Real-time Hardware Research Articles

Performance of the Belle II calorimeter trigger system at the SuperKEKB Phase 3 run

The Belle II experiment at the SuperKEKB electron-positron collider began physics data-taking in 2019 with full detectors. The main goal of the Belle II is to search for new physics beyond the Standard Model in heavy flavor decays and Dark sector. In order to select events of interest efficiently under severe beam background environment from higher instantaneous luminosity run than the KEKB collider, we have upgraded a real-time hardware trigger system using the CsI(Tl) electromagnetic calorimeter. In this report, performance of the calorimeter trigger system under SuperKEKB Phase 3 operation is described.

A Controllable Thyristor-Based Commutation Failure Inhibitor for LCC-HVDC Transmission Systems

Commutation failure is a serious malfunction in line-commutated high voltage direct current (HVdc) converters which is mainly caused by the inverter ac faults, and results in a temporary interruption of transmitted power and damage to the converter equipment. In this article, a controllable commutation failure inhibitor (CCFI) is developed which obviates the main drawbacks of the existing power electronic based and fault current limiting based strategies. Under normal circumstances, the developed CCFI improves the steady-state stability and the power transfer capability of the inverter ac lines, while it does not cause excessive voltage stress on the converter valves. In addition, it would reduce the risk of commutation failure occurrence, since it does not lead to any voltage drop in the commutation circuit. When a fault occurs at one of the inverter ac systems, its corresponding CCFI limits the fault current depending on the reduced extinction angle. This would not only inhibit the successive commutation failures on the HVdc converter, but also extend the lifetime of components in the inverter ac systems. The practical feasibility of the developed CCFI is assessed through laboratory testing, using a real-time Opal-RT hardware prototyping platform. The obtained results indicate that the developed CCFI can reliably inhibit the commutation failures during various types of faults.

Closed-loop controller design, stability analysis and hardware implementation for fractional neutron point kinetics model

The aim of this work is the analysis, design and hardware implementation of the fractional-order point kinetics (FNPK) model along with its closed-loop controller. The stability and closed-loop control of FNPK models are critical issues. The closed-loop stability of the controller-plant structure is established. Further, the designed PI/PD controllers are implemented in real-time on a DSP processor. The simulation and real-time hardware studies confirm that the designed PI/PD controllers result in a damped stable closed-loop response.

A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles

Abstract Hybrid energy storage systems have attracted more and more interests due to their improved performances compared with sole energy source in system efficiency and battery lifetime. This study aims to propose a real-time energy management control strategy for achieving these goals. The strategy is based on a combination of wavelet transform, neural network and fuzzy logic. Wavelet transform is an effective tool in extracting different frequency components of load power demand to match the characteristics of battery and supercapacitor. However, it is hard to be directly applied in a real-time system. For this, a neural network model, which is offline trained using the dataset obtained from the wavelet transform decomposition, is developed to online predict the low frequency power demand for the battery. Accordingly, the high frequency power demand is online calculated and distributed to the supercapacitor. In addition, a fuzzy logic based supervisory controller is further developed for controlling the supercapacitor voltage within a certain suitable range. Finally, a 72 V battery and 96 V supercapacitor hybrid energy storage system real-time hardware platform has been developed to validate the effectiveness of the proposed energy management control strategy.

A role for embedded instrumentation in real-time hardware assurance and online monitoring against cybersecurity threats

Our 2019 AUTOTESTCON paper [1] introduced the concept of hardware assurance for security and trust that focused on Trojan payloads and triggers and the use of embedded instruments and data collection from those instruments as one of the detection strategies. A Phase I Small Business Innovation Research grant funded the proof of concept of using a COTS board as a sandboxed emulation environment to safely study the attack scenarios associated with various Trojan attacks, and that effort is described in this article.

Motion correction methods for MRS: experts' consensus recommendations.

Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.

Algorithm optimisation and hardware implementation of interprediction mode decision

High efficiency video coding is the most widely used video coding standard. It has higher coding performance compared with its predecessor, H.264, but it also has higher computational complexity. Interprediction is the most computationally intensive part of the entire video encoding process. Selecting the optimal interprediction mode by the rate–distortion cost calculation function requires substantial complex calculation and memory access, thus greatly increasing the difficulty of real-time hardware encoding. This study proposes to replace the traditional complex error square sum calculation with an estimation method for distortion and rate. The estimation of distortion uses the Hadamard-transformed sum of absolute transformation difference instead of the complex calculation of the sum of squared difference, whereas the estimation of rate is obtained by weighting the number of prediction units (PUs). The experiment proves that the proposed interprediction rate–distortion cost calculation model can greatly reduce computational complexity when BD-rate is increased by 3.02%. In hardware implementation, the value of rate can be obtained by indexing the number of PUs, and the resource expenditure is small.

A hybrid fuzzy with feedback integral phase locked loop-based control strategy for unified power quality conditioner

This paper proposes a hybrid fuzzy control strategy comprising both Takagi-Sugeno fuzzy and Mamdani fuzzy along with novel feedback integral phase-locked loop based modified synchronous reference frame for unified power quality conditioner for power quality enhancement in power distribution network. The hybrid fuzzy controller can vary the gain nonlinearly for controlling dc-bus capacitor voltage of unified power quality conditioner, which limits the dc-bus voltage deviations during the load and supply voltage turbulences. Moreover, the proposed novel feedback integral phase-locked loop system is employed to improve the grid synchronization performance of unified power quality conditioner by considering the feedback integral loop and nonlinear adaptive-filter type phase detector approach. As a consequence, the proposed feedback integral phase-locked loop system can extract the positive sequence signal perfectly during power system disturbances. Additionally, the novelty of modified synchronous reference frame control technique relies upon nonintervention with low pass filter as well as high pass filter. Hence, the proposed control technique quickly and accurately extracts the reference signal from the distorted power system. The efficacy of the proposed control technique is justified through a real-time Opal-RT Hardware underneath different states of power system and compared with both conventional modified phase locked loop system and Takagi-Sugeno fuzzy with synchronous reference frame-based control system.

F-DNA: Fast Convolution Architecture for Deconvolutional Network Acceleration

Deconvolutional neural network (DeCNN), such as fully convolutional network (FCN) and generative adversarial network (GAN), has shown great potential in various vision tasks. Convolution and deconvolution, the two major operations of DeCNN, both require real-time hardware acceleration. However, some previous designs for deconvolutions require large memory for overlapped results, while others incur computation imbalance and cause resource underutilization. In this article, we propose an efficient method to convert deconvolutions to convolutions, which enables balanced computations to make full use of processing elements. Based on the fast FIR algorithm, a reconfigurable conv–deconv unit (RCU) with low complexity is designed, which can support various types of convolutions and deconvolutions. By exploiting the computing characteristics of RCUs, a computation-balance scheme is developed to eliminate large memory requirements caused by overlapped results. In addition, a fast convolution architecture for deconvolutional network acceleration (F-DNA) is proposed. The dataflow of F-DNA improves the computation efficiency through input data reuse. The architecture is implemented on Xilinx Virtex-UltraScale, for two typical DeCNNs, DCGAN and FSRCNN. Implementation results show that the proposed design outperforms existing works significantly, particularly in terms of computation efficiency and memory requirements.

Low‐cost hardware‐in‐the‐loop for real‐time simulation of electric machines and electric drive

This study aims to present the discrete models and the methodology to implement real-time simulations of electric machines using a low-cost digital signal processor (DSP). The DC machine and the three-phase induction machine are modelled in real-time using a Texas Instruments DSP TMS28379D, where the discrete models are implemented using C language. A minimum time-step of 1 µs can be achieved for the DC machine and 1.5 µs for the inductions machine in the experimental hardware. To validate the described models and show their precision, they are compared with commercial computational models from PSIM®. In addition, closed-loop speed control strategies are applied to the real-time DSP experimental models, showing perfect concordance with the machine theory. For the DC machine, a speed control strategy with an inner current control loop is applied and for the induction machine, a field-oriented control for the speed control. The proposed real-time simulation hardware has a great potential for low-budget research and educational purposes since it can replace a real machine setup for a very low price, with great accuracy, variable parameters and free from risks, such as accidents or equipment damage. Furthermore, it uses cheap hardware with free software and a high-level programing language.

A Real Time Hardware Co-Simulation of Linearization of Nonlinear Sensor using ANFIS Linearizer

A Real Time Hardware Co-Simulation of Linearization of Nonlinear Sensor using ANFIS Linearizer

Design and implementation of fuzzy logic based modified real-reactive power control of inverter for low voltage ride through enhancement in grid connected solar PV system

In a grid connected solar PV system, during grid faults, increase in grid current, dip in voltage at point of common coupling and overshoot in dc link voltage in the inverter side are observed. The solar PV system gets disconnected from the grid whenever the grid voltage goes below a certain nominal value in order to safeguard the cascaded power converter systems from high currents and over voltages. This paper proposes an intelligent fuzzy based real and reactive power control of inverter for effective Low Voltage Ride Through (LVRT) capability during grid faults. The proposed fuzzy logic based modified inverter real and reactive power control strategy, stabilizes the grid voltage by injecting reactive current and limits the grid over current by reducing active power flow into the grid. To regulate the overshoot in dc link voltage during grid faults, a braking chopper and dynamic braking resistor strategies are included in the proposed LVRT scheme. The performance of the proposed inverter control strategy is studied through simulation of 100 kW grid connected solar PV system in MATLAB/SIMULINK 2018a platform. The algorithm is validated in real time hardware of 1 kW grid connected solar PV system. The simulation and hardware results validate the superior performance of the proposed inverter LVRT control strategy in comparison with other conventional schemes during grid faults.

The airplane trim system – new functionalities

PurposeA standard automatic flight control system – autopilot – will become required equipment of the future aircraft, operating in the common sky. For a specific group of aircraft, they are too expensive and too energy-consuming solutions. This paper aims to present the concept of an automatic flight control system that overcomes those limitations.Design/methodology/approachThe proposed automatic flight control system uses the trim tabs in all prime flight controlling surfaces: elevator, ailerons and rudder, for stabilizing and controlling the steady flights of an aircraft.FindingsThe results of an aeroplane flight controlled with the use of trim tabs simulation tests and remarks have been presented and discussed. The simulation was conducted in real-time hardware in the loop environment. The stabilization of the flight was achieved in performed test scenarios.Originality/valueThe possibility to control an aircraft with coordinated deflections of the trimming surfaces is a beneficial alternate to those currently used and can be recommended for use in the next-generation aircraft.

Real-time simulator and offline/online closed-loop test bed for power system modeling and development

The high complexity of modern power systems forces engineers to design, implement, and test different equipment, precisely. The real-time simulator is one of the best tools for a researcher to test and verify complex power systems in the laboratories. Based on this issue, they can study power system control methods as well as the effects of various models in the partially simulated system. This paper presents a low-cost and versatile power system simulator that is applicable for a wide range of real-time hardware in the loop (RT-HIL) simulations of power systems. To implement the proposed closed-loop simulator, all the essential parts of a power plant, including generator, turbine-governor, excitation system (automatic voltage regulator (AVR), and power system stabilizer (PSS)), grid interconnection and loads are implemented in MATLAB/Simulink environment. To achieve fast and reliable measurement functions, dedicated programs are developed in the LabVIEW real-time and related FPGA programming environments. The data transfer between the MATLAB/Simulink model and LABVIEW and its real-time hardware (PXIe-8133) is accomplished by building a *.dll file as an inbuilt function. A HIL simulation of the excitation system is implemented using a real excitation cabinet to test system capability and also investigate system reaction against disturbances. The performance of the proposed simulator is investigated under various scenarios such as excitation voltage set-point change, the step response of automatic voltage regulator, network three-phase fault, torque step, and step load changes. The close correspondence between HIL and MATLAB/Simulink simulations reveals the validity of the implemented model for the excitation system as well as the effectiveness, flexibility, versatility, applicability, and usefulness of the offline/online closed-loop simulator for power system modeling and development.

Emulation of burst-based adaptive link rates in NetFPGA towards green networking

In recent times, energy consumption in communication media has been increasing drastically. In the literature, energy-saving techniques that enable network devices to enter sleep state or limit the data rate have been proposed to reduce energy costs. In our earlier work, we proposed an energy-saving technique called burst-based adaptive link rate BBALR , the simulation of which assures increased energy savings. In this paper, we have emulated the hardware implementation of BBALR and compared its performance with the outputs of other prominent energy-saving policies based on dynamic link rate adaption. The energy savings are mapped from the measured sleep time and reference power values. We have used NetFPGA as the testbed, which is a research platform for building real-time network hardware prototypes.

Use of Artificial Intelligence & Machine Learning with Deep Learning for Glaucoma Detection in Human Eyes & its Real Time Hardware Implementation

In this paper, a brief introduction to AI, ML and the Eye w.r.t. Deep Learning for Glaucoma Detection and Hardware Implementation is being presented. The result is the outcome of the Post-Graduate project work of the student that is going to be carried out in the second year of the course & this work is just the synopsis that is being framed for the carrying out of the detection of glaucoma disease.

The development, design and characterisation of a scale model Horizontal Axis Tidal Turbine for dynamic load quantification

The paper describes the development and characterisation of three 0.9 m diameter lab-scale Horizontal Axis Tidal Turbines. The blade development process has been outlined and was used to generate a design specification. Each turbine houses instrumentation to measure rotor thrust, torque and blade root bending moments on each blade, in both ‘flapwise’ and ‘edgewise’ directions. A permanent magnet synchronous machine and encoder are integrated to allow for servo-control of the turbine as well as to provide position and rotational velocity measurements, resulting in three turbines that can be individually controlled using speed or torque control. Analogue signals are captured via a real-time operating system and field programmable gate array hardware architecture facilitating sample rates of up to 2 kHz. Results from testing the pilot turbine at three differing facilities during the development process are presented. Here good agreement, less than 7% variation, was found when comparing the testing undertaken at various flume and tow tank facilities. Lastly, the findings of a test campaign to characterise the performance of each of the three turbines are presented. Very good agreement in non-dimensional values for each of the three manufactured turbines was found.

On Stability of PV Clusters With Distributed Power Reserve Capability

This article presents a stability analysis of photovoltaic (PV) clusters forming a dc microgrid with a distributed power reserve capability. The PV clusters feature flexible PV power generation under fluctuating ambient conditions via the distributed power reserve control. The PV set-points are adjusted based on grid operator command that is accomplished in a decentralized manner. Stability analysis of the PV clusters has been carried out to determine the safe operation regions of the PV units with respect to operation set-point adjustments. The proposed stability analysis approach considers all the characteristics of the PV subsystems. The stability analysis demonstrates that the PV cluster architecture is stable as far as the dc bus stray resistance has a finite value. Also, the grid-interfacing converter must operate in input current control mode (CCM) instead of input voltage control mode (VCM) for better stability margin. Several case studies are emulated on an IEEE 5-Bus system using real-time controller hardware in the loop (C-HIL) to verify the stability and the theoretical expectations of the proposed system.

Performance Evaluation of a Real-Time Seismic Detection System Based on CFAR Detectors

In this research work, we present a novel application specific Ordered cell averaging-CFAR (OCA-CFAR) detector for anthropogenic seismic events. We investigate the use of geophones and real-time hardware for the detection of moving targets such as vehicle, human footsteps and activities such as hammering on the ground. We present here energy assisted variants of Constant False Alarm Rate (CFAR) detector implemented on a real-time embedded platform to obtain detection cues of the ground seismic source. The OCA-CFAR algorithm has been modified in terms of window length and position of Cell Under Test (CUT), suitably adapted to the nature of seismic events engendered from moving vehicles and humans. The proposed detector has been compared with the popularly known variants such as Cell Averaging-CFAR, Least Of-CFAR, Greatest Of-CFAR and Ordered Statistics-CFAR for probabilities of false alarm ranging from 1e-1 to 1e-5. The proposed method shows an increased detection rate with minimized false alarms on the field trials and a dataset containing the seismic signature of human activities and some of civilian vehicles.

Data-Driven Framework to Model Identification, Event Detection, and Topology Change Location Using D-PMUs

We introduce a model-free scheme that is capable of identifying topology changes in distribution networks using data from distribution-level phasor measurement units (D-PMUs). To do so, we use algebraic tools of behavioral system theory to develop easy-to-implement algorithms. The intrinsic difficulties associated with D-PMU measurements and their proposed solution are discussed as an additional challenge for the implementation of the proposed algorithms. Experimental results using real D-PMUs and laboratory real-time hardware are provided as a proof of concept using a standard IEEE-test node feeder.