Strong Coupling Characteristics Research Articles

Research on Power Cyber-Physical Cross-Domain Attack Paths Based on Graph Knowledge

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method.

A Resource Prediction Method For Air Traffic Cyber-Physical-Social System

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

Optimisation of steel rolling schedule based on evolutionary multi-tasking transfer algorithm

Strip rolling is an important part of steel processing. The load distribution scheme in the rolling process directly affects production efficiency and product quality. A dynamic rolling process with changing speed is investigated to improve quality and reduce energy use. To balance the iterative calculation of the complex industrial evolution process and the requirements of high real-time, this study examine the rolling process by combining the multivariate, multi-constraint, and strong coupling characteristics of field measured data. On this basis, several conflicting rolling optimisation objectives in the process of rolling schedule optimisation were analysed, and a dimension reduction migration model based on transfer component analysis was established. In case of an inefficient transfer of feasible solutions, a multi-objective multifactorial evolutionary algorithm based on an explicit transfer solution strategy (MOMFEA-ETS) was proposed. The proposed algorithm obtained four average distance optimal values for eight practical rolling programming problems.

Kinematic Characteristics Analysis and Assembly Application of the Rectangular Pyramid Deployable Mechanism

Deployable mechanisms have the characteristics of complex loops and strong coupling. As a result, the solution to the degrees of freedom (DOFs) and kinematic characteristics of the basic mechanism unit are often complicated, and the solution for combined mechanisms is rarely reported in the relevant literature. In this paper, the kinematic characteristics of the rectangular pyramid deployable mechanism (RPDM) are analyzed, and the DOFs of the RPDM are calculated based on the screw theory. In addition, the DOF number and properties of its moving components are analyzed. Based on the condition that the nonhomogeneous linear equations have a unique solution, the singularity analysis of the mechanism is complete, which indicates that the mechanism can be continuously deployed and folded. Based on the geometric characteristics of the mechanism, its folded ratio is calculated. Finally, a ring, a planar, and a parabolic truss deployable antenna mechanism are assembled based on the RPDM, and the DOF variation for the different assembly modes is obtained via the screw constraint matrix. This study provides a good reference for the design and analysis of such multi-closed-loop deployable mechanisms, and the large-scale space-deployable mechanisms constructed in this paper have good application prospects in the aerospace field.

Bursting oscillation behaviors of a multi-time scales pumped storage power station with governor subsystem nonlinearity

Pumped-storage power station (PSPS) play a crucial role in supporting the grid integration of intermittent energy and require frequent regulation to balance fluctuations. Eliminating potential stability risks under the strong nonlinear and complex multi-scale coupling characteristics to ensure the stability of PSPS is crucial for new power systems. Although governor nonlinear elements have been studied in previous studies, the effects and potential risks of their non-smooth characteristics on the stability of different subsystems have not been reported. In this study, a mathematical model of PSPS considering the non-smooth characteristics of governor is established by introducing a segmentation function. Then the influence laws of their effect on important variables in different subsystems are studied through the time waveforms and phase trajectories. Additionally, sensitivity analysis is employed to identify key parameters affecting stability at various stages. Finally, the risk of bursting oscillation caused by the nonlinear elements is revealed. The results show that the nonlinear elements have less effect on the hydraulic subsystem, but their non-smooth characteristic induces state switching in mechanical subsystems, which poses a risk for frequency oscillations. Adjusting the deadband has an inhibiting effect on both oscillations caused by nonlinear elements and improves stability in hydraulic and mechanical subsystems. This work provides fresh perspectives and theoretical basis for minimizing the risk of bursting oscillations while improving Pumped-storage plant stability.

Study on High-Precision Tension Control Technology for a Cold-Rolling Pilot Mill with Hydraulic Tension

Tension control in cold-rolling pilot mills (CRPMs) with hydraulic tension is subject to extraneous forces resulting from positional disturbances, strong coupling, and time-varying characteristics, making it challenging to achieve ideal control results using the existing control methods. Therefore, in this study, a mathematical model of tension is first derived and then used to analyze the properties and difficulties associated with tension control. Second, a hydraulic servo-control system based on servovalves and proportional pressure relief valves is developed. In this system, redundant feedforward flow is generated by a servovalve according to the rolling schedule. The surplus flow is absorbed by a proportional pressure relief valve in the closed-loop control of tension. Third, simulation analysis is performed. Under severe friction disturbance (maximum 0.2 kN), and with the wide range of forward and backward slip, an accuracy of ±0.27 kN in tension control can still be achieved using the proposed control strategy, thereby demonstrating its effectiveness. Moreover, it has obvious advantages over existing control methods. Finally, an experimental study of tension is carried out in the cold-rolling mill with a maximum tension capacity of 50 kN, achieving ±0.2 kN tension control precision in the multipass rolling process.

Study on the Mechanism and Suppression of Harmonic Vibration of AMB-Rotor System

The AMB-rotor system is complex and has strong coupling characteristics, which allows multi-harmonic disturbances to enter the system through different ways to produce vibrations with rich spectrum components, which has a great influence on the improvement of micro-vibration accuracy of the rotor system. To further achieve active control of the micro-vibration in the AMB-rotor system, firstly, the mechanism of multi-source disturbance is analyzed according to the working principle of the AMB-rotor system, and the mathematical and physical relationship between the mechanism of disturbance generation and the inducement is deeply studied. Then, the structure of a novel adaptive notch filter, the method of adaptive frequency estimation and analysis of harmonic current suppression in the AMB system are presented. Finally, simulation and experimental research using an MSCMG system demonstrate the feasibility of the proposed method regarding the elimination of harmonic control current.

Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm

Aiming at the problem of high energy consumption caused by the nonlinear, strong coupling, and large hysteresis characteristics of central air conditioning (CAC) system, an energy-saving optimization method based on WTD–CNN–LSTM and multi-strategy improved sparrow search algorithm (MISSA) is proposed, which works to minimize the total energy consumption during the operation of the CAC system. Firstly, minimize the sum of energy consumption of chillers, freezing pumps, cooling pumps, and cooling towers as the objective function, and use the range of operating parameters of each equipment as the basic constraint conditions to establish an energy-saving optimization model for the CAC system. Then, WTD–CNN–-LSTM is used to predict the future cooling load, and the predicted results are used as key constraint condition to achieve on-demand cooling. Finally, MISSA is proposed to improve the initialization, update and convergence stages of the CAC system operation parameter optimization process, accurately obtaining the optimal operating parameters. Compared with manual experience, MISSA reduces energy consumption by 15.32 % and improves energy efficiency ratio by 20.76 %. Meanwhile, compared with other optimization algorithms, MISSA reduces energy consumption by 4.85–13.26 % and improves energy efficiency ratio by 6.53–16.33 % after optimizing the CAC system. The experiment verifies that MISSA is more energy efficient when applied to the CAC system and has the advantages of accuracy, fast convergence, strong global search capability, and the ability to jump out of local optimization.

Contact Force Surrogate Model and Its Application in Pantograph–Catenary Parameter Optimization

The significant increase in the speed of high-speed trains has made the optimization of pantograph–catenary parameters aimed at improving current collection quality become one of the key issues that urgently need to be addressed. In this paper, a method and solutions are proposed for optimizing multiple pantograph–catenary parameters, taking into account the speed levels and engineering feasibility, for pantograph–catenary systems that contain dozens of parameters and exhibit strong nonlinear coupling characteristics. Firstly, a surrogate model capable of accurately predicting the standard deviation of contact force based on speed and 14 pantograph–catenary parameters was constructed by using the pantograph–catenary finite element model and feedforward neural network. Secondly, sensitivity analysis and rating of the pantograph–catenary parameters under different speeds were conducted using the variance-based method and the surrogate model. Finally, by combining the sensitivity analysis results and the Selective Crow Search Algorithm, joint optimization of 10 combinations of the pantograph–catenary parameters across the entire speed range was performed, providing efficient pantograph–catenary parameter optimization solutions for various engineering conditions.

Research on Greenhouse Environment Prediction Based on GCAKF-CNN-LSTM

Highlights A GCAKF-CNN-LSTM model is proposed for greenhouse temperature and humidity forecasting. The grey correlation analysis is used to select the most relevant variables. Kalman filter is applied for denoising to improve the data quality. The proposed model achieves higher forecasting accuracy with the lowest forecasting errors. Abstract. Accurate prediction of temperature and humidity in the greenhouse environment is helpful to regulate the environment and promote crop growth. Aiming at the characteristics of nonlinear and strong coupling in the greenhouse environment, this article proposes a hybrid greenhouse temperature and humidity prediction model based on preprocessing algorithms, Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Firstly, grey correlation analysis (GCA) is used to screen the data features and analyze the factors affecting the temperature and humidity in the greenhouse. Secondly, data is denoised by the Kalman filter (KF) to reduce the noise interference. Thirdly, the local connection and weight sharing features of the CNN are applied to obtain effective features from the series, and the long- and short-term dependence relationships of the data are learned by using the LSTM networks. Finally, the proposed model is validated on the greenhouse data. Experimental results demonstrated that, compared with Back Propagation(BP), Gated Recurrent Units (GRU), and LSTM, the RMSE of temperature prediction results was reduced by 31.5%, 21.6%, 14.4%, and the MAE reduced by 48.5%, 41.0%, and 32.3%, respectively. The RMSE of humidity prediction results decreased by 28.3%, 2.73%, and 0.63%, and the MAE decreased by 69.4%, 54.5%, and 10.8%, respectively. The proposed model can improve prediction accuracy and provide a decision basis for improving the timeliness of the greenhouse environmental control system. Keywords: Convolutional neural network, Greenhouse environment prediction, Kalman filter, Long short-term memory network.

An intelligent control method for PEMFC air supply subsystem to optimize dynamic response performance

The excellent dynamic response performance of the proton exchange membrane fuel cell (PEMFC) system provides an important guarantee for quickly and accurately following the power demand of fuel cell vehicles. Insufficient air supply during load changes is the main reason for poor dynamic response. However, due to the strong coupling characteristics of flow and pressure, the existing technology has shortcomings such as poor anti-interference ability, slow decision-making speed and unstable control performance, which limit the optimization effect. To solve the above problem, this paper proposes a control method for air supply subsystem to optimize PEMFC dynamic response performance based on deep reinforcement learning. First, a model of the air supply subsystem is established, and its accuracy is verified through PEMFC system experimental bench. Second, the relationship law between the control parameters of air supply subsystem and the dynamic response performance of PEMFC is revealed. Finally, an air supply subsystem control architecture based on Soft Actor-Critic (SAC) is proposed. When the load power is stepped up from 58.5 kW to 101.6 kW, the time for the voltage to reach the steady state under intelligent control is reduced from 1.2 s to 0.7 s, and the electrical efficiency is increased from 72.6 % to 79.1 % compared with traditional decoupling control. The dynamic response performance is improved by 41.6 % and the efficiency is improved by 6.5 %.

Numerical investigation and optimization on dynamic power generation performance of enhanced geothermal system

The Enhanced geothermal system (EGS) is a crucial technology for the development and utilization of hot dry rock (HDR). However, current research typically overlooks the strong coupling characteristics of fracture seepage, wellbore transport, and thermoelectric conversion, leading to a lack of precision in describing the dynamic operational performance of EGS throughout its entire lifecycle. A dynamic numerical model of evaporator and condenser is established by the moving boundary (MB) method. Subsequently, the dynamic power generation process is integrated with a coupled thermal-hydraulic-mechanical (THM) model of the fractured reservoir, proposing a methodology for calculating the dynamic performance of EGS throughout its lifecycle.Traditional EGS models exhibit significant deviations between calculated results and actual performance due to the neglect of instantaneous changes in injection temperature and the failure to consider the nonlinear characteristics of heat exchangers. The production pressure difference is a primary factor influencing the net output power of EGS. Therefore, selecting an appropriate injection pressure is crucial to ensuring continuous power generation throughout the EGS lifecycle. By optimizing the operating parameters of the actual EGS project, the optimal injection flow rate, fracture aperture, initial fracture permeability and well spacing are 50 kg/s, 0.03 m, 1.5 × 10−10 m2 and 350 m respectively.

Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

To guarantee stable power system operation, a synchronous condenser (SC) is configured in an ultra-high voltage direct current (UHVDC) converter station to provide dynamic reactive power support to the power system. The research on the reactive power output modelling of a SC in an UHVDC converter station has important theoretical significance and practical value for the reactive power control of a SC in an UHVDC converter station. Focusing on the reactive power regulation system of the SC with strong coupling, multivariable, and nonlinear features, it is difficult for the universal analytic method to build the SC reactive power output model. A novel reactive power output model of the SC in the UHVDC converter station based on interlaced superposition convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed, in which a novel interlaced superposition CNN composed of convolution units with two different structures is built to increase the depth of the network and avoid over-fitting. Particularly the branch channels of convolution units with two different structures are connected by a convolution layer and a skip connection respectively. The interlaced superposition CNN-BiLSTM model is that the combination of the interlaced superposition CNN model and BiLSTM model for improving the model accuracy and generic capability. The Bayesian optimization method is used to optimize its hyperparameters. The application of interlaced Superposition CNN-BiLSTM in SC reactive power output modelling is a new technology. The excitation voltage and excitation current of the SC are used as inputs for the training and testing sampled data, and the reactive power of the SC is used as outputs for the training and testing sampled data. Therefore, the reactive power output model of a SC in an UHVDC converter station based on interlaced superposition CNN-BiLSTM is obtained, and it attains a low root mean square error (RMSE = 0.126750) and a high determination coefficient (R2 = 0.999999).

Cross-Space Conduction Assessment Method of Network Attack Risk under the Strong Coupling Characteristics of Electric Power Cyber Physics

With the deep integration and wide application of advanced digital sensing, Internet of Things technology, and energy technology in power systems. Power information systems and physical systems are gradually being coupled and developed into power cyber–physical systems (CPS). A number of blackouts in recent years have shown that cyberspace cyber attacks on CPS can lead to the intensification and rapid spread of faults in the physical space of the power grid, and even system collapse. Aiming at the difficulty of analyzing the evolution of cyber–physical cross-space impacts of cyber-attacks, this paper proposes a cross-domain propagation impact assessment method that considers cyber–physical coupling risks caused by attacks. First, according to the multiple coupling relationship between the power system information space and physical space, the monitoring function model and the control function model are established. Second, under the effect of high-concealment attack, analyze the impact of the risk caused by its failure after it is transmitted to the physical space with different propagation probabilities. Finally, the experimental verification was carried out using the IEEE RTS79 standard test system. The simulation results show that the proposed method can comprehensively consider the cyber–physical energy supply coupling relationship, the risk propagation probability, and the operating characteristics of the information system, and effectively quantify and evaluate the impact of information space network attacks on the physical space entity power grid. It further reveals the objective law that information space risks can evolve and spread across domains under the condition of strong coupling of information physics.

Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models.

The near-isothermal rolling process has the characteristics of multi-variable and strong coupling, and the industrial conditions change constantly during the actual rolling process. It is difficult to consider the influence of various factors in industrial sites using theoretical derivation, and the compensation coefficient is difficult to accurately determine. The neural network model compensates for the difficulty in determining the compensation coefficient of the theoretical model. The neural network can be trained in advance through historical data, the trained network can be applied to industrial sites for prediction, and previous training errors can be compensated for through online learning using real-time data collected on site. But it requires a large amount of effective historical data, so this research uses a combination of production data from a controllable two-roll rolling mill and finite element simulation to provide training data support for the neural network. Five trained neural networks are used for prediction, and the results are compared with industrial site data, verifying the reliability and accuracy of genetic algorithm optimized neural network prediction. We successfully solved the problem of low control accuracy of TiAl alloy outlet thickness during near-isothermal rolling process.

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

Adaptive robust control for biped walking under uncertain external forces

Adaptability and robustness are important expressions of the intelligent walking ability of biped robots. This paper is concerned with the problem of dynamical biped walking and robust control of biped robots under external forces. Due to the characteristics of strong coupling and hybrid, the robotic system is modeled as a rigid kinematic chain with Lagrange equations. A novel adaptive feedback controller is proposed based on sliding mode control (SMC) and hybrid zero dynamics. The novelty of the proposed work lies in taking the uncertainty of upper-bound error into consideration. The hybrid robust control is mentioned to approximate unknown dynamic functions with the adaptive weight. The restricted Poincare return map is utilized to analyze the stability of a nonlinear impulsive system. It ensures that the flow of the continuous subsystem can pass through the impact cross section. Finally, the simulation results illustrate that the proposed adaptive SMC control system can favorably track the reference trajectories, even when a fault occurs, which verifies the effectiveness of the proposed method.

Research on the multi-physical coupling characteristics of the machine tool and milling process based on the systematically integrated model

Good machine tool precision and a stable milling process are important foundations for obtaining high-quality qualified parts. During the machining process, the machine tool and the milling process will impact each other, and further jointly affect the motion accuracy as well as part quality. This paper makes a deep analysis of the machine tool and milling process from a new perspective, which regards the machine tool and milling process as a coupled entirety, and studies the interaction between them from a systematic perspective. By establishing a systematically integrated model of the ball screw feed system and milling process, the multi-physical simulation of the whole system is realized with the consideration of multi-source harmonics. The electrical-magnetic-mechanical-kinematic coupling characteristics between the machine tool and the milling process are investigated based on the established model and experiments. The research results show that the servo system and the milling process are two major harmonic sources in the system that generate low-medium frequency and medium-high frequency harmonics, respectively. As the machining process progresses, these harmonics will be transmitted and coupled through the servo motor, mechanical structure, and feedback signals, leading to the fluctuation in the motion of the worktable and the influences on the surface quality of the final part. A type of part surface defect that can only be explained by integrated analysis of the whole system is also demonstrated. The research work indicates that the machine tool and milling process is an integrated system with strong coupling characteristics. It's of great significance to study them from a systematic perspective.

Research on improved linear auto-disturbance rejection control of hybrid energy storage in optical storage microgrid

Photovoltaic power generation has uncertainties such as randomness and volatility. In order to ensure the stable operation of the power system, hybrid energy storage technology is introduced to improve the voltage stability of the optical storage microgrid. Traditional PI control still needs some help in response speed and overshoot. Facing the hybrid energy storage microgrid system with nonlinear and strong coupling characteristics, in order to improve power quality and reduce bus voltage fluctuation and impact, this paper designs an improved active disturbance rejection control (ADRC) integrated energy storage control system based on a fuzzy algorithm, Based on the traditional double closed-loop PI control, the fuzzy LADRC is used to replace the voltage outer loop, and the system model is built on the MATLAB/Simulink platform for simulation. The results show that the proposed control strategy has a smaller overshoot, faster dynamic response time, and good anti-interference ability compared with the traditional PI and ADRC, which improves the dynamic performance of the photovoltaic energy storage system while stabilizing the bus voltage fluctuation and ensures the stable operation of the powergrid.

Key deviation source diagnosis of complex thin-walled structures based on complex networks and weighted transfer entropy

Abstract There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.