Emulation Results Research Articles

Power-hardware-in-the-loop validation of air-source heat pump for fast frequency response applications

This paper presents a standard power-hardware-in-the-loop testing platform to simulate detailed air-source heat pump dynamics based on well-established modeling knowledge. Distributed air-source heat pumps can be potentially used for fast frequency response. However, using air-source heat pumps for rapid modulation cannot be based on the current assumptions of linear speed-power transient characteristics developed for low-speed temperature control applications. Customized setups with experimental validation options are needed to design the new fast frequency response compatible heat pump controllers. Most power system laboratories struggle to build a customized air-source heat pump, which hinders research progress. The proposed platform is relatively universal, can fit different heat pumps with minor modifications, and can be implemented on standard PHIL emulators in power system laboratories. Emulation results, real-time implementation details, and model complexity metrics are presented to assist in the transference of the setup to other laboratories.

A distribution network traveling wave localization method based on CEEMD-DEO3S

A traveling wave head calibration way according to Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Three-point Symmetric Differential Energy Operator (DEO3S) is proposed to address the issues of noise interference and difficulty in precise positioning in local area network wave positioning. The way filters out low-frequency bandwidth in the fault data by performing CEEMD decomposition on the signal and then uses the DEO3S algorithm to define the time of arrival of the fault wave head. The emulation results indicate the proposed way can precisely extract wave heads even in the presence of a large amount of noise interference, and has good robustness and accuracy. Compared with traditional traveling wave positioning methods, it has obvious advantages and further improves the accuracy and reliability of traveling wave positioning.

The perpetual need of randomized clinical trials: challenges and uncertainties in emulating the REDUCE-AMI trial.

Trial emulations in observational data analyses can complement findings from randomized clinical trials, inform future trial designs, or generate evidence when randomized studies are not feasible due to resource constraints and ethical or practical limitations. Importantly, trial emulation designs facilitate causal inference in observational data analyses by enhancing counterfactual thinking and comparisons of real-world observations (e.g. Mendelian Randomization) to hypothetical interventions. In order to enhance credibility, trial emulations would benefit from prospective registration, publication of statistical analysis plans, and subsequent prospective benchmarking to randomized clinical trials prior to their publication. Confounding by indication, however, is the key challenge to interpreting observed intended effects of an intervention as causal in observational data analyses. We discuss the target trial emulation of the REDUCE-AMI randomized clinical trial (ClinicalTrials.gov ID NCT03278509; beta-blocker use in patients with preserved left ventricular ejection fraction after myocardial infarction) to illustrate the challenges and uncertainties of studying intended effects of interventions without randomization to account for confounding. We furthermore directly compare the findings, statistical power, and clinical interpretation of the results of the REDUCE-AMI target trial emulation to those from the simultaneously published randomized clinical trial. The complexity and subtlety of confounding by indication when studying intended effects of interventions can generally only be addressed by randomization.

Multidomain Device-Level Fuel-Cell Modeling and Real-Time Hardware Emulation for Marine Research Vessel Power System

Marine research vessels (MRVs) are redesigned and refurbished to meet higher energy conversion efficiency and modular integration schemes while conforming to stronger environmental regulations. The proton exchange membrane fuel cell (PEMFC) is currently regarded as a potential power source for marine transportation applications due to its advantages of stability, sustainability, and zero emissions. This paper proposes a hierarchical scheme for the real-time hardware emulation of the MRV’s power system and a comprehensive multi-domain model for PEMFCs. The PEMFC model is presented in the electrochemical, hydration, and thermal domains by ordinary differential equations considering the interactions and dynamics of each domain. Meanwhile, the multi-domain PEMFC model considers the implications of the fluctuating supply of the onboard hydrogen circulation system. Moreover, the dynamics of the lithium-ion battery stacks are represented by an equivalent circuit model which considers heat flux phenomena. In the case study, the DC-AC grid of the MRV’s power system and electric propulsion system is configured using extensive electrification technology with an average model. The real-time hardware emulation is conducted on the Xilinx® UltraScale+™ VCU118 FPGA platform to execute the device-level and system-level behavior transients of the MRV. The results of the real-time hardware emulation have been validated against the full-scale hybrid MRV power system emulation over a wide operating range.

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

A Radiation Source Positioning System Based on GEO Satellites TDOA/FDOA Measurements

Traditional passive satellite positioning systems are facing problems such as construction difficulty, high operation and maintenance costs, and positioning range limits caused by quantity of launched satellites, due to gradually scarce earth’s orbit resources. Therefore, a radiation source positioning system design scheme based on quilted GEO communication satellites is proposed to meet with those problems. The proposed positioning system is able to intelligently choose one of three modes (three-satellites TDOA mode, dual-satellites FDOA/TDOA mode, or single-satellite mode), on the basis of searching satellites database, to position unknown radiation source. The positioning result is corrected using a known reference radiation station. To improve signal detection performance, a technology of co-channel interference cancellation is proposed when unknown radiation source signal forwarded by neighboring satellite is interference badly by its communication signal. Results of emulation shows， the proposed system can effectively locate unknown radiation sources within a range of 1000 kilometers, its positioning accuracy can reach 10 kilometers.

VxH: A Systematic Determination of Efficient Hierarchical Voxel Structures

Three-dimensional (3D) maps with many millions to billions of points are now used in an increasing number of applications, with processing rates in the hundreds of thousands to millions of points per second. In mobile applications, power and energy consumption for managing such data and extracting useful information thereof are critical concerns. We have developed structures and methodologies with the purpose of minimizing memory usage and associated energy consumption for indexing and serialization of voxelized point-clouds. The primary source of points in our case is airborne laser scanning, but our methodology is not restricted to only this setting. Our emulated results show a memory usage reduction factor of roughly up to 200× that of Octree/Octomap and a file size reduction factor of up to 1.65× compared with the predominating compression scheme for airborne Lidar data, LASzip. In addition, our structures enable significantly more efficient processing since they are included in a hierarchical structure that captures geometric aspects.

Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes

The soft decision decoding algorithm for cyclic codes, especially the maximum likelihood (ML) decoding algorithm, can obtain significant performance superior to that of algebraic decoding, but the complexity is much higher. To deal with this problem, an improved soft decision decoding algorithm based on a cyclic information set and its efficient implementation architecture are proposed. This algorithm employs the property of the cyclic codes to generate a series of cyclic information sequences by circularly shifting, constructing the cyclic information set. Then, a limited number of candidate information sequences are efficiently generated using an iterative computation method, and the candidate codewords are generated using the very concise encoding method of the cyclic codes. Furthermore, the efficient hardware architecture based on systolic arrays is also proposed to generate candidate information sequences and to select the optimal candidate codewords. An emulation platform is constructed to verify the error correction performance and to determine the optimal decoder parameters. Emulation results indicate that, with appropriate parameter selection, the proposed decoding algorithm can achieve a bit error rate approaching the ML performance while maintaining low complexity.

Faster-Than-Real-Time Hardware Emulation of Extensive Contingencies for Dynamic Security Analysis of Large-Scale Integrated AC/DC Grid

The rapid expansion of modern power systems has brought a tremendous computational challenge to dynamic security analysis (DSA) tools which consequently need to process extensive contingencies. In this work, hardware emulation is investigated to accelerate the DSA solution of a large-scale AC/DC system deployed on the field-programmable gate arrays (FPGAs) faster-than-real-time (FTRT) execution. Electromagnetic transient (EMT) modeling of the DC grid is conducted since the fast converter dynamics require a small time-step for accuracy; in contrast, the transient stability (TS) simulation is applicable to the AC grid which tolerates a much larger step size. To coordinate the 2 different types of simulation, an interface based on dynamic voltage injection is proposed to integrate the AC and DC grids, in addition to maintaining a low hardware latency. An emulation platform consisting of multiple FPGA boards is established so that with a proper allocation it has a sufficient capacity to accommodate the system under study which has 6 ACTIVSg 500-bus systems interconnected by a 6-terminal DC grid. The efficacy of the proposed FTRT hardware emulation platform is demonstrated by 2 case studies with more than 5500 contingencies analyzed in total, where an FTRT ratio of more than 208 is achieved for the hybrid AC/DC grid, while it is over 277 times for a single 500-bus system. Furthermore, the FTRT dynamic emulation results, including the security indices, are validated by the simulation tool DSATools/TSAT.

Improvement and Simulation of PV Power Generation Control Algorithm Based on MPPT

As the traditional PV MPPT algorithm cannot meet both the tracking speed and steady-state accuracy, a new solution idea is provided to improve the traditional conductivity increment method on basis of the slope of the PV cell output P-V curve. The algorithm dynamically changes the step size by combining duty cycle and power and exponential functions, and rapidly adapts to variation in the external environment, solving the slow tracking speed and low steady-state accuracy when the traditional conductance incremental method is subject to sudden changes in illumination. The emulation results indicate that the modified algorithm and the alterable-step increment conductivity method can be simulated in the same environment using MATLAB/Simulink software. The emulation results indicate that the modified algorithm, compared with the traditional algorithm in tracking the MPPT speed and amplitude, can effectively reduce the tracking time with high steady-state accuracy.

Ramanujan Fourier Mode Decomposition and Its Application in Gear Fault Diagnosis

As an important part of rotating machinery, gear is easy to appear some unexpected fault states, and its fault diagnosis is very important. Fourier decomposition method (FDM) is a common method for gear fault diagnosis, but the noise robustness, period recognition, and extraction capabilities of FDM are unsatisfactory. Based on this, in this article, Ramanujan Fourier mode decomposition (RFMD) method is proposed. The RFMD not only has a complete mathematical theory foundation but also has an excellent ability to identify and extract periodic components. Emulational and experimental results of planetary gearbox show that the RFMD method has good noise robustness and can accurately extract gear fault characteristic information. Thus, it is an effective gear fault diagnosis method.

Toward Programmability of Radio Resource Control Based on O-RAN

Abstract Open Radio Access Network (O-RAN) is a concept that aims at embedding intelligence at the network edge and at disaggregating of network functionality from the hardware. The paper studies how the O-RAN concept can be used for optimization of radio resource management. The research focuses on adaptive radio resource allocation based on predictions of device activity. For narrowband devices which send sporadically small volumes of data, a feature is defined which enables a device with no activity for a short time to suspend its session and to resume it moving in active state. Dynamic configuration of the inactivity timer based on prediction of device activity may further optimize radio resource allocation. The paper studies an O-RAN use case for dynamic radio resource control and presents the results of emulation of the RESTful interface defined between the O-RAN non-real-time and near real-time functions.

Synthesis Design of Dual-Band Filtering Power Dividers Based on E-Shape Resonators

In this study, a comprehensive design of dual-band filtering power dividers (FPDs) with arbitrary phase distribution is presented. With a series of analytical equations for the direct synthesis design, the proposed method shows its great capacity of designing dual-band FPDs with any pre-specified responses, arbitrary phase distribution, and high isolation. To verify this design method, a demonstrated dual-band FPD with 90° phase differences between two outputs based on E-shape resonators has been implemented. The emulational results coincide with the measured results well, showing the feasibility of the proposed method.

Model Order Reduction for Real-Time FPGA-Based Finite Element Transient Simulation of Three-Phase Transformer

The finite element method (FEM) is commonly used in modeling the electromagnetic field of transformers with high accuracy; however, the computation time of the FEM is prohibitively high for real-time simulation due to high model order. Reducing the model order is helpful to improve the computational efficiency of the FEM. The proper orthogonal decomposition (POD) is an efficient method to reduce the linear model order but encounters difficulties with nonlinear models. In this paper, a model order reduction (MOR) method based on the combination of POD and the transmission-line modeling (TLM) is proposed to reduce the nonlinear finite element model order and computation time. The TLM method is used to separate the nonlinear components from the model, and then the POD is used to reduce the linear domain order of the model. The nonlinear components obtained by TLM can be solved in parallel on the field programmable gate array (FPGA) to improve computational efficiency further. This paper has studied the transient states of a three-phase transformer with the current excitation and field-circuit coupling. The real-time hardware emulation results are validated by results from offline simulation using Comsol®.

Итеративный алгоритм посимвольного приема OFDM сигнальных конструкций при наличии сосредоточенных по спектру помех.

The article describes the results of an iterative algorithm for symbol-by-symbol receiving signal structures based on signals with orthogonal frequency multiplexing, pseudorandom sequences and error-correcting low-density codes. The results of emulation this iterative reception algorithm for the signal structures in the presence of channel concentrated interferences are given and the stability of signal structures to the distorting influence of the considered class of interference is shown. It is shown that a signal design based on a low-density code in combination with an iterative algorithm for symbol-by-symbol reception is more effective with respect to noise immunity compared to a convolutional code in combination with a Viterbi reception algorithm implementing the maximum likelihood rule.

Intelligent Service Orchestration in Edge Cloud Networks

The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform.

A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks

With the globalisation of the multimedia entertainment industry and the popularity of streaming and content services, multicast routing is (re-)gaining interest as a bandwidth saving technique. In the 1990’s, multicast routing received a great deal of attention from the research community; nevertheless, its main problems still remain mostly unaddressed and do not reach the acceptance level required for its wide deployment. Among other reasons, the scaling limitation and the relative complexity of the standard multicast protocol architecture can be attributed to the conventional approach of overlaying the multicast routing on top of the unicast routing topology. In this paper, we present the Greedy Compact Multicast Routing (GCMR) scheme. GMCR is characterised by its scalable architecture and independence from any addressing and unicast routing schemes; more specifically, the local knowledge of the cost to direct neighbour nodes is enough for the GCMR scheme to properly operate. The branches of the multicast tree are constructed directly by the joining destination nodes which acquire the routing information needed to reach the multicast source by means of an incremental two-stage search process. In this paper we present the details of GCMR and evaluate its performance in terms of multicast tree size (i.e., the stretch), the memory space consumption, the communication cost, and the transmission cost. The comparative performance analysis is performed against one reference algorithm and two well-known protocol standards. Both simulation and emulation results show that GCMR achieves the expected performance objectives and provide the guidelines for further improvements.

Towards Ultra Reliable Low Latency Multipath TCP for Connected Autonomous Vehicles

Connected Autonomous Vehicles (CAVs) are Not-So-Futuristic. CAVs will be highly dynamic by intelligently exploiting multipath communication over several radio technologies, such as high-speed WiFi and 5G and beyond networks. Yet, the likelihood of data communication loss can be very high and/, or packets arrive at the destination not in correct working order due to erratic and mixed time-varying wireless links. Furthermore, the vehicular data traffic is susceptible to loss and delay variation, which recommends the need to investigate new multipath TCP (MPTCP) protocols for ultra-reliable low latency communication (URLLC) over such heterogeneous networks while reassuring CAVs' needs. We undertake the challenge by jointly considering network coding and balanced linked adaptation for performing coupled congestion control across multiple wireless paths. Consequently, the proposed low delay MPTCP framework for connecting autonomous vehicles is efficient and intelligent by design. We conduct a rigorous convergence analysis of the MPTCP design framework. In summation, we provide a detailed mathematical study and demonstrate that the latency penalty for the URLLC-MPTCP developed over these networks becomes negligible when considering the possible benefits that multiple network convergence could offer. Our extensive emulation results demonstrate all these lucrative features of URLLC-MPTCP.

Cognitive and autonomous QoT-driven optical line controller

In the direction of disaggregated and cognitive optical networks, this work proposes and experimentally tests a vendor-agnostic optical line controller architecture capable of autonomously setting the working point of optical amplifiers to maximize the capacity of a ROADM-to-ROADM (reconfigurable optical add–drop multiplexer) link. From a procedural point of view, once the equipment is installed, the presented software framework performs an automatic characterization of the line, span by span, to abstract the properties of the physical layer. This process requires the exploitation of monitoring devices such as optical channel monitors and optical time domain reflectometers, available, in a future perspective, in each amplification site. On the basis of this information, an optimization algorithm determines the working point of each amplifier to maximize the quality of transmission (QoT) over the entire band. The optical line controller has been experimentally tested in the laboratory using two different control strategies, achieving in both cases a homogeneous QoT for each channel close to the maximum average and an excellent match with respect to emulation results. In this framework, the Gaussian noise simulation in Python (GNPy) open source Python library is used as the physical model for optical propagation through the fiber, and the covariance matrix adaptation evolution strategy is used as an optimization algorithm to identify properties of each fiber span and to maximize the link capacity.

New Structure of a Special Pentaprism Designed by Simulation for Orthogonal Phase Microimaging

Quantitative phase imaging (QPI) has a very important application in the nondestructive imaging of biological cells. In view of the complex structure and various devices of imaging optical structure in traditional dual-channel orthogonal QPI, a new structure of a special pentaprism is designed as the main body of the orthogonal dual-channel phase microscopic imaging based on simulation design. In this dual-channel orthogonal QPI light structure, a hollow channel is designed as a sampling channel inside the pentaprism, a single light source through deflection of the pentaprism can form orthogonal dual-channel beams, with optical lens after diffraction modulation can form orthogonal dual-channel interference, the orthogonal dual-channel wrapped phase microscopic images are imaged on the sensors. The emulational and experimental results show that this design greatly simplifies the optical structure, uses very few optical devices, and the imaging path structure is compact and stable. With the use of single light source, the imaging quality of the dual light path has been significantly improved, which can provide a new technical basis for the improvement of biological cell (3-D) imaging technology and its instrument advancement.