Mitigation of Circulating Currents for Parallel Connected Sources in a Standalone DC Microgrid

In a standalone DC microgrid, sources are interconnected in a parallel configuration. When sources of different power ratings are parallel connected, there arises a major issue of circulating currents which disturb current sharing by sources as per their capacity. Consequently, the voltage regulation becomes poorer. Additionally, connecting line resistances also play their part to contribute to abnormal current sharing. Droop controllers are normally preferred for the mitigation of circulating currents among parallel-connected sources. However, droop controllers cannot eliminate circulating currents for different rating sources. Hence, current sharing and voltage regulation cannot be ensured simultaneously. To address the issues, a distributed architecture-based Sliding Mode Control (SMC) technique is proposed in this paper. An analysis of the circulating currents for a two-source system is presented. Simulation results are presented to show the effectiveness and fail-safe operation of the proposed technique in a steady-state condition.

Performing Energy-Efficient Pick-and-Place Motions for High-Speed Robots by using Variable Stiffness Springs

Typically, for pick-and-place robots operating at high speeds, an enormous amount of energy is lost during the robot braking phase. This is due to the fact that, during such operational phase, most of the energy is dissipated as heat on the braking resistances of the motor drivers. In order to increase the energy-efficiency during the high-speed pick-and-place cycles, this paper investigates the use of variable stiffness springs (VSS) in parallel configuration with the motors. These springs store the energy during the braking phase, instead of dissipating it. The energy is then released to actuate the robot in a next displacement phase. This design approach is combined with a motion generator which seeks to optimize trajectories for input torques reduction (and thus of energy consumption), through solving a boundary value problem (BVP) based on the robot dynamics. Experimental results of the suggested approach on a five-bar mechanism show the drastic reduction of input torques, and therefore of energetic losses.

Forward Kinematics of Delta Manipulator by Novel Hybrid Neural Network

For the parallel configuration of the robot manipulator, the solution of Forward Kinematics (FK) is tough as compared to Inverse Kinematics (IK). This work presents a novel hybrid method of optimizing an Artificial Neural Network (ANN) specifically Multilayer Perceptron (MLP) with Genetic Algorithm (GA) and Step-wise Linear Regression (SWLR) to solve the complex FK of Delta Parallel Manipulator (DPM). The joint space angular positional data has been iterated using IK to generate point cloud of Cartesian space positional data. This data set is highly random and broad which leads to higher-order nonlinearity. Hence, normalization of the dataset has been done to avoid outliers from the dataset and to achieve better performance. The developed ANN based MLP gave a mean square error of 0.0000762 and an overall R2 value of 0.99918. Finally, the proposed network has been simulated to solve FK of the parallel manipulator and to check its efficacy. For given joint angles, the proposed network predicted positional values which are in good approximation with known trajectory solved by standard analytical method.

DNA bridges: A novel platform for single-molecule sequencing and other DNA-protein interaction applications

DNA molecular combing is a technique that stretches thousands of long individual DNA molecules (up to 10 Mbp) into a parallel configuration on surface. It has previously been proposed to sequence these molecules by synthesis. However, this approach poses two critical challenges: 1-Combed DNA molecules are overstretched and therefore a nonoptimal substrate for polymerase extension. 2-The combing surface sterically impedes full enzymatic access to the DNA backbone. Here, we introduce a novel approach that attaches thousands of molecules to a removable surface, with a tunable stretching factor. Next, we dissolve portions of the surface, leaving the DNA molecules suspended as ‘bridges’. We demonstrate that the suspended molecules are enzymatically accessible, and we have used an enzyme to incorporate labeled nucleotides, as predicted by the specific molecular sequence. Our results suggest that this novel platform is a promising candidate to achieve high-throughput sequencing of Mbp-long molecules, which could have additional genomic applications, such as the study of other protein-DNA interactions.

Fiber Optic Load Cells with Enhanced Sensitivity by Optical Vernier Effect

Developing technologies capable of constantly assessing and optimizing day-to-day activities has been a research priority for several years. A key factor in such technologies is the use of highly sensitive sensors to monitor in real-time numerous parameters, such as temperature and load. Due to their unique features, optical fiber sensors became one of the most interesting and viable solutions for applications dependent on those parameters. In this work, we present an optical fiber load sensor, called load cell, based on Fabry–Pérot hollow cavities embedded in a polymeric material. By using the load cells in a parallel configuration with a non-embedded hollow cavity, the optical Vernier effect was generated, allowing maximum sensitivity values of 0.433 nm N−1 and 0.66 nm °C−1 to be attained for vertical load and temperature, respectively. The proposed sensor’s performance, allied with the proposed configuration, makes it a viable and suitable device for a wide range of applications, namely those requiring high thermal and load sensitivities.

Reliability analysis of a small solar system for a home

PurposeThe purpose of this paper is to model and to improve the stability and long-lasting operation of the small home solar system configuration regarding the reliability, availability, sensitivity, cost analysis and mean time to failure (MTTF).Design/methodology/approachA model of a small home solar system is designed in this write up. It is designed in a series–parallel configuration, such that four panels are arranged in parallel, working under 1-out-of-4: G; policy and two batteries configured in parallel also, working under 1-out-of-2: G; policy. The panels are connected to a charge controller, then to the batteries and lastly to an inverter, all connected together in series configuration. Different types of system reliability such as reliability, sensitivity, availability, MTTF and cost analysis for particular values of the failure and repair rates have been evaluated by using a supplementary variable and Laplace transforms and demonstrated the computed results on tables and graphs. The main objective here is to improve the stability and long-lasting operation of the small solar system configuration regarding the reliability, availability, sensitivity, cost analysis and MTTF.FindingsThe future behavior of the small solar system and similar systems can be easily predicted at any given time for any parametric values, it is also better to provide repair than replacements in the system for better availability and reliability, it signifies that γp, γb, γc and γi are responsible for the better performance of the system, the variation of sensitivity together with the parametric values variations and lastly deduced that the profit will decrease whenever service cost increase.Originality/valueThis paper provides a model of small home solar system and its reliability analysis.

Parallel Hybrid Electric Vehicle Modelling and Model Predictive Control

This paper presents the modelling and calculations for a hybrid electric vehicle (HEV) in parallel configuration, including a main electrical driving motor (EM), an internal combustion engine (ICE), and a starter/generator motor. The modelling equations of the HEV include vehicle acceleration and jerk, so that simulations can investigate the vehicle drivability and comfortability with different control parameters. A model predictive control (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with hard constraints as it provides a faster setpoint tracking and smoother clutch engagement. The conversion of some hard constraints into softened constraints can improve the MPC stability and robustness. The MPC with softened constraints can maintain the system stability, while the MPC with hard constraints becomes unstable if some input constraints lead to the violation of output constraints.

Universal Protoplanetary Disk Size under Complete Nonideal Magnetohydrodynamics: The Interplay between Ion-neutral Friction, Hall Effect, and Ohmic Dissipation

The role of nonideal magnetohydrodynamics has been proven critical during the formation of protoplanetary disks, particularly in regulating their sizes. We provide a simple model to predict the disk size under the interplay among ambipolar diffusion, the Hall effect, and ohmic dissipation. The model predicts a small disk size of around 20 au that depends only sublinearly on disk parameters, for a wide range of initial conditions of subsolar mass and moderate magnetization. It is able to explain phenomena manifested in existing numerical simulations, including the bimodal disk behavior under parallel and antiparallel alignment between the rotation and magnetic field. In the parallel configuration, the disk size decreases and eventually disappears. In the antiparallel configuration, the disk has an outer partition (or pseudodisk), which is flat, shrinking, and short-lived, as well as an inner partition, which grows slowly with mass and is long-lived. Even with significant initial magnetization, the vertical field in the disk can only dominate at the early stage when the mass is low, and the toroidal field eventually dominates in all disks.

Model-based motion simulation of delta parallel robot

For a parallel configuration of a robot manipulator, the solution of Forward Kinematics (FK) is tough compared to Inverse Kinematics (IK). This paper presents the model-based motion planning of a delta parallel robot in Simulink’s SimScape environment. A model was developed and simulated for motion study. The developed model has been simulated to solve the FK of the parallel manipulator and to check its efficacy. First, a helix curve has been planned within the reachable workspace. Then IK was solved to extract angular positions of the biceps. Obtained angular positions then fed to SimScape model to run a simulation. The path taken by the end effector of the system calculated by simulation is in good approximation to the planned helix path. Further, visual simulation and motion analysis of delta parallel robot can be performed by Model-based simulation and solves mechanical design as well control system design problems. Experimental study also shows that the circular path designed for experiment is well followed in real time simulation.