of the motor to the load, the misalignment on the shaft and the load itself being an eccentric load. Especially at high speeds, this causes permanent damage to the motors. Specification and elimination of these fluctuations, which occur for different reasons, are very important for a reliable operation. In this study, unbalanced load situations are investigated in an unbalanced PMSM and the speed ripples in the unbalanced load of the motor are attempted to be decreased by the proposed Adaptive Harmonic Injection approach. The simulation is carried out under various operating conditions by abruptly and linearly changing the motor speed under unbalanced load. It is observed that speed fluctuations in the motor can be effectively suppressed under different operating condition.