Speed Error Research Articles

Wind–Wave Misalignment in Irish Waters and Its Impact on Floating Offshore Wind Turbines

This study examined the impact of wind–wave misalignment on floating offshore wind turbines (FOWTs) in Irish waters, analysing average weather and extreme events, including hurricane conditions. Using the ERA5 reanalysis dataset validated against Irish Marine Data Buoy Observation Network measurements, the results showed a satisfactory accuracy with an average wind speed error of −0.54 m/s and a strong correlation coefficient of 0.92. Wind–wave misalignment was found to be inversely correlated with wind speed (correlation coefficient: −0.41), with minimum misalignment occurring approximately seven hours after a change in wind direction. The study revealed that misalignment could exceed 30∘ during hurricanes, contradicting standard assumptions of alignment under extreme conditions. The investigation highlighted that in western coastal areas, average misalignment could reach 57.95∘, while sheltered Irish Sea regions experienced lower values, such as 23.06∘. Numerical simulations confirmed that these misalignment events amplified side-to-side turbine deflections significantly. This research underscores the need to incorporate misalignment effects into industry testing standards and suggests that current methodologies may underestimate fatigue loads by up to 50%. This work emphasizes improved design and testing protocols for FOWTs in complex marine environments and highlights the suitability of ERA5 for climate analysis in Ireland.

The Performance of a High-Resolution WRF Modelling System in the Simulation of Severe Tropical Cyclones over the Bay of Bengal Using the IMDAA Regional Reanalysis Dataset

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement.

A double closed loop digital hydraulic cylinder position system based on switching active disturbance rejection control

A switching active disturbance rejection control (SADRC) strategy was proposed to solve the composite disturbance challenge arising from gap, LuGre friction, hydraulic spring force, and external load disturbance in the double closed-loop digital hydraulic cylinder position control system. Firstly, leveraging the established mathematical model of the double closed-loop digital hydraulic cylinder, the high-order state equation was derived. Subsequently, the high-order double closed-loop digital hydraulic cylinder control system was transformed into a second-order integral series control system using ADRC strategy. Given the pronounced nonlinear characteristics of double closed-loop digital hydraulic cylinders, combined with the characteristics of switching control, a SADRC strategy was proposed, and the stability of the closed-loop system was proved based on Lyapunov theory and switching rules. Finally, the effectiveness of the proposed control method was verified through simulation and experiment. Results indicate that the system's response speed employing the SADRC strategy outperforms the PID control strategy by 32.56%, with a 2.0% reduction in error. The average error in the response speed of the digital hydraulic cylinder and the MATLAB simulation value of the tracking error are 9.0% and 1.50% respectively. Notably, the simulation and experimental results exhibit a consistent overall trend, affirming the feasibility and effectiveness of the control strategy.

Characteristic Analysis and Error Compensation Method of Space Vector Pulse Width Modulation-Based Driver for Permanent Magnet Synchronous Motors.

Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness. To address this, the mechanism behind these non-ideal characteristics is analyzed based on the principles of space vector pulse width modulation (SVPWM) and its circuit structure. Tests are then conducted to examine the actual driver characteristics and verify the analysis. Building on this, a real-time compensation method is proposed, physically matched to the driver. Using the volt-second equivalence principle, an input-output voltage model of the driver is derived, with model parameters estimated from test data. The driving error is then compensated with a voltage method based on the model. The results of simulations and experiments show that the proposed method effectively mitigates the influence of the driver's non-ideal characteristics, improving the driving and speed control accuracies by 88.07% (reducing the voltage error from 0.7345 V to 0.0879 V for a drastic command voltage with a sinusoidal amplitude of 10 V and a frequency of 50 Hz) and 53.08% (reducing the speed error from 0.0130°/s to 0.0061°/s for a lower command speed with a sinusoidal amplitude of 20° and a frequency of 0.1 Hz), respectively, in terms of the root mean square errors. This method is cost-effective, practical, and significantly enhances the control performance of PMSMs.

Kernel Adaptive Filtering Algorithm Based on Hyperbolic Tangent Mixed Error Function

This paper proposes an adaptive filtering algorithm based on the symmetry Kernel Hyperbolic Tangent Mixed Error Criterion (KHTMC), aimed at addressing the identification of nonlinear systems under non-Gaussian noise environments. The algorithm optimizes signal processing by constructing a mixed cost function that combines the symmetry logarithmic square error and the hyperbolic tangent function and integrates it with the kernel adaptive filtering method. Simulation results show that, compared to existing kernel adaptive filtering algorithms, the KHTMC algorithm exhibits significant advantages in terms of convergence speed and steady-state mean square error. It demonstrates strong robustness and tracking performance, especially when dealing with mixed non-Gaussian noise. Therefore, this algorithm shows great potential in signal processing applications under complex noise conditions, offering a more reliable and efficient solution.

Unreported large errors in a common method for sound source localization of marine mammals.

Confidence intervals of location of calling marine mammals, derived from time differences of arrival (TDOA) between receivers, depend on errors of TDOAs, receiver location, clocks, sound speeds, and location method. Simulations demonstrate Ishmael, a TDOA locator based on uncorrected least squares minimization (ULSM), yields errors with mean, standard deviation, and maximum of 0.1, 0.2, and 0.9 km, respectively, due to sensitivity to inputs and numerical implementation when applied to scenarios with minuscule errors; e.g., five clock-synchronized receivers residing on the vertices of a square with one in its center. This sensitivity can mask other causes of location error due to small uncertainties in receiver location and sound speed. Realistic uncertainties of sound speed up to ±7.5 m/s lead to errors up to 4 km. With unsynchronized clocks and common practice of correcting TDOA from synchronization measurements at the start and end of an experiment, errors of location are 10 to 1000 km. These problems occur because ULSM was not designed to account for all errors. ULSM is also available in PAMGuard and other systems and is used to study behavior and abundance of calling marine mammals. ULSM is briefly compared to another method designed to account for errors.

Bounded Containment Maneuvering Protocols for Marine Surface Vehicles With Quantized Communications and Tracking Errors Constrained Guidance: Theory and Experiment.

A new type of containment maneuvering protocols for multiple marine surface vehicles (MSVs) is developed to follow a parameterized path in this work, where the tracking errors are constrained within finite time and the information needed to be transmitted is quantized during coordination. To achieve containment maneuvering of multiple MSVs, a two-objective coordinated control framework is proposed. For the geometric objective, by developing tan-type barrier Lyapunov functions (BLFs) and extended Lyapunov condition-based finite-time guidance laws, the performance of the parameterized line-of-sight guidance framework, including convergence speed and tracking error constraints, is improved. For the dynamic objective, based on quantized control strategy and smooth saturation functions, novel bounded containment maneuvering protocols are proposed to dramatically alleviate the burden of communications among MSVs and ensure more faster dynamic behavior on tracking the path updating speed. Both theoretical analysis and experimental tests with comparative studies illustrate the validity of the proposed containment maneuvering strategy.

Wind power correction model designed by the quantitative assessment for the impacts of forecasted wind speed error

Wind power correction model designed by the quantitative assessment for the impacts of forecasted wind speed error

Optimal PI controller parameter setting for torque ripple minimization in SVPWM–DTC based BLDC motor drive using sine cosine algorithm

Abstract This paper focuses on the selection of the most suitable values for the PI controller parameters to minimize the discrepancy between the desired reference values and the actual values of speed and torque in the context of Space Vector Pulse Width Modulation-Direct Torque Control (SVPWM-DTC) of Brushless DC motors (BLDCM). To effectively fine-tune the PI controller, the criteria of Integral of Squared Speed Error (ISSE), Integral of Squared Torque Error (ISTE), and Integral of Squared Flux Error (ISFE) are taken into account as the objective functions across various scenarios. Nevertheless, when it comes to optimizing multiple objective functions, the most optimal approach is to reduce their linear combination. This paper employs JAYA and Sine Cosine algorithms (SCA) for the calibration of the gain parameters of the PI Controller. To assess the effectiveness of the proposed optimal PI controller for SVPWM-DTC of BLDCM, simulations are conducted. According to the findings of the simulations, the Sine Cosine technique yields a remarkable improvement by reducing the torque ripple and controlling the speed peaks overshoot reducing it to 61.6% and 11.9% respectively, and the JAYA algorithm.

Accurate Underwater Acoustic Positioning Considering Geometric Configuration

The accuracy of underwater acoustic positioning is significantly influenced by observing geometry configuration and sound speed variation. The geometric configuration includes the sailing track of the surface vessel as well as the geometry of the seafloor array, all of which are relevant to data processing and error analysis. In this paper, we study how to reduce the influence of sound speed error on underwater positioning based on geometric configuration. For the single point positioning model, an integrated sound speed compensation approach considering the vessel track is proposed to improve positioning accuracy. For the seafloor geodetic network, a network positioning model with virtual seafloor baseline constraints is proposed to improve positioning accuracy. Acoustic positioning experiments conducted in the South China Sea and Japanese open data verify the effectiveness of presented methods. The results show that there is significant diurnal sound speed variation in the South China Sea. The discrepancies between positioning results of different data sets are reduced by sound speed compensation, and the standard deviations of horizontal components are better than 10 cm. The positioning accuracy of repeated surveys in Japanese geodetic network is improved with virtual baseline constraints. Compared with solutions with no virtual baseline constraints, the standard deviation of array center coordinate series is reduced by 8.7%, 21.3%, and 57.1% for the east, north, and up components when dealing with long-term GNSS-A data.

Advanced sensorless control of a 12S/19P YASA-AFFSSPM motor using extended state observer and adaptive sliding mode control

This paper focuses on enhancing the sensorless control performance of a 12slots/19 poles yokeless and segmented armature axial flux-switching sandwiched permanent-magnet motor by proposing a rotor position Extended State Observer based on a extended back-EMF model method. Additionally, an adaptive sliding mode speed loop compensation method is introduced to address the significant cogging torque of the motor. By injecting the observed cogging torque as compensation into the q-axis current harmonic, this method aims to improve the motor's vibration and disturbance rejection performance in sliding mode control while eliminating steady-state errors in rotor speed and position estimation. The effectiveness of these control algorithms is validated through simulations and experiments under various operating conditions, demonstrating their potential for improving the position signal-free tracking performance of the investigated motor. The results indicate that the proposed control strategies achieve a maximum speed estimation error of approximately 1 rpm during steady-state operation and a maximum position estimation error of about 1.5°, showcasing high accuracy and robustness against disturbances.

Study of the double closed-loop active disturbance rejection control strategy for the longitudinal motion of fully submerged hydrofoil craft

In the process of high-speed driving, the longitudinal motion of the hydrofoil craft has the characteristics of parameter uncertainty and strong coupling, which leads to poor stability of the hydrofoil craft and the problem of high precision requirement for disturbance wave data. Therefore, a control method based on active disturbance rejection control (ADRC) and double closed loop is proposed. The algorithm is under the condition of decoupling the ship’s motion attitude and realizes the internal and external double-loop active disturbance rejection control of pitch angle and heave displacement based on the high-order sliding mode observer (HSMO) with better adaptability. By designing the speed error integral sliding mode surface, the dynamic characteristics of the system have been improved., and improve overall hydrofoil stability at high speeds. Finally, based on the Unreal Engine 5 platform, a visual longitudinal motion control system of hydrofoil craft is constructed. The simulation results show that compared to the existing sliding control mode, this control method can reduce the heave displacement and pitch angle by about 50%, shorten the response time of real-time control of hydrofoil craft. The superiority and effectiveness of the control system were verified.

An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions

Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modesspeed control and distance controlare implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.

MEEF criterion-based spline adaptive filtering algorithm and its application

This paper presents an innovative the minimum error entropy with fiducial points (MEEF)-based spline adaptive filtering (S-AF) algorithm, called SAF-MEEF algorithm, which outperforms the conventional SAF algorithms that use the mean square error (MSE) criterion in reducing non-Gaussian interference. To overcome the limitation of the fixed step-size, a variable step-size strategy is also developed, resulting in the SAF-VMEEF algorithm, which improves the convergence speed and steady-state error performance. Furthermore, the computational complexity and convergence analysis of the SAF-MEEF are discussed. Nonlinear system identification simulations test the performance of the presented algorithms. Furthermore, this article accomplishes the application of nonlinear active noise control (ANC). Their effectiveness and robustness against non-Gaussian noise are demonstrated in different experimental scenarios, including α-stable noise, real-world functional magnetic resonance imaging (fMRI) noise, and real-life server room (SR) noise.

Dynamic Response Control Strategy for Parallel Hybrid Ships Based on PMP-HMPC

With increasingly stringent emission regulations, various clean fuel engines, electric propulsion systems, and renewable energy sources have been demonstratively applied in marine power systems. The development of control strategies that can effectively and efficiently coordinate the operation of multiple energy sources has become a key research focus. This study uses a modular modeling method to establish a system simulation model for a parallel hybrid ship with a natural gas engine (NGE) as the prime mover, and designs an energy management control strategy that can run in real time. The strategy is based on Pontryagin’s minimum principle (PMP) for power allocation, and is supplemented by a hybrid model predictive control (HMPC) method for speed-tracking control of the power system. Finally, the designed strategy is evaluated. Through simulation and hardware-in-the-loop (HIL) experimental validation, results compared with the Rule-based strategy indicate that under the given conditions, the SOC final value deviation from the initial value is reduced from 11.5% (in the reference strategy) to 0.39%. The system speed error integral is significantly lower at 39.06, compared to 2264.67 in the reference strategy. While gas consumption increased slightly by 2.4%, emissions were reduced by 3.2%.

The Longitudinal Impact of the COVID-19 Pandemic in Italy: Literacy Acquisition in Low-SES/High-SES Monolingual Children and Low-SES Bilingual Children

In the present study, we applied a two-year longitudinal design to examine the reading and spelling acquisition of primary school students during the COVID-19 pandemic crisis among three groups with different linguistic conditions and socioeconomic status (SES): low-SES and high-SES monolinguals, and low-SES bilingual children (minority language). Reading speed and spelling errors were examined at the beginning of second grade in fall 2019 (pre-pandemic phase) and tested again, with standardised tests, two years later in fall 2021 (post-pandemic phase) when children attended fourth grade. The results showed that, on average, all the children increased both their reading speed and spelling skills. With respect to the role of the COVID-19 pandemic on basic literacy, we cannot broadly conclude that it hampered literacy, given that all groups (regardless of their linguistic condition) showed development in reading and spelling. However, it is worth mentioning that, in light of the qualitative comparison to the norm, spelling accuracy is noticeably lower in low-SES bilinguals than in monolinguals, whereas reading speed is comparable to the standardised value and does not differ significantly between monolinguals and bilinguals. The results contribute to the debate on the interaction between SES and language literacy acquisition in minority languages, particularly under unique conditions such as the COVID-19 pandemic.

Fractional order PID controller optimised by hybrid algorithm for instant torque control of IM motor

ABSTRACT ‘Direct torque control (DTC) is an advanced motion control technique introduced to produce fast torque control in Induction motors’. However, IM has encountered faults in the performance of flux, speed and torque. For improving the performance of induction motors, several controlling techniques were developed in the literature. This paper intends to propose an ‘Optimized Fractional Order Proportional Integral Controller (FoPID)’ for induction motors to accomplish an efficient control of speed by tuning the torque. Moreover, it is planned to exploit a direct instant model, where the speed error (variation amongst estimated speed and reference speed of induction motor) is given as input to the FoPID controller. As a major contribution, the FoPID controller parameters are fine-tuned by means of an innovative hybrid algorithm known as ‘Moth Flame Integrated Dragonfly Algorithm (MFI-DA), which combines both the concepts of Moth Flame Optimization (MFO) and Dragonfly (DA) model’. At last, the efficiency of the accepted method is verified by means of various measures.

Evaluation of an Infinite-Level Inverter Operation Powered by a DC–DC Converter in Open and Closed Loop

This paper evaluates the open- and closed-loop DC–DC converter operation within a DC coupling multilevel inverter architecture to obtain an infinite-level stepped sinusoidal voltage. Adding a cascade controller to the DC–DC converter should reduce the settling time and increase the number of levels in the output voltage waveform; it could decrease the speed error and phase shift concerning the sinusoidal reference signal. The proposed methodology consists of implementing an experimental multilevel inverter with DC coupling through a single-phase bridge inverter energized from a BUCK converter. Trigger signals for the two converters are obtained from a control circuit based in an ATMEGA644P microcontroller to explore its capabilities in power electronics applications. A digital controller is also implemented to evaluate the operation of the BUCK converter in open and closed loop and observe its influence in the stepped sinusoidal output voltage. The evaluation is performed to energize a resistive load with common output voltage in multilevel inverters, i.e., 3, 5, 7, 11, and infinity levels. Results show that during the design stage, fast dynamic elements, like the storage capacitor, can be used to obtain a minimum THD because the settling time is sufficiently fast, the speed error remains small, and there is no need for a controller. A digital controller requires processing time, and although in theory it can reduce the settling time to a minimum, the processor introduces latency in the control signals generation, producing the opposite effect. Controller complexity of the digital controller must be considered because it increases processing time and influences the efficiency of the closed-loop operation.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

Investigation of dynamic stall models on the aeroelastic responses of a floating offshore wind turbine

Dynamic stall effects significantly affect the aerodynamic load prediction of wind turbines. In order to investigate the dynamic stall effects on the loads and responses of a 15 MW floating offshore wind turbine (FOWT), a novel dynamic stall model, namely IAG, is implemented within the widely-used simulation software package OpenFAST in this study. The superiority and accuracy of the IAG model are verified by comparisons against experimental data and numerical results from the Beddoes-Leishman (B-L) model. The results have shown that the IAG model is able to more accurately capture edges of the hysteresis loops of aerodynamic coefficients corresponding to various airfoils and operation states. The aeroelastic responses of a 15 MW floating offshore wind turbine under normal and extreme environmental conditions are calculated by employing the IAG model. The impact of dynamic stall models on blade loads and displacements has been analyzed. It is found that the B-L model produces larger loads and displacements under high wind speed and yaw error conditions, attributed to the insufficiently computational robustness of the B-L model under deep stall situations and the seriously dynamic stall circumstances. The 1st-order and 2nd-order bending modes of the blade are expected to be enhanced by the aerodynamic loads that are predicted using the B-L model. Consequently, the bending-torsional coupling effects would be enhanced, leading to an increase up to 64.7 % on the in-plane bending moment. This study has confirmed that the dynamic stall model should be properly selected properly for the fully coupled analysis of FOWTs.