Step Size Changes Research Articles

An improved P&O-based DC-link voltage control algorithm for standalone PV systems with adaptive step-size and operating point determination strategy

ABSTRACT An improved DC-Link voltage controller (IPOVR), which is built upon the perturb & observe-based voltage regulator (POVR), is proposed in this paper. The aim is to achieve the best balance between tracking time and steady-state oscillation while maintaining good tracking accuracy. This is achieved by implementing a dynamic step-size (∆D) changing strategy and operating region detection technique. Quick tracking time is produced with large initial ∆D, and low steady-state oscillation is obtained through continuous ∆D reduction. Additionally, through the operating region determination technique, the IPOVR operates over a much wider range, typically with lower PV current (IPV), which helps in reducing the losses of the system. It is found that the IPOVR has the lowest steady-state ripple and the highest tracking accuracy, which are averaged at 0.44% and 99.98%, respectively. In terms of tracking time, the IPOVR is averaged at 0.15 s, which is only 0.07 s slower compared to the POVR with high ∆D but is 0.19 s faster than the POVR with low ∆D. Furthermore, the IPOVR recorded higher efficiencies under all tested conditions compared to the POVR. The IPOVR is also demonstrated to be working decently when being connected to a multilevel inverter to generate AC power.

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.

Target Detection Method Based on Adaptive Step-Size SAMP Combining Off-Grid Correction for Coherent Frequency-Agile Radar

Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex electromagnetic environments. However, due to the nonlinearity of radar carrier frequency and the limitation of the Doppler tolerance of high-resolution range cells, the undesirable blind-speed sidelobes are generated in the two-dimensional (2D) range–velocity plane after coherent integration (CI) using the traditional methods based on a matching filter, which may degrade the target detection performance. To solve this problem, an adaptive step-size sparsity adaptive matching pursuit (SAMP) algorithm combining off-grid correction (ASSAMP-OC) is proposed in this paper, which seeks to achieve a better trade-off between recovery efficiency and detection performance. Firstly, an adaptive iteration step size based on the Spearman correlation coefficients (SCCS) is devised, which solves the problem of the traditional SAMP algorithm being insensitive to the change in iteration step size when the residuals vary slightly, and improves the recovery speed. Secondly, the off-grid correction method by combining a regularized stagewise backtracking idea and gradient descent optimization (GDO) is adopted to improve the recovery accuracy and suppress the blind-speed sidelobe energy (BSSE), which helps to reduce CI gain loss and improve the target detection performance without the prior information of the sparsity lever. Finally, simulation and experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of target detection probability, target signal energy ratio after recovery, and computational cost, compared to several existing methods.

Improved variable step adaptive filtering algorithm and its application in time delay estimation for continuous wave dual-sensor pulse signals

Continuous wave mud pulse transmission is crucial for wireless logging, but surface pump noise disrupts upstream mud pulses, hindering downhole information extraction. At a theoretical level, the dual-sensor differential denoising approach is an excellent technology for suppressing pump noise, but precisely identifying the time delay, which has a considerable impact on the denoising effect, remains a challenge. This paper provides an improved variable step-size adaptive time delay estimation method TVSS-LMSTDE to address this issue. Iteration time regulates the change in step size. The preset adjustment principle states that modifying parameters ζ and γ controls the learning features of step size. A series of stable and erratic dual-sensor continuous wave mud pulse signals interfered by various noise intensities are created in the simulation, and the performance of three different variable step-size technologies—error-oriented, nonlinear relationship, and iterative time regulation—in terms of convergence speed, estimation stability, and calculation amount are compared and analyzed. The results show that the iterative time regulation technique produces a minimal mean square error in time delay estimate, with generally steady estimation and excellent accuracy. When applied to hydraulic cycle tests, the technique accurately gets the time delay value of dual-sensor signals, giving critical support for the successful recovery of mud pulse data severely disrupted by pump noise.

Economical handling of Runge–Kutta–Nyström step rejection

Runge–Kutta–Nyström (RKN) pairs are amongst the most popular methods for solving numerically Initial Value Problems of second order. Here we consider the special case with first derivative absent from the right hand side of the classical definition of this problem. While using a RKN pair, we may experience rejection of some steps through the interval of integration. Traditionally, the whole amount of evaluations are then dropped and we proceed with a completely new round of computations. In this work we propose avoiding this waste and continue by using again the rejected RKN stages. We especially focus on a RKN pair of orders six and four. After a step rejection we use again all the previously evaluated stages and add only one new stage. We proceed by evaluating the output in a smaller step. By this technique we manage to reduce significantly the cost in a set of problems that are known to pose difficulties in RKN step size changing algorithms.

Photovoltaic MPPT Control Strategy Based on Improved Conductance Increment Method

In this research, a new incremental conductance approach with variable step size is suggested in order to address the inadequacies of the conventional incremental conductance method in tracking accuracy and dynamic reaction speed. In this paper, an absolute value of dP / (dV – dI) is used as the change in step size, and the step size of the tracking process is dynamically adjusted using the exponential function as the adjustment coefficient to reduce the maximum power point’s tracking duration. Finally, on the Matlab platform, a simulation system was created. According to the simulation findings, the suggested approach has less steady-state oscillations and can track the maximum power point more quickly than the conventional conductance increment method, which increases the efficiency of solar power generation.

Indoor Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Fish Swarm Algorithm

In recent years, with the popularity of lighting infrastructure,indoor visible three-dimensional positioning has attracted extensive attention.However, in the visible three-dimensional positioning algorithm, the positioning error is relatively large due to the high complexity of the system. For the reason that reduce positioning error, an improved fish swarm algorithm is proposed. Adaptive step size and chaotic change are introduced in fish swarm algorithm. In order to reduce the search time in the algorithm, swallowing behavior is adopted to deal with fish, so that the remaining useless weak fish in the iteration process of the improved algorithm is far less than that of the traditional fish swarm algorithm. The positioning system can get sub-micron-level accuracy in the indoor positioning area.

A Family of Nested General Linear Methods for Solving Ordinary Differential Equations

General linear methods (GLMs) was introduced as a generalization of Runge{Kutta methods (RKMs) and linear multistep methods (LMMs). The discovery of general linear method gave insight into the discovery of new methods that are neither RKMs or LMMs. Here, new classes of GLMs that are nested in their stages and mono-implicit in the output are presented, these methods are referred to as nested general linear methods (NGLMs). Procedures for deriving members that are algebraically stable are discussed herein and algebraically stable NGLMs have been derived up to order p = 5. Implementation procedure of these nested general linear methods which include the solution of non-linear systems of equations by simplified Newton iterations and step size changing strategy are discussed. The order p = 3 NGLM has been implemented on two test problems by variable step size, and the results compared with the results of MATLAB ode15s and RADAU IIA.

Performance Investigation of Grid Connected DFIG Based Wind Energy System

The paper realizes the investigation of control operation and performance in grid integrated Doubly Fed Induction Generator (DFIG) system. Battery Energy Storage (BES), coupled at the DC link of DFIG is controlled by bidirectional power converter to compensate for utility/load demand. Rotor side converter (RSC) uses tip-speed ratio maximum power point tracking (MPPT) algorithm to harness maximum power from the wind turbine. An adjustable step size least mean square (LMS) based adaptive control is implemented for the grid side converter (GSC) of DFIG system that besides managing power balance at the Point of Common Coupling (PCC) also addresses power quality issues encountered in the system due to the presence of non-linear, unbalanced loads. The step size changes with the mean square error enabling the adaptive filter to detect system changes while producing a small steady state error. Performance of the system is exhibited and validated through simulated results in a developed Simulink model for steady state and dynamic conditions. The Total Harmonic Distortion (THD) in grid currents and voltage is within IEEE 519 standard guidelines.

Intracrystalline deformation microstructures in natural olivine with implications for stress estimation

Constraining the stress related to lithospheric deformation in natural rocks is key to develop and test a geodynamic model. However, the cautions of extrapolating piezometers that are established on experimental samples to natural rocks are less addressed. In this study, we investigated the microstructures of a natural harzburgite sample using the electron backscatter diffraction (EBSD) technique. Subgrain boundary (SGB) geometries suggest large percentages of (010)[100] and {0kl}[100] dislocation slip systems in olivines. More importantly, multiple low-angle misorientation boundaries (LAMBs) variants are recognized for the first time in olivine based on their distinctive characteristics with the change of EBSD mapping step size. The LAMBs that exist at a small step size (≤ 1 μm) are mostly equivalent to real SGBs, while other LAMBs that appear only when the step size is larger (> 1 μm) are artificial SGBs. Besides, the former develop mainly in the high LAMB density grains, whereas the latter are mostly found in the low LAMB density grains. This result reinforces the previous knowledge that the stress calculated using subgrain-related piezometers is meaningful only when real SGBs are captured at sufficiently small step size. Furthermore, we provide a proof of concept that SGB density and kernel average misorientation (KAM) are two viable metrics to estimate stress. These two alternative piezometers, which still need calibrations using the experimentally deformed samples, are anticipated to have wide applications in natural rocks.

A Low-Power Logarithmic CMOS Digital-to-Analog Converter for Neural Signal Recording

Benefitting from the exponential I-V characteristic curve of CMOS transistors operating in the subthreshold regime, we propose a logarithmic digital-to-analog converter (DAC) for neural recording systems. The reported 8-bit logarithmic DAC converts small voltages with lower resolution and as the input signal increases the resolution of the conversion also increases. The step size changes from 65 mV for the first step to 0.15 mV for the last step. The DAC is designed and implemented in a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology. Operating at 5 MHz the circuit consumes 3.11 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> from a 1.8-V supply. The input-output characteristic of the DAC resembles a logarithmic curve with the difference between measurement results and the ideal logarithm function having a root-mean-square error (RMSE) of 2.4 mV.

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.

A fast explicit diffusion algorithm of fractional order anisotropic diffusion for image denoising

&lt;p style='text-indent:20px;'&gt;In this paper, we mainly show a novel fast fractional order anisotropic diffusion algorithm for noise removal based on the recent numerical scheme called the Fast Explicit Diffusion. To balance the efficiency and accuracy of the algorithm, the truncated matrix method is used to deal with the iterative matrix in the model and its error is also estimated. In particular, we obtain the stability condition of the iteration by the spectrum analysis method. Through implementing the fast explicit format iteration algorithm with periodic change of time step size, the efficiency of the algorithm is greatly improved. At last, we show some numerical results on denoising tasks. Many experimental results confirm that the algorithm can more quickly achieve satisfactory denoising results.&lt;/p&gt;

Variable Step-Size Control Based on Two-Steps for Radau IIA Methods

Two-step embedded methods of order s based on s -stage Radau IIA formulas are considered for the variable step-size integration of stiff differential equations. These embedded methods are aimed at local error control and are computed through a linear combination of the internal stages of the underlying method in the last two steps. Particular embedded methods for 2 ≤ s ≤ 7 internal stages with good stability properties and damping for the stiff components are constructed. Furthermore, a new formula for step-size change is proposed, having the advantage that it can be applied to any s -stage Radau IIA method. It is shown through numerical testing on some representative stiff problems that the RADAU5 code by Hairer and Wanner with the new strategy performs as well or even better as with the standard one, which is only feasible for an odd number of stages. Numerical experiments support the efficiency and flexibility of the new step-size change strategy.

A Consensus-Based Diffusion Levenberg-Marquardt Method for Collaborative Localization With Extension to Distributed Optimization

Non-linear least squares problems arise from data fitting have received recently a lot of attention, particularly for the estimates of the model parameters over networked systems. Although the diffusion Gauss-Newton method offers many advantages for solving the non-linear least squares problem in wireless sensor network to estimate target position parameter, there are some key challenges when applying it to practice, including singularity of Gauss-Newton Hessian, selection to constant step sizes and steady state oscillation. These remaining issues lead to obvious performance degradation such as high computational cost, vulnerability to step size change and resulting instability on estimation.In this paper, to eliminate the singularity, we develop a diffusion Levenberg-Marquardt method such that the problem of constant step size is also addressed together. Then, to reach agreement on estimated vector, a consensus implementation is further proposed, thus eliminating the oscillation during steady state. Consequently, the proposed consensus-based diffusion Levenberg-Marquardt method provides a general solution for the non-linear least squares problems with an objective that takes the form of a sum of squared residual terms. By applying to collaborative localization and distributed optimization arise in large scale machine learning, simulation results confirm the effectiveness and wide applicability of proposed method in terms of convergence rate, accuracy and consistency of estimates.

Logarithmic Versus Linear Change in Step Size When Using an Adaptive Threshold-Seeking Procedure in a Frequency Discrimination Task: Does It Matter?

Different rules for changing step sizes (e.g., logarithmic, linear) are alternately used in adaptive threshold-seeking procedures, with no clear justification. We hypothesized that the linear rule may yield more accurate thresholds for poor performers because the step sizes are predetermined and fixed across listeners and thus can be small, in contrast to the logarithmic rule, in which step sizes are changed with respect to the listener's performance. Purpose The aim of this study was to test the effect of logarithmic and linear rules on frequency discrimination (FD) thresholds. Method Three experiments involving human subjects and Monte Carlo computer simulations were designed and conducted. In the 1st experiment, FD thresholds were estimated in 40 young adults with either 3-interval 2-alternative forced choice (3I2AFC; n = 19) or 2-interval 2AFC (n = 21) in a within-subject design. In the 2nd experiment, thresholds were estimated in 16 children (7-8 years old) in a within-subject design, using 3I2AFC. In the 3rd experiment, thresholds were estimated in 30 young adults in a between-subjects design using 3I2AFC. Results No significant differences were shown between the 2 rules, regardless of age group, method, or level of FD performance. Computer simulations supported the empirical findings, predicting similar FD thresholds for both rules in the majority of runs. However, they also yielded more accurate thresholds with the linear rule, but with a larger number of outliers, which increased as the listener's attention level decreased. Conclusion Overall, the use of a particular rule has little influence on FD thresholds. Possible outliers may be minimized by monitoring the participant's attention at the beginning of the run.

Super-convergent implicit–explicit Peer methods with variable step sizes

Dynamical systems with sub-processes evolving on many different time scales are ubiquitous in applications. Their efficient solution is greatly enhanced by automatic time step variation. This paper is concerned with the theory, construction and application of IMEX-Peer methods that are super-convergent for variable step sizes and A-stable in the implicit part. IMEX schemes combine the necessary stability of implicit and low computational costs of explicit methods to efficiently solve systems of ordinary differential equations with both stiff and non-stiff parts included in the source term. To construct super-convergent IMEX-Peer methods which keep their higher order for variable step sizes and exhibit favourable linear stability properties, we derive necessary and sufficient conditions on the nodes and coefficient matrices and apply an extrapolation approach based on already computed stage values. New super-convergent IMEX-Peer methods of order s+1 for s=2,3,4 stages are given as result of additional order conditions which maintain the super-convergence property independent of step size changes. Numerical experiments and a comparison to other super-convergent IMEX-Peer methods show the potential of the new methods when applied with local error control.

Research on Global Path Planning Method of Mobile Robot Based on BAS

In this paper, a path planning algorithm based on the beetle antennae search (BAS) is presented, which can effectively improve the speed, obstacle avoidance and environmental adaptability of the robot path planning. The distance between the beetle and the target point is the fitness function, and the changing step size is to avoid the local optimization. According to compare random coordinates of the beetle’s left and right antennae, the direction of walking is determined to plan the better path in the static environment. The obstacle avoidance is judged by whether the beetle’s position and walking trail are in the effective area. Multiple beetles are set at each path point, and the optimal direction which approaches the theoretical optimal path, is chosen after comparing with every beetle’s trail. A large number of results of the experiments based on RRT and BAS algorithm indicate that BAS algorithm is more capable of searching its path and avoiding obstacles. Even in a complex environment, compared with other algorithms, BAS is more likely to rapidly plan an optimal path which can successfully avoid obstruction.

Robust Variable Step-Size Affine Projection Sign Algorithm Against Impulsive Noises

The present study introduces a robust variable step-size affine projection sign adaptive algorithm (RVSS-APSA) in impulsive noise environments. In the proposed RVSS-APA, the weight coefficients are updated based on the minimization of $$\ell _2$$-norm of the a posteriori error and the step size changes according to the minimization of $$\ell _1$$-norm of the a posteriori error. This algorithm reduces the steady-state misalignment and increases the convergence rate for colored input signal as well as with or without impulsive noise interference. Also, a new simple reset algorithm is proposed to improve the tracking ability of the introduced algorithm. The simulation results demonstrate a good performance for the proposed algorithm in different situations.

Nahua mushroom gatherers use area-restricted search strategies that conform to marginal value theorem predictions

We develop a method of analysis for testing the marginal value theorem (MVT) in natural settings that does not require an independent definition or mapping of patches. We draw on recent theoretical work on area-restricted search (ARS) that links turning-angle and step-size changes to geographically localized encounter-rates. These models allow us to estimate "giving-up times" using encounter-annotated GPS tracking data. Applied to a case study of Nahua mushroom foragers, these models identify distinct forms of intrapatch and interpatch search behavior, with intrapatch search transitioning to interpatch search after a predictable interval of time since the last encounter with a harvested mushroom. Our empirical estimate of giving-up time coincides with the theoretically optimal giving-up time derived under the MVT in the same environment. The MVT is currently underused in studies of human foraging and settlement patterns, due in large part to the difficulty of identifying discrete resource patches and quantifying their characteristics. Our methods mitigate the need to make such discrete maps of patches and thus have the potential to broaden the scope for empirical evaluations of the MVT and related theory in humans. Beyond studies of naturalistic foraging in humans and other animals, our approach has implications for optimization of search behavior in a range of applied fields where search dynamics must be adapted to shifting patterns of environmental heterogeneity affecting prey density and patchiness.