Accurate System Model Research Articles

Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems

Efficient critical load restoration under extreme natural disasters is a promising solution to establish resilient distribution systems. Deep reinforcement learning (DRL) approaches are widely adopted in the load restoration problem to avoid incorporating the accurate distribution system model and improve online decision efficiency. However, the vulnerability of DRL towards adversarial examples may lead to unpracticable decisions and pose potential threats to load restoration. To address this issue, this paper proposes a robustness assessment and enhancement method for DRL-enabled distribution system load restoration. In particular, the distribution system load restoration problem is formulated as a Markov decision process, and a deep Q-network is adopted to learn the optimal decision policy. Then, an adversarial example generation optimization model incorporating the deep Q-network is established to implement the robustness assessment of the DRL-enabled load restoration against adversarial examples. Furthermore, adversarial training with the experience replay of adversarial examples is adopted to retrain the agent and improve the stability of the load restoration decision-making. Finally, the effectiveness of the proposed method is analyzed and verified in the modified IEEE 33-bus system and IEEE 123-bus system. The results show that robustness evaluation and enhancement significantly reduce the application risk of DRL in load restoration with safety-critical requirements.

Hybrid mechanistic and neural network modeling of nuclear reactors

An accurate and efficient model is the foundation for dynamic characteristics analysis and control design of nuclear reactors. Nevertheless, the widely-used mechanistic models of nuclear reactors inevitably have some deviations from the actual reactors due to inaccurate model parameters and model assumptions. This paper proposes a hybrid mechanistic and neural network modeling method for nuclear reactors to eliminate or minimizing these deviations. The high-efficiency point reactor kinetics model was adopted as the mechanistic reactor core model, and the simulation results obtained using the commercial program RELAP5 were taken as the reference operational data, based on which the reactivity feedback in the mechanistic model that is difficult to be accurately determined during reactor operations was calibrated using a genetic algorithm optimized BP neural network. Dynamic simulation results of a Generation III large pressured water reactor under three typical reactivity change transients show that the core power and outlet coolant temperature obtained with the hybrid model match much better with the reference results than those of the mechanistic model, demonstrating that the hybrid modeling method can improve the accuracy of the mechanistic reactor model effectively. This study can provide a valuable reference for accurate and efficient modeling of complex systems.

Self-updating digital twin of a hydrogen-powered furnace using data assimilation

Data assimilation, i.e., upgrading a numerical model by using experimental observations, is applied to adapt the performances of a simulation-based digital twin (DT) of a semi-industrial combustion furnace, based on available experimental data. More specifically, we rely on Kalman filter (KF) to adjust the prediction of our model by accounting for the underlying uncertainties. The DT is obtained by combining dimensionality reduction (through Proper Orthogonal Decomposition, POD) and regression (using Kriging) applied to Reynolds-averaged Navier–Stokes simulations of the furnace covering a three-dimensional design space, including both geometric and operational parameters. The experimental campaign concerns the measurement of the axial and radial profile of temperature inside the chamber and the NO concentrations at the outlet of the furnace, for a fuel mixture ranging from pure methane to pure hydrogen. Two types of KF algorithms are analyzed, i.e. the steady-state and the recursive ones. Both methodologies demonstrate improved DT performances, highlighting the significance of the Kalman gain in weighing the model’s prediction and measurement uncertainties. We also conduct a sensitivity analysis of data errors to reinforce this concept. The results of our study demonstrate the potential of data assimilation to build accurate and adaptive reduced-order models of realistic combustion systems.

Model-free adaptive robust control based on TDE for robot with disturbance and input saturation

AbstractA model-free adaptive robust control based on time delay estimation (TDE) is proposed for robot in the presence of disturbance and input saturation. TDE is utilized to estimate the complicated nonlinear terms of the robot including unknown dynamics and disturbance, and a TDE error observer is developed to estimate the inevitable TDE error. When the input torque of the robot exceeds the upper or lower limit of the input saturation, an auxiliary system and a saturation deviation boundary adaptive law are employed to mitigate the negative impact of input saturation on the position tracking. Finally, the robust control law is obtained by backstepping. The stability of the closed-loop system is proved by Lyapunov functions, and the validity of the proposed method is demonstrated by comparative simulations and experiments. Compared with the model-based controllers and other model-free controllers, the proposed method does not necessitate the accurate dynamic model of the complicated system and with lower computation. Moreover, it can guarantee the desired position tracking performance of the robot even subject to disturbance and input saturation simultaneously.

Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

In the past few decades, drones have become lighter, with longer hang times, and exhibit more agile performance. To maximize their capabilities during flights in complex environments, researchers have proposed various model-based perception, planning, and control methods aimed at decomposing the problem into modules and collaboratively accomplishing the task in a sequential manner. However, in practical environments, it is extremely difficult to model both the drones and their environments, with very few existing model-based methods. In this study, we propose a novel model-free reinforcement-learning-based method that can learn the optimal planning and control policy from experienced flight data. During the training phase, the policy considers the complete state of the drones and environmental information as inputs. It then self-optimizes based on a predefined reward function. In practical implementations, the policy takes inputs from onboard and external sensors and outputs optimal control commands to low-level velocity controllers in an end-to-end manner. By capitalizing on this property, the planning and control policy can be improved without the need for an accurate system model and can drive drones to traverse complex environments at high speeds. The policy was trained and tested in a simulator, as well as in real-world flight experiments, demonstrating its practical applicability. The results show that this model-free method can learn to fly effectively and that it holds great potential to handle different tasks and environments.

Consensus-Based Distributed Cooperative Perception for Connected and Automated Vehicles

Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.

Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems

The pump system is the heart of the concrete pump truck, and its main function is to deliver high-pressure concrete into the pipeline through the reciprocating motion and cooperation of the main cylinder and the distribution valve driven by the swing cylinder. However, there are currently problems, such as unclear pumping operation rules and mismatched commutation timing, that result in poor pumping efficiency and short life of wear parts such as spectacle plates and cutting rings. To address above problems, based on AMEsim software and system test data, this thesis establishes an accurate simulation model of the pump system with more than 90% precision. Based on this model, the simulation reveals the operating rules of the main cylinder and the swing cylinder during the pumping process; draws the commutation timing diagram of the pumping system; clarifies that the commutation timing has four characteristics: sequential, hysteresis, coupling and cyclic; and proposes an optimization scheme for the commutation timing, i.e., reducing the relief pressure of the main cylinder and shortening the swing time of the distribution valve. The proposed optimization has been proven in the market and significantly improved the life of wearing parts by more than 90%. This study provides technical reference for the structure and parameter optimization of concrete pumping systems.

Toward an extreme-scale electronic structure system.

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree-Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45minusing 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency.

Computational simulations of propagation of a tsunami wave across the ocean

The 2D regularized long-wave equation is a mathematical model used to describe the behavior of water waves in two dimensions, taking into account nonlinear and dispersive effects. To obtain soliton wave solutions, computational methods have been employed to solve this equation. Solitons are localized wave solutions that maintain their shape and speed over long distances, making them useful in various applications, such as optical fiber communication and modeling water waves. This study utilized the Khater II, septic-B-spline, and Adomian decomposition techniques to solve the 2D regularized long-wave equation and obtain soliton wave solutions. The results showed a wide range of soliton solutions, including bright and dark solitons as well as soliton pairs and chains. Furthermore, we investigated the impact of the regularization parameter on the soliton solutions and observed that it affects the soliton speed and shape. Our findings demonstrate how computers can be utilized to obtain new soliton wave solutions for the 2D regularized long-wave equation, which have implications for various fields, such as oceanography, engineering, and physics. By understanding how solitons behave within the context of this equation, we can create more accurate models of water waves and other physical systems.

A data‐driven fault diagnosis of high speed maglev train levitation system

SummaryThe levitation system is a crucial component of a maglev train as it is responsible for levitating the train body above the track. Real‐time diagnosis of faults in the levitation system is essential for commercial operational purposes. Despite extensive research on the mechanism of the levitation system, it is challenging to obtain an accurate system model due to unavoidable noise and disturbances. To address this issue, this paper proposes a fault diagnosis method for the maglev train levitation system. This method combines model‐based fault diagnosis with a data‐based implementation approach to maximize the benefits of both approaches while considering system noise and model uncertainty. Additionally, a secondary confirmation of fault isolation results is proposed to improve the reliability of fault diagnosis and minimize the effect of false alarms. Finally, a subsequent processing of fault data is recommended to enrich fault analysis methods.

Transport coefficients for ion and solvent coupling. The case of the lithium-ion battery electrolyte.

Transport properties are essential for the understanding and modeling of electrochemical cells, in particular complex systems like lithium-ion batteries. In this study, we demonstrate how a certain degree of freedom in the choice of variables allows us to efficiently determine a complete set of transport properties. We apply the entropy production invariance condition to different sets of electrolyte variables and obtain a general set of formulas. We demonstrate the application of these formulas to an electrolyte typical for lithium-ion batteries, 1M lithium hexafluoro-phosphate in a 1:1 wt. % mixture of ethylene and diethyl carbonates. While simplifications can be introduced, they provide inadequate predictions of conductivity and transport numbers, and we argue that a full matrix of Onsager coefficients is needed for adequate property predictions. Our findings highlight the importance of a complete set of transport coefficients for accurate modeling of complex electrochemical systems and the need for careful consideration of the choice of variables used to determine these properties.

Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning

There has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system’s model is not available. In this paper, a novel model-free approach to perform operation control of DC microgrids based on reinforcement learning algorithms, specifically Q-learning and Q-network, has been proposed. This approach circumvents the need to know the accurate model of a DC grid by exploiting an interaction with the DC microgrids to learn the best policy, which leads to more optimal operation. The proposed approach has been compared with with mixed-integer quadratic programming (MIQP) as the baseline deterministic model that requires an accurate system model. The result shows that, in a system of three nodes, both Q-learning (74.2707) and Q-network (74.4254) are able to learn to make a control decision that is close to the MIQP (75.0489) solution. With the introduction of both model uncertainty and noisy sensor measurements, the Q-network performs better (72.3714) compared to MIQP (72.1596), whereas Q-learn fails to learn.

LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System

Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.

Advances in large amplitude oscillatory shear Rheology of food materials

Molecular interactions determine the microstructure of food, as well as its response to deformation and flow. In order to design efficient processing equipment, to produce high-quality, stable end products, to predict textural and sensory properties, and to ensure consumer acceptance, the characterization of food rheology is essential. Deformations are rapid and large during the processing of foods and during consumption. In food studies, large amplitude oscillatory shear (LAOS) has become increasingly popular due to its ability to mimic real-life processes. When food is subjected to dynamic oscillatory shear tests, a sinusoidal deformation is applied, the mechanical stress (or strain) is probed, and the response is recorded. This chapter summarize main methods to extract meaningful rheological parameters from complex LAOS response of selected food materials. A time-resolved nonlinear rheology method, sequence of physical processes (SPP), gave detailed interpretations of transient microstructures, whereas the Fourier Transform coupled with Chebyshev decomposition (FTC) method provide static measurements at specific strains. LAOS behavior and its relationship to food microstructures and texture still needed to be studied in depth. By constructing more accurate mechanical models of complex food systems, the fundamental knowledge can be applied to evaluate the nonlinear rheology of food for consumer acceptance and efficient processing.

Passivity-Based Partial Sequential Model Predictive Control of T-Type Grid-Connected Converters With Dynamic Damping Injection

LCL-filter interfaced three-level T-type converters (LCL-3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C) have been widely used in low-voltage applications due to their elevated power quality. However, there exist several problems. To begin with, the performance of the finite-control-set model predictive control (FCS-MPC) controlled 3LT2C relies on an accurate system model and measurement. Consequently, output performances will be influenced when model parameter mismatches, grid impedance variation, and the dead-time of power devices occur. In addition, the robustness of the FCS-MPC controller may also encounter challenges under disturbances and noise. Further, LCL-filter has the resonance problems in the grid currents, which may endanger the system stability. To solve these problems, a passivity-based (PB) partial sequential MPC (PSMPC) with dynamic-damping (DD) injection (DDPB-PSMPC method), which outperforms FCS-MPC and ensures the asymptotic stability, is proposed. First, the passive output voltage based on the Euler-Lagrange (EL) model is obtained using dynamic damping injection. Second, by embedding passive output voltage into MPC, a passivity-based PSMPC robust controller is designed to enhance its anti-disturbance abilities and achieve resonance suppression. Finally, to avoid the time-consuming of weighting factor selection, a PSMPC that allocates non-conflicting objectives in the same layer is introduced. Experimental results on 10-kW prototype demonstrates its excellent performance over existing techniques.

Mixed H 2/H ∞ Control With Dynamic Event-Triggered Mechanism for Partially Unknown Nonlinear Stochastic Systems

This technical note discusses the design process of dynamic event-triggered control (DETC) with the mixed <inline-formula> <tex-math notation="LaTeX">$H_2/H_\infty$</tex-math> </inline-formula> for partially unknown nonlinear stochastic systems. The purpose of this problem is to design a controller to make the closed-loop system achieve the expected <inline-formula> <tex-math notation="LaTeX">$H_2$</tex-math> </inline-formula> performance under the condition that the <inline-formula> <tex-math notation="LaTeX">$H_\infty$</tex-math> </inline-formula> continuous attenuation level is protected. Firstly, a two-player non-zero-sum game for stochastic system is given. Then we prove the optimal control strategy and the worst interference, which constitute the Nash equilibrium solution and can be derived from the corresponding Hamiltonian functions. Furthermore, two neural networks (NNs) are used to realize Nash equilibrium. Under the condition of dynamic event-triggered mechanism (DETM), the control strategy only is updated at the trigger moment. In addition, the stability and weights convergence of the system are proved mathematically. Finally, two numerical example are given to prove it. <i>Note to Practitioners</i>&#x2014;In the practice of control engineering, mixed <inline-formula> <tex-math notation="LaTeX">$H_2/H_\infty$</tex-math> </inline-formula> control can not only evaluate the <inline-formula> <tex-math notation="LaTeX">$H_2$</tex-math> </inline-formula> performance index of the transient behavior of the system, but also measure the <inline-formula> <tex-math notation="LaTeX">$H_\infty$</tex-math> </inline-formula> performance index of the system&#x2019;s robustness to external disturbances and parameter uncertainty. And many useful signals and interference vary randomly. Therefore, the optimal control of stochastic systems and dynamics play an important role in the modern industry. Saving control resources is very important for actual production. Therefore DETC is considered in this paper. On the other hand, in practice, accurate system models are difficult to obtain. In order to tackle this difficulty, by designing a novel scheme via integral reinforcement learning technique, the system relaxes the requirement of drift dynamic and Zeno behavior is avoided.

Capacitor Current Control Based Virtual Inertia Control of Autonomous DC Microgrid

Virtual inertia (VI) control of DC microgrids (DC MG) is a potential solution to the voltage stability issue caused by the intermittency of loads and renewable sources. Existing VI strategies for DC MG rely on a first-order differential equation (FODE) relating voltage (speed) with current (torque) to control the grid-forming converters that are crucial in an autonomous DC MG. However the output impedance of these converters can distort the inertial response. Existing research works overcome this by using a feed-forward controller (FFC) necessitating an accurate system model for proper compensation. Hence, in this paper, a novel VI scheme based on capacitor current control which does not rely on any differential equation is proposed. The proposed VI scheme employs a static gain to restrict the capacitor current for inertia emulation without any additional FFC. Furthermore, the proposed VI scheme is extended to parallel-connected converters to study their steady-state and transient coordination. Finally, the proposed strategy is validated in simulation using MATLAB&#x002F;Simulink and is also experimentally verified in a laboratory prototype using the TMS320F280049C controller.

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (CAM) system design based on general modeling network specification (GMNS)

This study presents a novel framework and open-source tools for simulating and managing connected and automated mobility (CAM) systems, taking into account their hierarchical nature and various levels of scheduling. The framework is based on a multi-layered network representation, which allows for efficient and accurate modeling of CAM systems at different levels of granularity, from macroscopic to microscopic. By employing this hierarchical approach, we achieve a balance between the level of detail in the representation and computational efficiency. Additionally, a spatial-discrete virtual track-based representation is introduced for precise vehicle dynamics modeling and for ensuring consistency with higher-level routing decisions. This facilitates individualized active traffic management for CAM applications. As part of our research, we have developed osm2gmns, an open-source package that allows users to effortlessly access and process transportation networks from OpenStreetMap in the General Modeling Network Specification (GMNS) format, facilitating data sharing and research collaboration. Furthermore, we explore traffic simulation, optimization, and operation methodologies for CAM systems, particularly focusing on the extent of scheduling capabilities. To support the research community, we further introduce an open-source package CAMLite for CAM system modeling. The effectiveness of our proposed methodologies and tools is demonstrated through a series of numerical experiments.

High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO4 blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications.