Off-line Simulation Research Articles

Dynamic Parameter Identification of a Human-Exoskeleton System With the Motor Torque Data

The objective of this paper is to identify dynamic parameters of the human-exoskeleton system (HES) with the motor torque data considering modeling the passive elastic joint torque of the user. Based on a lower limb exoskeleton prototype we recently developed, the dynamics of the joint actuators and two degrees of freedom link-based swing leg are firstly modeled. The passive elasticity of the human joint is modeled by double-exponential equations. Systematic experiments are then conducted, during which the motor current is measured to compute the motor torque for identification. Finally, the dynamic parameters of the joint actuators, the exoskeleton leg and the human-exoskeleton leg are successively identified. The identification is implemented through the off-line inverse dynamic model simulation with the measured motor torque data. A consistent tendency is found between the simulated motor torque after identification and the measured one. This demonstrates the validity of the identification method proposed. This paper provides a systemic method of identifying the HES with the motor torque data and enriches the experimental proof that the double-exponential model is feasible to describe the passive elastic joint torque.

Model Reference Adaptive System based Apparent Power Sharing in Inverter based Microgrids

Power sharing amongst sources can be improved in an islanded microgrid by utilizing the information of the network’s impedance. In this paper, an impedance based model reference adaptive system (Z-MRAS) scheme is proposed to estimate the impedance angle for converter based distributed generators (DGs) in the islanded mode. The focus is to enhance the proportional apparent power sharing between the DGs operated using generalized droop control (GDC). The performance of GDC depends on the effective tie-line impedance between the DGs and the proposed MRAS scheme provides the feedback of the estimated impedance angle. The accuracy of the impedance angle estimation is established for various tie-line <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X/R$</tex-math></inline-formula> ratios, and this results in enhanced power sharing performance for balanced, harmonic, unbalanced, dynamic and constant power load cases as well as parallel line and underground cable cases. The proposed method is validated through off-line simulations in MATLAB and controller hardware in loop (CHIL) using real time simulator (Opal-RT) including cases of meshed networks and DG plug and play for a modified CIGRE network.

Machine Learning Based Transient Stability Emulation and Dynamic System Equivalencing of Large-Scale AC-DC Grids for Faster-Than-Real-Time Digital Twin

Modern power systems have been expanding significantly including the integration of high voltage direct current (HVDC) systems, bringing a tremendous computational challenge to transient stability simulation for dynamic security assessment (DSA). In this work, a practical method for energy control center with the machine learning (ML) based synchronous generator model (SGM) and dynamic equivalent model (DEM) is proposed to reduce the computational burden of the traditional transient stability (TS) simulation. The proposed ML-based models are deployed on the field programmable gate arrays (FPGAs) for faster-than-real-time (FTRT) digital twin hardware emulation of the real power system. The Gated Recurrent Unit (GRU) algorithm is adopted to train the SGM and DEM, where the training and testing datasets are obtained from the off-line simulation tool DSAToolsTM/TSAT®. A test system containing 15 ACTIVSg 500-bus systems interconnected by a 15-terminal DC grid is established for validating the accuracy of the proposed FTRT digital twin emulation platform. Due to the complexity of emulating large-scale AC-DC grid, multiple FPGA boards are applied, and a proper interface strategy is also proposed for data synchronization. As a result, the efficacy of the hardware emulation is demonstrated by two case studies, where an FTRT ratio of more than 684 is achieved by applying the GRU-SGM, while it reaches over 208 times for hybrid computational-ML based digital twin of AC-DC grid.

Design, Fabrication and Test of a High- Temperature Superconducting Linear Synchronous Motor Mover Magnet Prototype for High-Speed Maglev

High-temperature superconducting linear synchronous motors (HTS-LSMs) have many advantages, such as high thrust density, high efficiency, large electromagnetic gap, and liquid-helium-free refrigeration, because of the high operating temperature and good mechanical tolerance of high-temperature superconductors. Therefore, HTS-LSMs have broad application prospects in the field of high-speed maglev propulsion system. To study the dynamic stability of an HTS-LSM, this work aims at designing, fabricating and testing an HTS magnet as the mover magnet of an HTS-LSM. The HTS mover magnet is a monopole HTS magnet, and it is designed according to electromagnetic, structural, and thermal properties and the measurement system. A thermal model and structural dynamics model were constructed to analyze the dynamic refrigeration performance and structural dynamics characteristics of the HTS magnet. The validation of these models was verified by experimental results. The HTS coils in the HTS mover magnet were fabricated using epoxy impregnation with primary and secondary curing processes. Static tests and dynamic tests were performed to comprehensively study the characteristics of the HTS magnet. The magnet could be cooled to below 20 K and could be excited to 246 A with a certain temperature margin. An electromagnetic simulator was designed and manufactured to realize the off-line simulation of the actual operation of the HTS-LSM. The dynamic experimental results show that the HTS magnet could withstand a vibration environment of up to 18 gRMS without quenching and structural damage. This study provides useful information for the design and application of an HTS-LSM.

Indirect Shared Control for Cooperative Driving Between Driver and Automation in Steer-by-Wire Vehicles

It is widely acknowledged that drivers should remain in the control loop before automated vehicles completely meet real-world operational conditions. This paper presents an “indirect shared control” framework for steer-by-wire vehicles, which allows the control authority to be continuously shared between the driver and automation through an weighted-input-summation method. A “best-response” driver steering model based on model predictive control (MPC) for indirect shared control is proposed. Unlike any conventional driver model for manual driving, this model assumes that drivers can learn and incorporate the controller strategy into their internal model for predictive path following. The analytic solution to the driver model is provided to enable off-line simulations. A driving-simulator experiment was conducted to demonstrate the advantages of the indirect shared control system in a highway lane-keeping task. The result showed that the proposed indirect shared control method was effective to improve the subjects’ lane-keeping performance and reduce steering control effort. The proposed driver steering model was also validated by the experiment data, which produced a smaller prediction error than the conventional MPC driver model.

Research on real time simulation modeling method of large scale MMC electromagnetic transient

Modular multilevel converter (MMC) contains a large number of power electronic switching devices. The modeling method based on switching circuit model needs a lot of resources and the simulation speed is slow, so it is difficult to realize large-scale real-time simulation of electromagnetic transient. A MMC electromagnetic transient numerical modeling method based on ideal transformer model (ITM) is presented. Firstly, the MMC system is divided into the main circuit network and the sub module group network by ITM method, and the error caused by decoupling delay in serial and parallel real time simulation is compensated respectively by interpolation prediction and advanced interpolation prediction. Secondly, the capacitor in sub module is discreted respectively by trapezoidal integration method, backward Euler method and Gear-2 method. Based on the above numerical integration, the difference equations of capacitance voltage, capacitance current and output voltage of half bridge and full bridge sub modules are derived. Then, in order to improve the calculation speed, a simplified numerical model of half bridge and full bridge sub module based on switching function is proposed. Finally, the MMC based on switching circuit model runs off-line simulation in the simulation software, and the above MMC numerical modeling method runs real-time simulation in Speedgoat real-time simulator. The off-line and real-time simulation results of the MMC numerical modeling method and the switching circuit model are compared. And the simulation results verify the feasibility and effectiveness of the above MMC numerical modeling method in real time simulation.

Optimization of Water Distribution System Operation with Multiple Tanks and Pumps: Application for Asmara, Eritrea’s Water Supply System

Pump energy consumption is the major operational cost of water distribution systems (WDS). Defining better pump operation policies can reduce the cost and the enhance service provided. The nonlinear hydraulic behavior of WDS makes the pump scheduling problem complex, especially in large WDS in which several tanks and pumping stations coexist. The nonlinear hydraulic behavior of WDS is captured using numerical simulation. However, the required energy calculations were computed from the flow rate and head increase after the pumping stations. There are many optimization algorithms for the reduction of the operational cost. The majority of these algorithms are applied to hypothetical case studies with reduced feasible solution spaces. When the search space becomes large and many alternatives (pumps combinations) are possible, most of the proposed algorithms fail. Thus, the application of these algorithms in real case studies is very limited, and water utility managers still rely on human expertise to schedule pump operation rather than on automatic scheduling. This paper proposes the use of a genetic algorithm for large WDS with a many tanks and pumps. Offline hydraulic simulations allow the definition of water level bounds in the tanks. The combination of the pumps is determined a priori and the optimization algorithm is run in a reduced search space. These preparations allow the genetic algorithm to identify quickly the optimal policy and reduce the cost of energy consumption. The proposed framework was applied to the WDS of the city of Asmara, Eritrea, with 12 tanks and 9 pumping stations. The results showed that the tanks controlled by pumps worked perfectly and the water level was kept within the defined ranges. However, the constraints were not fulfilled for uncontrolled tanks. This application showed the limitations of methodologies proposed in the literature, and it is suggested that further techniques be found to schedule the pump working policies of large WDS while reducing the violations of constraints.

Precise current sharing and decentralized power management schemes based on virtual frequency droop method for LVDC microgrids

One of the challenging issues in DC microgrids attracted by many researchers is the current sharing enhancement among distributed generations (DGs) with different rated currents and/or lines impedances. In this paper, DG topology is considered comprising photovoltaic (PV) system as power resource and battery energy storage system (BESS). By inspecting the frequency droop control of synchronous generators, a unique corrective sinusoidal term is added to the output voltage in DC-DC converters of each DG with purpose of current sharing control between DC DGs based on their nominal capacities. This virtual decentralized current sharing scheme named as the frequency droop method (FDM) can accurately regulate the point of common coupling (PCC) voltage without any communication link. Moreover, a decentralized power management scheme with different operating modes for PV system and BESS is proposed in this paper based on the state of charge of BESS and the frequency obtained from FDM scheme. The performance of FDM and power management schemes is investigated by time domain off-line and real-time simulations in MATLAB/SIMULINK environment. Seven case studies are implemented considering the effects of load and solar irradiance variations under equality and inequality of lines impedances as well as rated currents, mode transition, and plug-and-play ability. By applying the proposed control schemes, the purposes of precise current sharing and DC-link voltage regulation are achieved smoothly with an acceptable transient compared to an existing power sharing method. Also, FDM scheme is more reliable than available methods due to its independence on line impedance.

The Off-Line Simulation on Measuring through Software PC-DMIS CAD++ V4.3

The article analyses and evaluates the ever-important topic of assessing geometric deviation of tolerated formations related to bases with the usage of coordinate measuring machines. The basic system for off-line simulation consists of the coordinate planes of a component’s coordinate system. At the beginning of the measurement, the coordinate system is created by the “3–2–1” alignment. Due to production deviations in real surfaces of the component, each measurement generates mutually different coordinate systems, which is well proven by the experiment on measuring with a coordinate measuring machine DEA Global Performance 12.22.10. An integral part of the article is also the quantification of geometric deviations of ideal tolerated formations related to bases, the estimate of the uncertainty of measurement arising from the placement of points in defining the base system, and the effect of such uncertainty upon the interval of satisfactory values in conformity with the STN EN ISO 14253-1 technical standard. The article also includes a proposal measure in order to ensure the reproducibility of defining the mutual position of coordinate systems.

Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep deterministic policy gradient (DDPG). We show that a straightforward implementation of DDPG converges slowly, has a poor quality-of-service (QoS) performance, and cannot be implemented in real-world 5G systems, which are non-stationary in general. To address these issues, we propose a theoretical DRL framework, where theoretical models from wireless communications are used to formulate a Markov decision process in DRL. To reduce the convergence time and improve the QoS of each user, we design a knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the scheduler design problem, such as the knowledge of the QoS, the target scheduling policy, and the importance of each training sample, determined by the approximation error of the value function and the number of packet losses. Furthermore, we develop an architecture for online training and inference, where K-DDPG initializes the scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off-line simulations and non-stationary real-world systems. Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing $30\% \sim 50\%$ packet losses). Experimental results show that with off-line initialization, our approach achieves better initial QoS than random initialization and the online fine-tuning converges in few minutes.

Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations

A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized Decomposition (PGD) is employed to simulate a Permanent-Magnet Synchronous Motor (PMSM). The result is a virtual chart allowing real-time evaluation of the magnetic vector potential as a function of the operation point of the motor, or even as a function of constructive parameters, such as the remanent flux in permanent magnets. Currently, these solutions are highly demanded by the industry, especially with the recent developments in the Electric Vehicle (EV). In this framework, standard discretization techniques require highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.

FPGA-based Adaptive Variable Step MPPT Real Time Simulation for Photovoltaic Systems

In order to obtain more output power, maximum power point tracking (MPPT) control technology is usually designed to track the maximum power of photovoltaic (PV) array. The traditional power disturbance observation method tends to cause power oscillation loss, so an adaptive variable step algorithm is presented to balance the tracking accuracy and speed of PV system. Different from off-line simulation, the overall model is built on FPGA to complete the real time digital realization of MPPT control for PV system. Finally, the model is simulated and verified by ModelSim software. The experimental results show that the system can track the maximum power more quickly and accurately in real time.

An Improved Energy Management Strategy for 24t Heavy-Duty Hybrid Emergency Rescue Vehicle With Dual-Motor Torque Increasing

Emergency rescue vehicles are special equipment for fire, flood, earthquake and other disasters. They need to have high efficiency and high mobility under various terrain and road conditions. The fuel economy of emergency rescue vehicles is very poor in climbing and complex terrain due to the heavy weight of vehicle chassis and upper loading. The dual-motor torque increasing hybrid power system designed in this paper greatly improves the dynamic performance and mobility of emergency rescue vehicles, and the use of dual-motor drive in steep slopes and complex terrain can avoid the engine working in low efficiency area. In order to further improve the fuel economy of vehicles under various working conditions, a fuzzy logic control strategy based on improved particle swarm optimization (PSO) is proposed in this paper. The torques of engine and dual-motor are reasonably distributed under different working conditions. A novel fuzzy parameter optimization mode is designed, which reduces the workload of optimization calculation and ensures the optimization accuracy. The off-line simulation and hardware-in-the-loop experiments are carried out for the dual-motor torque increasing hybrid power system model and the proposed improved energy control strategy. The results show that the fuzzy logic control strategy optimized by simulated annealing particle swarm optimization algorithm (SimuAPSO-FLC) has the best effect and meets the requirements of high efficiency, high mobility and stability of the emergency rescue vehicle.

On-Line Determination of Salient-Pole Hydro Generator Parameters by Neural Network Estimator Using Operating Data (PEANN)

A novel application of Artificial Neural Network (ANN) to estimate and track Hydro Generator Dynamic Parameters using online disturbance measurements is presented within this paper. The data for training ANN are obtained through off-line simulation of the generators modelled in a one-machine-infinite-bus environment using the parameters sets that are representative of practical data. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANN are organized in coordination with the results obtained from the observability analysis of synchronous generator dynamic parameters in its dynamic behaviour. A collection of 10 ANNs with similar input patterns and different outputs are developed to determine a set of dynamic parameters. The trained ANNs are employed in a real-time operational environment for estimating generator parameters using online measurements acquired during disturbance conditions. The ANNs are employed and tested to identify generator parameters using online measurements obtained during different disturbances. Simulation studies demonstrate the ability of the ANNs to accurately estimate dynamic parameters of hydro-generators. The results also show the impact of test conditions on the accuracy degree of estimation for these parameters. The optimal structure of ANNs is also determined to minimize the error in estimating each dynamic parameter.

Damping of Subsynchronous Control Interactions in Large-Scale PV Installations Through Faster-Than-Real-Time Dynamic Emulation

Large-scale photovoltaic (PV) power plant has witnessed a dramatic increase in the integration into transmission and distribution network, manifesting subsynchronous control interaction (SSCI) when the host grid is weak. In this work, the oscillation modes of a typical PV network are analyzed, and a faster-than-real-time (FTRT) emulation is proposed for predicting the SSCI and consequently mitigating its impacts on AC grid by taking the effective active/reactive power control action. The electromagnetic transient (EMT) simulation is utilized to model the PV panels and converter stations to reflect the actual dynamic process. Meanwhile, the AC grid undergoes transient stability (TS) simulation to obtain a high speed up over real-time, and consequently, a power-voltage interface is adopted for the coexistence of different simulation methods. The reconfigurability and parallelism of the field-programmable gate arrays (FPGAs) enable the EMT-TS co-emulation strategy to run concurrently. With a remarkable 122 FTRT ratio, the proposed hardware emulation can provide an effective solution before the SSCI causes serious disruption following its detection. The hardware emulation results are validated by the off-line simulation tool Matlab/Simulink® and TSAT® in the DSATools™ suite.

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy.

This paper addresses the energy consumption of distillation process via an actuator, which is a challenging problem in process industries. Precise control action would enhance energy consumption and improve the productivity. This paper is an experimental validation of EPC-PI control algorithm and analysis of distillate purity of a lab-scale distillation column. The PI control scheme uses closed-loop data of extended predictive controller (EPC) that has been performed through off-line simulation. The performance of control method is compared with different schemes such as Hägglund’s one-third rule and Skogestad’s overshoot method. The issue of integral windup in the multivariable process is addressed in the aspect of optimal energy consumption. The energy consumption calculations are made with respect to power utility of actuators throughout the process. The distillate product of post-controller implementation is processed to qualitative analysis using UV spectroscopy. Performance index is carried out via integral time absolute error (ITAE) by perturbing plant parameters up to 30% uncertainty.

Forecasting coastal overtopping at engineered and naturally defended coastlines

As sea level rises and development of the coastal zone continues, coastal flooding poses an increasing risk to coastal communities. Wave runup can contribute many meters to the vertical reach of the sea, especially on steep gravel beaches, and wave overtopping is a key contributor to coastal flooding along coastlines exposed to energetic wave conditions. However, operational forecasting of wave overtopping has rarely been attempted due to the need for high-resolution inshore water levels and wave conditions, up-to-date coastal profile and sea defence information, and availability of models or formulae that can robustly predict overtopping for a range of coastal profile types. Here, we have developed and tested an efficient forecasting system for providing operational warnings up to three days in advance for the entire 1000 km coastline of southwest England, called SWEEP-OWWL, which is capable of predicting wave runup elevation and overtopping volumes along the energetic and macrotidal coastline, featuring embayed, sandy, gravel, and engineered regions. Existing flood warning systems have used the process-based hydrodynamic model XBeach, but due to the computational cost, have resorted to populating look-up tables using off-line simulations and only a single realisation of the coastal bathymetry. Instead, SWEEP-OWWL runs in ‘real-time’ using a computationally efficient suite of empirical shoaling, breaking, runup, and overtopping equations at 184 coastal profiles, forced with hydrodynamic information from a regional 1-km spectral wave and hydrodynamic model. Importantly, the forecast system can be updated with the latest coastal profile data with no extra computational cost, which is shown to improve the accuracy of predicted overtopping rate by an order of magnitude in some cases. Compared to visual observations of flooding events from live streaming webcams around the southwest, the system correctly predicted the presence or absence of wave overtopping with 97% accuracy and showed skill in differentiating between low and high hazard events. Reliable forecasts of wave overtopping could considerably enhance a coastal community's ability to prepare and mitigate against the risk to life, property, and infrastructure during coastal flooding events, and the developed system shows that this can be achieved using a single desktop PC for entire regions featuring both natural and man-made sea defences.

An FPGA-Based Digital Real-Time Simulator for Hardware-in-the-Loop Testing of Traveling-Wave Relays

This paper presents an FPGA-based Digital Real-Time Simulator (DRTS) platform for Traveling-Wave Relay Testing (TWRT). Traveling-Wave Relays (TWRs) have relatively high sampling rate, as compared to traditional relays, and operate based on the high-frequency response of the power system. Therefore, performing Hardware-In-the-Loop (HIL) tests for a TWR requires i) high resolution, i.e., small simulation time-step and ii) accurate transmission line model to ensure proper replication of traveling waves subsequent to faults. These requirements introduce challenges for DRTS platforms. The envisioned platform in this work comprises an FPGA-based DRTS hosted by a CPU-based platform where the small time-step calculations are conducted by the former. A new state-space realization of the Frequency-Dependent Phase-Domain (FDPD) line model is introduced which enables a fixed hardware design, capable of accommodating its real and imaginary poles. The adopted solution approach enables the parallel solution of the FDPD model and network equations to maximize parallelism and facilitates the use of small time-steps. Performance and accuracy of the proposed DRTS are evaluated and verified against off-line simulation results. The platform is employed for closed-loop HIL testing of a commercial TWR and the results are reported.

Robust performance of virtual sensing methods for active noise control

This paper investigates the effect of changes in the environment on the performance of two widely-used virtual sensing methods for active noise control (ANC): the remote-microphone method and the additional-filter method. Robust performance of adaptive feedforward control algorithms incorporating such virtual sensing techniques is essential to achieving noise attenuation at the designated locations in practice, when subject to uncertainties in the control environment. Off-line simulations using the data measured with a headrest ANC system in a running car are initially conducted, to evaluate the performance of the two virtual sensing methods under practical conditions. The differences between the two methods are further studied by using an analytical model and numerical simulations of the headrest ANC system. It is shown that in general the additional-filter method is sensitive to uncertainties in the properties of the reference signals used for feedforward control, whereas the remote-microphone method is sensitive to changes in the plant responses related to the monitoring microphones. This study, therefore, can be used to guide the choice of virtual sensing methods in different applications.

Online Planning with Offline Simulation

One of the central issues in (finite horizon) online planning problems is to synthesize the impact of real time decisions on the subsequent states of the system, and the performance in the remaining time horizon (cost-to-go function). A complete resolution often leads to intractable dynamic programming problems. In this paper, we propose a computationally efficient approach to this problem that attains near-optimal performance in non-stationary environments. More specifically, we study a general class of online planning problems with concave objective functions and (global) feasibility constraints. A wide range of problems in supply chain management, online advertising, and network revenue management etc., can be appropriately modelled using this online planning framework. Leveraging on the value of the gradient information obtained from offline simulation (generated from the distributional information), we develop a generic approach to facilitate online planning for this class of problems. Furthermore, our proposed approach produces near optimal solution with sublinear regret and satisfies the feasibility constraints with high probability. We present extensive numerical evidence to validate the performance of this approach, and discuss its improvement over existing techniques that assume the underlying environment is stationary.