Low Distortion Research Articles

Analysis and Development of a Bidirectional Three-Phase Hybrid Rectifier for DC Microgrids With Distributed Energy Resources

This work presents the analysis and development of a Bidirectional Three-phase Hybrid Rectifier (BTHR) for application in Direct Current (DC) Microgrids connected to the utility grid. The voltage regulation technique, called Series DC-bus Voltage Compensation (SDCVC), allows the design of an active front-end converter with high power density. The decentralized control strategy ensures low harmonic distortion of current in the utility grid in both operating modes (rectification and regeneration), in addition to providing DC-bus voltage compensation against power flow dynamics related to Renewable Energy Sources (RES). The operating principle, the control strategy, and the design criteria used for determining the transfer function of the proposed BTHR-SDCVC-DCMG, as well as to the design of the DC-bus voltage controller, are presented. To validate the theoretical analysis, a 1kW prototype was implemented and evaluated at the laboratory. Steady state analysis demonstrate that high efficiency, high power factor, and low harmonic distortion of current are achieved. Finally, transient analysis was performed and demonstrated that the stability of the DC-bus voltage with and without the insertion of RES into the DC microgrid can be assured deploying the proposed solution.

Single-Stage Integrated Buck-Flyback PFC Converter With Low Total Harmonic Distortion Control

Single-Stage Integrated Buck-Flyback PFC Converter With Low Total Harmonic Distortion Control

A Hybrid PWM Strategy for Switching Loss Reduction in Three-Level Inverters

The advanced bus-clamping PWM (ABCPWM) sequences are well known to reduce line current THD in a three-level neutral point-clamped inverter. This paper deals with ABCPWM sequences employed optimally to achieve minimum switching loss throughout the linear operating region in a three-level inverter. The work extensively analyzes the performance of these sequences in terms of inverter switching loss and compares them with centered space vector modulation PWM (CSVPWM). Based on the analysis, a hybrid PWM scheme termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">switching loss reduction</i> is proposed by optimally employing ABCPWM sequences in conjunction with variation in power factors. At a given average switching frequency, the proposed hybrid scheme reduces inverter switching loss compared to CSVPWM and the recently proposed discontinuous PWM-based optimization strategy at all power factors and modulation indices. With this scheme, switching loss reduces to 22% to 36% compared to CSVPWM and a maximum of 12% reduction than the existing optimization strategy. Furthermore, the scheme gives lower harmonic distortion compared to these two existing PWM schemes near high modulation indices and near unity power factor load. The theoretical, simulated, and real-time simulation results are presented with experimental results using TMS320F28335 DSP controller for validating the proposed work.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac

Background and PurposeThe feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and MethodsApparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. ResultsPhantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. ConclusionAccurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

Control Scheme Influence on the Performance of Shunt Active Power Filter based on Five-level NPC Inverter in Steady and Transient Conditions

This paper presents an investigation on the performance of shunt active power filter (Shunt APF) systems based on five-level neural point clamped (NPC) inverter in steady and transient conditions adopting a fuzzy control scheme. The shunt APF is operated as a current source connected in parallel with the non-linear loads to inject compensates current harmonics into the AC source. The multi-level inverters have gained considerable interests in last year’s. Five-level inverter is recommended in medium and high voltage applications; their advantages are low harmonic distortions, low switching losses, low electromagnetic interference, low voltage stress of power semiconductors and low acoustic noise. Secondly fuzzy logic has been widely used in various industrial applications. To benefit from these advantages a shunt active power filter based on five-level (NPC) inverter using fuzzy control scheme is presented in this work. To identify the reference currents, synchronous detection method (SMD) is adopted. Proportional integral voltage controller is used to maintain the dc voltage across capacitor constant and reduce inverter losses. The simulation is performed using MATLAB-Simulink software and SIMPOWERSYSTEM toolbox. The obtained results in steady and transient states illustrate the performances and the effectiveness of the proposed shunt APF system.

A Comparative Study of 9-Level Inverter and 15-Level Multi Inverter for a Grid Connected System

Abstract: Recently, the emergence of single phase multilevel inverter has been increased due to its advantages over traditional one. Multilevel Inverters [MLI] are the ones which can give out a stepwise output waveform with the use of power electronic switches, power diodes and some DC voltage source which might be a series/parallel connected PV cells, a renewable source or a battery. Multilevel inverters not only produce low harmonic distortion but also decrease the dv/dt stresses on the equipment Multilevel inverters is a good option as the output voltage of a Multilevel inverter is a stepped waveform which approaches a sinusoid providing lesser harmonic distortion. As the number of levels are increased the harmonic distortion reduces but at the same time the number of switching devices and the DC voltage sources required increases, thus increasing the complexity of the system design and control. Multilevel inverter proposed in this paper uses only 7 switches to provide 9 levels and 15 levels of output voltage. A symmetrical Cascaded H bridge Multi level inverter (SCHBMLI) and Asymmetrical Cascaded H bridge Multilevel Inverter (ASCHBMLI) using PD-PWM technique have been analysed in this paper for grid connection. The Asymmetric Cascade H Bridge [ASCHMLI] implemented in real time system using PIC16F877A microcontroller with RL load. This type of proposed system is used for high power applications in photovoltaic and it’s reduce overall cost as well as size of the system.

Performance Analysis of Thirty-one Level Multilevel Inverter for Electric Vehicle Application

This research provides a new architecture for reduced component count 31-level multilevel inverter (MLI). The suggested design uses four unequal DC sources and an H-bridge to provide a maximum 31-level output voltage. For applications using renewable energy, the topology designed for the 31-level can be employed. This lowers the system's total cost, number of components, and size. Higher level counts for lower total harmonic distortion (THD), however, dependability concerns are more significant than MLIs' many benefits. Total standing voltage (TSV), cost function (CF), and power loss are the various metrics that are examined for 31-level MLI. Experiments with different combinational loads and dynamic load fluctuations with sudden load disturbances are performed to assess the inverter's performance. The cost-effectiveness of the suggested MLI is demonstrated while comparing its TSV with the cost function against other topologies that have been recently published.

Analytical Design of Gaussian Anisotropic 2D FIR Filters and Their Implementation Using the Block Filtering Approach

This work proposes an analytical design procedure for a particular class of 2D filters, namely anisotropic Gaussian FIR filters. The design is achieved in the frequency domain and starts from a low-pass Gaussian 1D prototype with imposed specifications, whose frequency response is efficiently approximated by a factored trigonometric polynomial using the Chebyshev series. Then, using specific 1D to 2D frequency mappings applied to the prototype, the frequency response for a 2D anisotropic filter with a specified orientation angle is directly derived in two versions, namely with a straight or elliptical shape in the frequency plane. The resulting filters have an accurate shape with low distortion. Several design examples for specified parameters (angle and selectivity) are provided. Then, simulations of directional filtering on various test images are given, which show their capability of extracting oriented lines or other various oriented objects from synthetic or real-life images. Finally, a computationally efficient implementation at the system level is proposed, based on a polyphase decomposition and block-filtering approach, which yields a 2D filter with a high degree of parallelism and low arithmetic complexity.

Uncovering the discharge structure evolution and electrochemical performance of sulfur cathode host 2D-Fe0.89M0.11S2 (M = Ti, V)

The sulfur cathode host materials with excellent electronic conductivity and catalytic performance are the key to highly efficient Li-S redox chemistry for the lithium‑sulfur batteries (LSBs). Although 2D-FeS2 as sulfur cathode host shows high theoretical capacity and flat voltage plateaus, the Li-S redox chemical reaction mechanism is still unclear. By using the first-principles calculations, the formation energy, working voltage, theoretical capacity, electronic conductivity, adsorption performance, electrocatalytic mechanism, Li+ diffusion and Li2S decomposition barrier of 2D-FeS2 and M (M = Ti, V) doped 2D-FeS2 are investigated to evaluate its physicochemical properties. 2D-Fe0.89M0.11S2 is thermodynamically stable due to its low lattice distortion, formation energy and mixing enthalpy. The charging voltage of 2D-Fe0.89Ti0.11S2 is better than that of 2D-Fe0.89V0.11S2, but the charging voltage step is reversed. The electrical conductivity of 2D-Fe0.89M0.11S2 is superior to 2D-FeS2. The closer the highest molecule occupied orbitals is to the Fermi level, the lower the discharge voltage will be. 2D-Fe0.89V0.11S2 and its discharge structure 2D-Li2Fe0.89V0.11S2 improve the efficiency of catalytic reduction of Li2S4. 2D-Fe0.89V0.11S2 has low Li+ diffusion and decomposition barrier of Li2S, which has potential application value in promoting the Li-S redox chemistry. This work deepens the understanding of the doping strategy and provides theoretical guidance for the design and development of sulfur cathode host to improve the Li-S redox chemistry.

Detection and Imaging of Corrosion Defects in Steel Structures Based on Ultrasonic Digital Image Processing

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Low-Distortion Clustering with Ordinal and Limited Cardinal Information

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we would like to evaluate clustering algorithms in terms of social cost objectives that are defined using d. This is done using the notion of distortion, which measures how far from optimality a clustering can be, taking into account all underlying metrics that are consistent with the ordinal information available. Unfortunately, the most important clustering objectives (e.g., those used in the well-known k-median and k-center problems) do not admit algorithms with finite distortion. To sidestep this disappointing fact, we follow two alternative approaches: We first explore whether resource augmentation can be beneficial. We consider algorithms that use more than k clusters but compare their social cost to that of the optimal k-clusterings. We show that using exponentially (in terms of k) many clusters, we can get low (constant or logarithmic) distortion for the k-center and k-median objectives. Interestingly, such an exponential blowup is shown to be necessary. More importantly, we explore whether limited cardinal information can be used to obtain better results. Somewhat surprisingly, for k-median and k-center, we show that a number of queries that is polynomial in k and only logarithmic in n (i.e., only sublinear in the number of agents for the most relevant scenarios in practice) is enough to get constant distortion.

Edge Boosted Global Awared Low-light Image Enhancement Network

Low-light images are captured in situations where the lighting is poor or the camera hardware is not capable of producing good quality images. These types of images tend to have low contrast, blurry details, noise, and color distortion. In computer vision applications, image brightness plays a crucial role, and therefore, low-light image enhancement is used as a preprocessing step. In this study, we have improved the Low-Light Enhancement Network with Global Awareness (GLADNet) method by adding a UNet-based edge information extraction unit. The channel attention mechanism was also incorporated into the edge information extraction unit to achieve color preservation. Our experiments show that our proposed method has achieved higher PSNR, SSIM, and FSIM metrics compared to reference images. Additionally, it has produced lower NIQE and BRISQUE values for non-reference performance evaluation. Moreover, our proposed method removes noise better and produces visual results that are closer to the target images.

A general micromechanics-based model for precipitate strengthening and fracture toughness in polycrystal high entropy alloys

High-entropy alloys (HEAs) usually exhibit exceptional mechanical properties attributed to one of important core effects for serious lattice strain to impede dislocation motion compared to the traditional alloys. However, their roles on the quantitative measurement for precipitate strengthening and fracture toughness are lack using the existing physical model. Here, we propose a mechanistic modelling to study effect of heterogeneous strain caused by lattice distortion on the precipitate strengthening and fracture toughness in the HEAs, and then verify this role using atomic simulation. The results indicate that the lattice distortion and precipitate synergistically impede the grain boundary migration, increasing the strength. In the dilute alloy with a low lattice distortion, the grain boundary migration process is less sensitive to the precipitate size. The stress field generated by the lattice distortion relieves the stress concentration at the crack tip under external force. This in turn alleviates the accumulation of dislocations and reduces the probability of crack extension. Furthermore, the heterogeneous strain caused by lattice distortion counteracts some of the applied stress and raises the critical stress for crack extension, which enhances the plasticity and the critical stress intensity factor. The developed unified model would be applicable to high entropy ceramics in similar scenario.

Experimental assessment of integral-type terminal sliding mode control designed for a single-phase grid-interlinked PV system

Due to the increasing global adoption of grid-connected photovoltaic (PV) systems and their unpredictable power production behavior, as well as their interactions with the electrical network, PV system controllability is a major aspect that should be taken into consideration. The paper under discussion implements an integral-type terminal sliding mode control (ITTSMC) technique for a single-stage, single-phase grid-interlinked PV system. The ITTSMC approach is dedicated to feeding the electrical network with a sinusoidal current with low total harmonic distortion (THD), providing accurate reference tracking with a finite time convergence, and increasing the system robustness against parameter mismatch and climatic conditions variation. In pursuit of this goal, the system's mathematical model is established under the proposed method, and its stability is proved using Lyapunov stability criterion. Reliability and performance assessment of the suggested control scheme have been carried out through a numerical simulation using Matlab/Simulink software. In addition, laboratory experiments employing a PV panel and Dspace 1104 controller board-based test bench are executed. The results obtained from various scenarios have unequivocally demonstrated the superior effectiveness of the adopted approach concerning power quality and dynamic performances compared to other existing controllers.

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

Microstructure, hardness, and tribological performance of D2 tool steel fabricated by laser cladding using pulsed wave and substrate heating

D2 steel is a tool grade steel that presents hot cracking problems during the solidification process due to its alloying elements. On the other hand, additive manufacturing processes have great potential to manufacture, repair, and functionalize metal parts due to their low heat input, minimal distortion, and excellent metallurgical bonding. In this work, D2 metallic powder was deposited by the laser cladding process using pulsed wave, without and with preheating of the substrate. Through ANOVA statistical analysis using a complete factorial design 24, the optimal regions were determined to obtain D2 deposits with minimum dilution and depth, and maximum widths, heights and high hardness. Once the optimal parameters of the process were established, the coatings were deposited without and with preheating the substrate where, regardless of the condition, coatings without defects and good metallurgical adhesion were obtained. Coatings with preheating obtained higher cooling rates, where lower laser powers promoted higher rates; while in the coatings without preheating, lower duty functions promoted higher rates. The austenite, martensite and M7C3 eutectic carbides phases were obtained, with austenite being the main phase due to the C content in solid solution due to the cooling rates achieved. The coatings in as-built condition, reached hardness values between 606 and 711 HV (55–60 HRC), where the preheated coatings obtained the highest values. On the contrary, the coatings without preheating showed a better wear resistance (7.672 x10-6 mm3/Nm) due to the formation of tribo-layers due to the formation of oxides due to the higher amount of austenite.

Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

The formation of nickelalactone in CO2/C2H4 coupling reaction: A benchmark, dispersion correction, and energy decomposition analysis

The oxidative coupling of CO2 and ethylene for synthesizing acrylic acid and its derivatives is considered one of the “dream reactions” in catalysis. The formation of acrylic acid by CO2/C2H4 coupling is hardly possible due to the endothermic nature of the reaction. This work reports a comprehensive study of the nickelalactone formation from CO2/C2H4 coupling over Ni-catalysts. We have evaluated the accuracy of the DLPNO-CCSD(T) method in determining the barrier heights and the reaction energetics for the nickelalactone formation. Extrapolation to the complete basis set limit was performed using two-point extrapolation methods. It was found that the DLPNO-CCSD(T) method, in combination with the complete basis set cc-pVTZ/cc-pVQZ extrapolation, gives the error within 1.0 kcal/mol. To gain a molecular understanding of the formation of acrylic acid by CO2/C2H4 coupling, the distortion-interaction model was used as an energy decomposition method to analyze the inner- and outer-sphere reaction transition states, considering two competing pathways. It was found that i) the inner-sphere reaction is superior to the outer-sphere reaction owing to the lower distortion energy and stronger interactions, ii) three prominent pairs of orbital interactions exist between the Ni-C2H4 complex and the CO2 molecule, and iii) the dispersion correction is crucial to obtaining reliable non-covalent interaction energies.

Application of Generative Adversarial Network in Image Color Correction

The popularity of electronic products has increased with the development of technology. Electronic devices allow people to obtain information through the transmission of images. However, color distortion can occur during the transmission process, which may hinder the usefulness of the images. To this end, a deep residual network and a deep convolutional network were used to define the generator and discriminator. Then, self-attention-enhanced convolution was applied to the generator network to construct an image resolution correction model based on coupled generative adversarial networks. On this basis, a generative network model integrating multi-scale features and contextual attention mechanism was constructed to achieve image restoration. Finally, performance and image restoration application tests were conducted on the constructed model. The test showed that when the coupled generative adversarial network was tested on the Set5 dataset, the image peak signal-to-noise ratio and image structure similarity values were 31.2575 and 0.8173. On the Set14 dataset, they were 30.8521 and 0.8079, respectively. The multi-scale feature-fusion algorithm was tested on the BSDS100 dataset with an image peak signal-to-noise ratio of 30.2541 and an image structure similarity value of 0.8352. Based on the data presented, it can be concluded that the image correction model constructed in this study has a strong image restoration ability. The reconstructed image has the highest similarity with the real high-resolution image and a low distortion rate. It can achieve the task of repairing problems such as color distortion during image transmission. In addition, this study can provide technical support for similar information correction and restoration work.

Analysis of the performance of a 25-level inverter with a minimum number of switches and reduced harmonics for an environment solar energy

A multilevel inverter is a type of electrical equipment that converts a DC voltage to a higher AC value by creating a stepped waveform using several voltage levels. Multilevel inverters may create waveforms with three or more voltage levels, although regular inverters cannot. This separation results in lower harmonic distortion, decreased electromagnetic interference, and higher efficiency. An innovative MLI design that makes use of fewer switches and PV sources solves this problem. The Perturbation and Observation (P&amp;O) approach is used to derive the Maximum Power Point Tracking (MPPT) from these PVs. The MC-SPWM technique was used in the design, simulation, and construction of single-phase and three-phase multilevel inverters with inverter levels ranging from three to twenty-five. The technique's cornerstones are phase disposition (PD-PWM) and power quality improvement. The main goal of the paper objectively analyzing the issue of power-system harmonics, providing information on causes, effects, and useful harmonic mitigation strategy.