Dynamic Time Delay Research Articles

Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model

Accurately predicting renewable energy consumption is crucial for sustainable social and economic development, especially in China during its energy transition. This research introduces a novel dynamic fractional-order discrete grey multi-power model (DFDGMM(1,1,N)) to enable accurate forecasting of renewable energy consumption in China. The proposed method introduces a fractional-order accumulation operator and three power exponents that not only ensure the priority of new information, but also accurately capture the nonlinear traits of system data. It also incorporates a dynamic time delay function to account for the time lag between energy and economic development, enhancing the model's flexibility. Additionally, the study combines the whale optimization algorithm and the double-error idea to optimal parameter search. The proposed model is versatile and can be simplified into 14 other grey models. The case study demonstrates the model's impressive predictive accuracy, with a fitting error of 4.02 % and a test error of 0.89 %. The model is then employed to forecast renewable energy consumption in China, predicting a rapid annual growth rate of 17.25 % from 2022 to 2030. Overall, this article successfully constructs a dynamic prediction model in theory and scientifically provides valuable data support for the nation's energy development planning in practice.

Resilient Consensus for Discrete-Time Multiagent Systems With a Dynamic Leader and Time Delay: Theory and Experiment.

The ever-present cyber-attacks have posed a significant challenge to consensus of multiagent systems (MASs), due to their capability of compromising agents. These threats underscore the critical need to design control strategies that can endow MASs with resilience. In this article, we address resilient consensus problems for first-and second-order discrete-time MASs, considering the presence of malicious agents, a dynamic leader, and communication delay. First, a resilient controller is designed for first-order MASs, and sufficient conditions are derived based on existing robust graph concepts to achieve consensus with ultimately bounded error. Next, since the derived error bound grows factorially with the system scale, a novel graph structure and a modified controller are proposed to limit the growth to a linear rate. Building upon the obtained results, an estimator-based control framework is introduced to solve resilient consensus problems for second-order MASs. Finally, comparative simulations and practical experiments based on unmanned ground vehicles and unmanned-aerial-vehicles are conducted to validate the effectiveness and practicability of the proposed control methods.

Forecasting China’s total renewable energy capacity using a novel dynamic fractional order discrete grey model

Accurately forecasting renewable energy capacity is crucial for informing national energy policies and promoting sustainable socio-economic growth. This study proposes a novel dynamic fractional-order discrete grey model (DFDGM (1,1)) for forecasting China's total renewable energy capacity. The model constructs a dynamic fractional time delay polynomial, which enhances the adaptability of the model to different sample sizes. The time response function of model is directly derived from the discrete equation, preventing errors caused by the transition from discreteness to continuity. Furthermore, the proposed model represents the unified form of six grey models, effectively expanding the model's application domain and scope. The method introduces a double error approach to facilitate optimal parameter selection, successfully addressing overfitting and randomness challenges in parameter selection. Empirical studies show that the proposed model outperforms eleven other methods, with fitting and forecasting errors of 0.39% and 1.13%, respectively. Finally, the proposed model forecasts the future development trend of China’s total renewable energy capacity. In general, this research not only improves the precision of predictions but also progresses the dynamic theory in prediction studies.

Forecasting clean energy power generation in China based on a novel fractional discrete grey model with a dynamic time-delay function

Clean energy plays an essential role in responding to environmental crises. Accurate forecasting of clean energy power generation can provide necessary references for the formulation of energy policy. This paper proposes a novel fractional discrete grey model with a dynamic time delay function (DTDFF-DGM (1,1)) to forecast clean energy power generation. This model introduces the fractional accumulation operator and the dynamic time-delay function into the discrete grey model, which ensures the priority of new information in the original data and improves the model's adaptability to different sample data. The optimal parameters of the model are calculated by the dynamic linkage between the fitting error and the test error, which effectively avoids the overfitting problem. Empirical studies have proved that the model has better prediction accuracy compared to other methods. Finally, the proposed model is employed to forecast clean energy power generation in China. The results show that from 2020 to 2025, the overall growth rate of hydropower, wind power and nuclear power in China will be 9.27%, 119.61% and 36.38%, respectively. Based on the discussion of the forecast results, relevant policy suggestions were made. This paper realizes the transformation from a static model to a dynamic model in methodology and promotes the sustainable development of clean energy power generation in application.

Investigation of the Dielectric Properties of the Insulating Gas of Commercial Gas-Filled Surge Arresters

Based on the experimental results of dynamic breakdown voltage and electrical breakdown time delay, the dielectric properties of insulating gas commercial gas-filled surge arresters were analyzed. Data for dynamic breakdown voltage obtained by the method of stepwise increase of voltage whose rise rates ranged from 1 to 10 V/s. It is shown that these values of voltage rise rate do not have a significant effect on the value of dynamic breakdown voltage. In order to monitor the processes caused by electrical breakdown on the dielectric properties of insulating gas of these components the electrical breakdown time delay was a measure for different values of relaxation time and applied voltage. It has been shown that the presence of positive ions, which are formed during breakdown and subsequent self-sustaining discharge with recombination time of about 1 ms, has a significant effect on the reduction the dielectric resistance of insulating gas. It has also been shown that the applied voltage whose values are close to DC-spark over-voltage has a significant effect on the delay response of these components.

Attitude and Altitude Control Design and Implementation of Quadrotor Using NI myRIO

Multi-rotor vehicles have demonstrated great potential in many applications, from goods delivery to military service. Its simple structure has drawn much attention in control research, with various feedback control design methods being tested on it. In this study, a quadrotor is constructed with National Instrument (NI) myRIO for its flight controller. Linear controllers are synthesized with a simple model and with a more detailed model, which also considers the actuator dynamic and system time delay, to exploit the limitations and trade-offs posted by hardware and its influence on linear control design and implementation. The simulation results match the real response better if the detailed model is used in the control design. In addition, the linear–quadratic–Gaussian (LQG) control provides the best response in the control design in this study. The constraints posted by the actuator and time delay are clearly observed in the control synthesis process and the experiment result. These constraints also led to poor control performance or even instability if not considered in the control implementation in advance.

Adaptive Energy Reference Time Domain Passivity Control of Haptic Interfaces.

Haptic devices are designed to assist humans in operating tasks in a remote or virtual environment. The passivity-based controllers feed back the forces from the environment while maintaining stability. This article presents the adaptive energy reference time domain passivity approach to overcome the sudden force change inherent in the conventional time domain passivity approach (TDPA). The main advantage of the proposed method is that it can be applied to the haptic interfaces interacting with delayed unknown environments without increasing conservatism compared to the conventional TDPA with or without energy reference. The adaptive energy reference is learned at each interaction by a passive estimation of the haptic interface energy. The energy reference is found using force and velocity data, which does not need the foreknowledge of the environment dynamic model parameters and time delay. Therefore, the designed controller can adapt to different environments and time delays. The proposed method is evaluated in both simulation and experimental setups where the parameters of the environments are unknown to the controller. It is shown that the sudden change in force is decreased compared to the conventional TDPA for haptic interface with or without time delay in the system.

Исследование теплофизических процессов получения различных алюмоматричных композитов

The possibilities of obtaining aluminum matrix composite materials of the composition Al−Al2O3 are considered by the method of liquid-phase oxidation of aluminum by purging the melt with oxygen. The obtained materials are a two-phase system with different concentrations of the solid phase. An algorithm for calculating the kinetics of aluminum oxidation in obtaining the oxide phase of an aluminum matrix composite is proposed. It is established that the beginning and completeness of the reaction on the surface of the gas bubble determines the synthesis of the oxide phase and is determined by the functions T(t), h(t) in the region of dynamic time delay, which, in turn, the queue is revealed as a function of the temperature in the contact zone of the gas bubble with the melt, taking into account the duration of the heating of the bubble along the cross section and the radius of the bubble itself. The microstructures of the obtained materials with different structural-phase state with variations in the time of purging and oxygen supply to the aluminum melt are presented

Investigation of thermophysical processes of obtaining various aluminum matrix composites

The possibilities of obtaining aluminum matrix composite materials of the composition Al-Al2O3 are considered by the method of liquid-phase oxidation of aluminum by purging the melt with oxygen. The obtained materials are a two-phase system with different concentrations of the solid phase. An algorithm for calculating the kinetics of aluminum oxidation in obtaining the oxide phase of an aluminum matrix composite is proposed. It is established that the beginning and completeness of the reaction on the surface of the gas bubble determines the synthesis of the oxide phase and is determined by the functions T(t), h(t) in the region of dynamic time delay, which, in turn, the queue is revealed as a function of the temperature in the contact zone of the gas bubble with the melt, taking into account the duration of the heating of the bubble along the cross section and the radius of the bubble itself. The microstructures of the obtained materials with different structural-phase state with variations in the time of purging and oxygen supply to the aluminum melt are presented. Keywords: aluminum matrix composite material, high temperature oxidation, gas bubble.

Adaptive control of time delay teleoperation system with uncertain dynamics.

A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

Robust yaw control of autonomous underwater vehicle based on fractional-order PID controller

Autonomous underwater vehicles (AUVs) have broad applications owing to their small size, low weight, strong ability to operate autonomously, and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in complex ocean environments and have attracted significant research attention in marine science and technology. The main difficulties with AUV motion control include the large uncertainties of dynamic and hydrodynamic characteristics and time delay of the signal transmission channel. In this study, we propose a robust fractional-order proportional–integral–derivative (FOPID) controller design for an AUV yaw control system. First, a three-dimensional stability region analysis method is proposed to achieve fractional orders. Unlike other stability region analysis methods, the proposed method is supported by theory instead of observation. Then, the other parameters are optimized according to the robust design specifications with respect to the parameter uncertainties. Therefore, the controlled system can tolerate different parameter uncertainties and fulfill transient performance specifications while maintaining system stability. The simulation results illustrate the superior robustness and transient performance of the proposed control algorithm.

Influence of low dose rate gamma radiation on breakdown voltage and electrical breakdown time delay of working gas Geiger-Muller chamber

This paper presents the experimental data on the dynamic breakdown voltage and electrical breakdown time delay of commercial Geiger-Muller chamber manufactured by PHILIPS. The distributions of these data were determined. Static breakdown voltage was estimated based on the mean value of dynamic breakdown voltage as the function of voltage increase rate, for the cases when gamma radiation was not presented and in its presence. In order to separate the mechanisms that predominantly influence the initiation of breakdown for different values of relaxation time, the dependences of mean value of electrical breakdown time delay as the function of relaxation time was analyzed. It has been shown that during the relaxation times up to 3 ms positive ions plays a dominant role in initiating the breakdown while for relaxation times from 3 ms to 30 s initiation of the breakdown is caused by electrically neutral active particles. The effect of gamma radiation on electrical breakdown time delay was observed for relaxation time greater than 7 s.

Robust trajectory tracking control of non-holonomic wheeled mobile robots using an adaptive fractional order parallel fuzzy PID controller

Non-holonomic wheeled mobile robots (WMRs) are highly uncertain, multi-input multi-output (MIMO), non-linear dynamic systems that are expected to perform under varying environment and structural reservations. An Adaptive Fractional Order Parallel Fuzzy Proportional-Integral-Derivative (AFO−PFPID) controller is proposed and investigated on WMR to meet the above challenges. Computer simulations were carried out under the effects of dynamic parameter variations, noise, forced displacement, time delay, and uncertainty in the pose to thoroughly assess the controller's performance. Further, to evaluate its relative assessment, the AFO−PFPID controller's performance is compared with its integer counterpart Adaptive Integer Order Parallel Fuzzy Proportional-Integral-Derivative (AIO−PFPID) controller. Both the controllers were tuned with the Multi-Objective Grey Wolf Optimization Algorithm to minimize the positional and velocity profile errors with an overall goal to attain effective trajectory tracking. Though both the controllers effectively performed tracking goals, the AFO−PFPID controller has offered a significantly robust performance even under the model uncertainties and disturbances. Therefore, based on the presented investigations, it is concluded that the AFO−PFPID controller is a superior control technique for non-holonomic WMRs trajectory tracking application.

Xenon-filled diode performance under influence of low doses of gamma radiation

This paper presents a detailed statistical analysis of experimental results of dynamic breakdown voltage and electrical breakdown time delay for xenon-filled diode. These quantities have a stochastic nature and they were measured in the cases when the xenon-filled diode was and was not exposed to a gamma radiation source, with exposure dose rate 7.7⋅10−12 C/(kg⋅s). The static breakdown voltage was estimated based on dynamic breakdown voltage as a function of voltage increase rate. The applicability of certain distributions to experimental dynamic breakdown voltage and electrical breakdown time delay data was also analyzed.

Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method

Efficient operation and fine control are the keys of heating stations (HSs) to reduce energy consumption and carbon emission. Identifying control regularity of HSs is significant to realize fine control. This paper firstly defines control regularity, regulation time step (RTS) and regulation time node (RTN) of HSs. Then identify HSs’ type based on insulation performance of buildings. Secondary loop supply and return temperature are chosen as characteristic parameters to determine pipe network thermal delay, while indoor temperature and the comprehensive outdoor temperature are used to determine building thermal inertia delay. Cross-correlation function dynamic time delay method is used to identify the delay time, and RTS of HS is determined. The regression model of RTS is obtained to decide daily RTNs. It is found that RTS of buildings with floor heating are larger than that with radiators, energy-saving buildings are larger than non-energy-saving buildings, and the high cold period is greater than the early and last cold period. The proposed method is applied to three HSs. Results show that average indoor temperature is maintained within the target range of 20 ± 1 °C, varies within a narrow range. Indoor temperature unevenness coefficient reduction indicates that indoor thermal comfort has been improved.

Complete decoupling compensation of three‐phase inverter with LCL filter in synchronous reference frame

A decoupling control strategy of three-phase inverter, which can realize complete decoupling of the active and reactive current in the synchronous reference frame (SRF) is proposed here. There are two coupling factors of current loop in SRF, one is time delay caused by digital control, and the other is AC filter. Dynamic time delay compensation is applied to eliminate coupling caused by the first factor. The phase of the bridge leg voltage can be predicted. The difference between it and the phase of reference voltage which takes part in pulse width modulation (PWM) is the dynamic compensation angle, which can compensate the time delay. High-order feedforward compensation is applied to eliminate coupling caused by the second factor. Complex vector model of the LCL filter is derived and the high-order coupling coefficients can be offset by feedforward compensation. The coupling characteristics and compensation principles are analysed in detail. The system stability is discussed. Finally, the simulation and experimental results are given to verify the theoretical analysis.

High-precision temperature measurement with adjustable operating range based on weak measurement

High-precision temperature measurement with adjustable operating range is investigated and experimentally demonstrated based on weak measurement in the frequency domain. The operating range of measurement is precisely modulated by introducing a dynamic extra time delay to the post-selection pumped by a femtosecond laser. By choosing appropriate nonlinear materials and the attenuation, the resolution of the optimized extra time delay can be improved by two orders of magnitude over that of a traditional phase delay compensator. Considering the influence of noise in the experiment, the highest precision of 8.03 × 10−7 °C can be achieved by using a currently available spectrometer. Moreover, the average sensitivity can reach to 38 nm/°C. Taking advantage of the high precision and adjustable operating range, the proposed method has great potential applications in high-precision temperature measurements.

Dynamic Soft Sensor Modeling for Time Sequence Data Based on Convolution Filters

Dynamic responses, measurement noise, and time delays in industrial processes can affect the accuracy of soft sensing models. Our goal is to create a dynamic soft sensor modeling method that is robust to the abovementioned problems. Aiming at time sequence data, a feature extraction-based model is constructed by combining convolution filtering techniques with a backpropagation neural network. In addition, training strategies based on gradient descent are proposed to ensure the efficiency and accuracy of the training process. Convolution parameters are trained with model parameters synchronously without prior knowledge. The performance is verified through simulations and industrial experiments in terms of measurement noise, sensor drifting, and dynamic modeling problems.

Clock synchronization error compensation algorithm based on BP neural network model

Error compensation is an important guarantee method to ensure the accuracy of clock synchronization in underwater sensor networks. Existing research methods mainly use linear fitting and least square method to compensate for clock synchronization parameters. Underwater wireless sensor network nodes are mobile, which leads the network nodes to be always in a time-varying state. In the process of synchronous forwarding, the position where the node sends and receives data packets will change, resulting in a relative moving distance, leading the dynamic delay to an increase in. In this way, as the number of forwarding nodes increases, the error of the clock gradually increases, causing the synchronization accuracy of the underwater sensor wireless network to gradually decrease. The existing underwater wireless sensor network clock synchronization algorithm does not fully consider the dynamic time delay caused by the movement of the node with the ocean current. It only uses the time stamp mechanism to solve the clock synchronization parameters, and then uses the traditional linear fitting to refine the synchronization parameters. The accurate solution of dynamic time delay is a key factor of synchronization accuracy. The use of traditional optimization algorithms to refine the synchronization parameters can easily fall into a local optimum, which makes the synchronization accuracy not high. Therefore, the existing traditional research on clock synchronization algorithms cannot well solve the problem of clock synchronization accuracy caused by node mobility. However this type of method does not consider the clock synchronization accuracy of node movement affected by ocean currents. To solve this problem, this paper proposes a clock synchronization error compensation algorithm based on BP neural network model. First, the deep-sea Lagrangian ocean current model is used to describe the movement of underwater nodes and simulate the movement speed of underwater nodes, and then a clock synchronization parameter model is established, and finally a BP neural network clock synchronization error compensation model is build, which conforms to the underwater environment, and the excitation function is defined, and regular term factor and compensatory factor are introduced to avoid model over-fitting. The BP neural network model clock synchronization error compensation algorithm is established for error back propagation. Simulation experiments show that compared with the comparison algorithm TSHL, MM-sync, and MU-sync, the accuracy of clock synchronization, namely the error between clock synchronization time and standard time, increased by 37.42%, 17.29% and 21.86%, and the mean square error is significantly reduced.

DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models

Taking advantage of varying degrees of attention on specific features, attention-based long short-term memory (ALSTM) networks have made inroads into the industrial multivariate time series prediction sector recently. However, conventional ALSTM models usually employ static time-delays to constant select input/output pairings of multivariate data, which apparently ignores dynamics of transmission time between industrial process variables and degrades the prediction performance by incorrectly extracting process characteristics. In response to this problem, this paper proposes a novel approach to extracting dynamic time-delays to reconstruct (DTDR) multivariate data for an improved ALSTM prediction model. Therein, the temporal locations and spans of multivariate data are adaptively tailored to input/output pairings of the ALSTM network according to the dynamic time-delays. Specifically, the multivariate data can be accurately matched in temporal positions, and the data information in the original temporal spans with Status transfer time abnormal are replaced. Consequently, this prediction model not only appropriately utilizes dynamics between the predicting and correlated variables, but also makes better attentions on key features extracted from optimum data. Applied to industrial distillation and methanol production processes, the proposed method demonstrates the capability of significantly improving network training speeds as well as prediction accuracies in contrast to static time-delay based ALSTM and LSTM models, expecting even more applications.