Coupling Delay Research Articles

Multi-party stochastic energy scheduling for industrial integrated energy systems considering thermal delay and thermoelectric coupling

Multi-dimensional stochastic factors challenge the interactive energy scheduling of the industrial integrated energy system (IIES). Previous research focuses on either deterministic energy scheduling or individual stochastic scheduling while neglecting complicated interactions among uncertain parties, which brings the research gaps about stochastic multi-party’s interaction. In this regard, a multi-party stochastic energy scheduling approach in IIES is proposed based on the stochastic game. A decentralized decision support system is considered, and a stochastic utility model is designed for decentralized IUs with multi-dimensional stochastic factors from photovoltaic (PV) production and IIES parameters, enabling them to participate in the multi-energy scheduling with their own strategies. A stochastic game model is developed considering the thermoelectric coupling and the IUs’ interaction. The co-decision mechanism, recognizing different transfer times of electrical and thermal energy, is built based on the state transition within the game. Moreover, a distributed solution algorithm that includes the Markov decision process and iterative method is designed to address the problem of the “curse of dimensionality” arising from multiple stochastic factors. Finally, case studies with realistic data from an industrial park in Guangdong Province, China, are designed to show the effectiveness of the proposed approach, which enhances IUs’ profits by 9.4% and fits flexible load strategies and price strategies. The decentralized system can also reduce the computation time by 70.1% compared to the centralized system. Through analyzing different number of scenarios and intervals for PV generation, electrical and thermal load, the conclusion has obtained that increase the number of scenarios has a negative effect on IUs’ decision, but increase the number of load intervals contributes to more specific results and higher utility.

Coherently Combined DFB Laser Array Chip With Reduced Relative Intensity Noise

The relative intensity noise (RIN) behavior of coherently combined DFB laser array by injection locking is experimentally investigated. A monolithically integrated four-element DFB laser array with a splitter and an output coupler is fabricated, and the frequency stabilities of the DFB lasers are improved to ensure a stable injection locking of DFB laser array by a master laser. It is demonstrated that the RIN of the coherently combined laser array is reduced by almost 20 dB at low frequencies compared with the free-running state. The results indicate that monolithic integration of DFB laser array under injection locking can help improve the RIN performance by eliminating variation in coupling delays and phase jitters.

Synthesis of white laser source based on nonlinear frequency conversion with stimulated Raman adiabatic passage

This paper proposes a theoretical scheme to synthesize white laser source based on the cascaded sum frequency generation (CSFG) model with stimulated Raman adiabatic passage (STIRAP). In the CSFG model, the signal laser, intermediate laser and output laser correspond to red, green and blue (RGB) lasers respectively. The results show that in general the red laser is completely converted to the blue laser, and there is no apparent intermediate green laser. Moreover, under the counterintuitive order, the pump parameters have a wider adjustment range of system conversion efficiency than the coupling delay parameters. We synthesize the white laser source through the following steps. First, we change the pump intensity to roughly adjust the conversion efficiency of the system, and then vary the coupling delay parameters to fine-tune RGB lasers output power ratio. Compared with the scheme based on frequency conversion with Stark-chirped rapid adiabatic passage to synthesize white laser source, the RGB lasers output of the new scheme can achieve better stability. The results of the investigation can provide a theoretical foundation for the research of novel optical devices.

Reconstruction of Temperature Field in Coal-fired Boiler Based on Limited Flame Image Information

ABSTRACT The internal combustion system of coal-fired boiler is a complex nonlinear system with large delay and strong coupling. The most basic requirement for the boiler combustion system is that the combustion flame inside the boiler is stable and uniform, and the flame temperature is the most important combustion characteristic of the combustion flame, so the method of using flame temperature as an intermediate variable to control the boiler system has been proposed by many scholars. Compared with the traditional control method that takes the main steam pressure and other parameters as the control variable, this method greatly reduces the delay of the system, and at the same time improves the stability and economy of the system. In this paper, a temperature field reconstruction method based on the improved two-color temperature measurement method is proposed. In this method, a mathematical model between the gray value of flame image and the parameters to be solved in the improved two-color temperature measurement method is established by using BP neural network. Then, the temperature field in furnace is reconstructed according to the gray value of flame image. This method solves the problem that traditional furnace temperature field reconstruction algorithm can not obtain accurate radiation energy data, which makes the measurement and calculation of this method simplified, and the accuracy is improved. The simulation results show that the maximum temperature error of this method is 4% and the average error is around 0.68%, which can meet the requirements of industry.

Chirp pseudo‐noise signal and its receiving scheme for LEO enhanced GNSS

Low earth orbit (LEO) constellations for enhancing global navigation satellite systems (GNSS) have been investigated and studied due to their higher floor power and rapid geometric changes. Compared with GNSS based on medium orbit (MEO) and geostationary (GEO) satellites, the higher Doppler frequency is one of the problems faced by the LEO enhanced GNSS. For the traditional BPSK signal, the complexity has been greatly improved in acquisition, especially in the cold start state, which means more requests on the signal design. The periodic chirp signal modulated by the pseudo-noise code (chirp-PC) is proposed as the ranging signal of LEO enhanced GNSS. The coupling characteristic of Doppler and delay in chirp-PC can make the search frequency grid less than the traditional BPSK signal with the same acquisition performance, which can greatly reduce the complexity. However, the delay estimation obtained by the acquisition is biased. A tracking loop based on the chirp phase locked loop (CPLL) was proposed for removing the coupling of Doppler and time delay. The simulations show that the tracking loop can track chirp-PC signals stably and unbiasedly, and the tracking accuracy is similar to that of the corresponding BPSK signal. Therefore, the chirp-PC signal is a new choice for LEO enhanced GNSS with low acquisition complexity and equivalent tracking accuracy to BPSK.

Cluster synchronization in fractional-order network with nondelay and delay coupling

In this paper, cluster synchronization for fractional-order complex network with nondelay and delay coupling is investigated. Based on the stability theory of fractional-order systems and the properties of fractional derivative, both static and adaptive control schemes are adopted to design effective controllers. Sufficient condition for achieving cluster synchronization about static controllers is provided. From the condition, the needed feedback gains can be estimated by simple calculations. Further, adaptive control scheme is introduced to design unified controllers. Noticeably, in the adaptive controllers, the feedback gains need not be calculated in advance and can adjust themselves to the needed values according to updating laws. Finally, numerical simulations are given to demonstrate the correctness of the obtained results.

Passivity Analysis of Coupled Stochastic Neural Networks with Multiweights

In this paper, we devote to the investigation of passivity in two types of coupled stochastic neural networks (CSNNs) with multiweights and incompatible input and output dimensions. First, some new definitions of passivity are proposed for stochastic systems that may have incompatible input and output dimensions. By utilizing stochastic analysis techniques and Lyapunov functional method, several sufficient conditions are respectively developed for ensuring that CSNNs without and with multiple delay couplings can realize passivity. Besides, the synchronization criteria for CSNNs with multiweights are established by employing the results of output-strictly passivity. Finally, two simulation examples are given to illustrate the validity of the theoretical results.

Thermal-Driven Dynamic Shape Change of Bimetallic Nanoparticles Extends Hot Electron Lifetime of Pt/MoS2 Catalysts.

Using a combination of time-domain density functional theory and nonadiabatic (NA) molecular dynamics, we demonstrate that the replacement of noble Pt with cheap Sn in the Pt nanoparticles sensitized MoS2 greatly retards the photoexcited "hot" electron relaxation. The simulations show that Sn substitution causes significant geometry distortion associated with the Sn dopant detaching from the Pt nanoparticle base, which decreases the NA coupling and creates an isolated trap state distant from the electron donor state. Generally, smaller NA coupling delays "hot" electron relaxation. At the same time, the photoexcited electron on MoS2 first populates the nanoparticles state and then slowly goes to the trap state, following relaxation to the nanoparticle acceptor state over 1 ps. As a result, the "hot" electron lives over 3.5 times longer than that in pristine Pt/MoS2 system. The long-lived "hot" electron associated with the reduced cost establishes a novel concept for developing high-efficient and cost-effective photocatalysts and photovoltaics.

Process Control of Ball Mill Based on MPC-DO

The grinding process of the ball mill is an essential operation in metallurgical concentration plants. Generally, the model of the process is established as a multivariable system characterized with strong coupling and time delay. In previous research, a two-input-two-output model was applied to describe the system, in which some key indicators of the process were ignored. To this end, a three-input-three-output system is proposed to improve the model accuracy. Moreover, some practical and effective control strategies have been studied. The common control methods, including model predictive control (MPC), disturbance observer (DO), and so on, show poor performance when strong external and internal disturbances exist. In this paper, a composite control strategy based on MPC-DO is put forward to realize the control of the three-input-three-output ball mill system. The disturbances of the system consist of external disturbances including fluctuation of ore hardness and internal disturbances including model mismatches and strong couplings. The proposed MPC-DO controller includes a feedback control component based on MPC and a feed-forward compensation component based on DO. The simulation results indicate that the composite control scheme based on MPC-DO has good performance of tracking and anti-interference in process control of the ball mill.

Modeling and dynamics of double Hindmarsh–Rose neuron with memristor-based magnetic coupling and time delay* *Project supported by the National Natural Science Foundation of China (Grant No. 61873186).

The firing of a neuron model is mainly affected by the following factors: the magnetic field, external forcing current, time delay, etc. In this paper, a new time-delayed electromagnetic field coupled dual Hindmarsh – Rose neuron network model is constructed. A magnetically controlled threshold memristor is improved to represent the self-connected and the coupled magnetic fields triggered by the dynamic change of neuronal membrane potential for the adjacent neurons. Numerical simulation confirms that the coupled magnetic field can activate resting neurons to generate rich firing patterns, such as spiking firings, bursting firings, and chaotic firings, and enable neurons to generate larger firing amplitudes. The study also found that the strength of magnetic coupling in the neural network also affects the number of peaks in thedischarge of bursting firing. Based on the existing medical treatment background of mental illness, the effects of time lag in the coupling process against neuron firing are studied. The results confirm that the neurons can respond well to external stimuli and coupled magnetic field with appropriate time delay, and keep periodic firing under a wide range of external forcing current.

Modified Kuramoto model with inverse-square law coupling and spatial time delay

We propose a modified Kuramoto model to describe the hand-clapping synchronization observed in a large group of people. There are two different ingredients in our model: The finiteness of the sound speed leads to the time-delay effect across the system, and the large distance makes the sound intensity decrease significantly. These two different but related factors are expected to make the synchrony in a large audience hard to achieve. We numerically and analytically test which between the two factors can be crucial to explain the failure of hand-clapping synchronization in a large group of people. We conclude that the time-delay effect due to the finite sound speed is not as important as the effect of the decreasing sound intensity with distance as long as the audience size is not too big.

Intermittent delay stabilization of complex-valued stochastic complex network

In this paper, the stabilization issue for complex-valued stochastic Markovian switching complex network with time delay and time-varying coupling structure (CSMNDC) is investigated via intermittent delay feedback control. Different from intermittent control based on current state in previous work, a class of intermittent control on the basis of past state is designed for the first time, by combining with the advantages of aperiodically intermittent control and delay feedback control. Then, some sufficient conditions are derived to guarantee the exponential stability in mean square of CSMNDC based on Lyapunov method, graph theory as well as some techniques of inequalities. In particular, the stabilization of networks is studied on complex space directly without splitting their real and imaginary parts by using complex generalized Itô’s formula. Additionally, both delay feedback control and aperiodically intermittent control are employed to solve the stabilization issue for CSMNDC here. Whereafter, the stabilization issue of complex-valued stochastic Markovian switching Cohen-Grossberg neural network with time delay and time-varying coupling structure is researched as a practical application of our theoretical results. Ultimately, a numerical example is presented to verify the validity and effectiveness of the theoretical results.

Research on time synchronization of linear pulse-coupled oscillators model with delay in nearest neighbor wireless multi-hop networks

Time synchronicity works as a popular requirement in wireless sensor networks. Pulse-coupled oscillators similar to firefly flashing and synchronization via discrete pulse coupling are widely used in wireless sensor networks. In this article, we have studied the time synchronization with communication delay in the nearest neighbor network of distributed sensors, based on the pulse-coupled oscillators model of synchronicity achieved by biological systems. First, we present a linear pulse-coupled oscillators model with coupling delay and the model is used to analyze the wireless sensor networks synchronization with communication delay. Second, we mathematically analyze the firing behaviors in the linear pulse-coupled oscillators network using the delayed excitatory coupling and track the synchronization process of the two and multi-oscillators and obtain the synchronization conditions from the regression mapping. Finally, through the proposed model implementation in the wireless sensor networks simulation framework, we demonstrate that the multi-oscillators system can be synchronized from a random starting stage distribution under linear phase responding functions and the nearest neighbor communication. The results show that our approach can achieve clock synchronization in wireless sensor networks with delayed nearest neighbor communication.

Aperiodically intermittent pinning outer synchronization control for delayed complex dynamical networks with reaction-diffusion terms

In this paper, we mainly study the outer synchronization problem of complex dynamical networks (CDNs) involving delays and reaction-diffusion terms by aperiodically intermittent pinning control (AIPC). By using piecewise function and Lyapunov-Krasovskii functional method, some criteria for the complete and exponential outer synchronization of the corresponding CDNs with and without the coupling delay are obtained. Furthermore, two numerical examples are provided to illustrate the effectiveness of the obtained results.

Causal coupling inference from multivariate time series based on ordinal partition transition networks

Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.

Delayed and reduced intralimb muscular coupling during postural reactions in individuals with incomplete spinal cord injury.

Postural strategies are enabled by rapid muscle activation sequences to prevent a fall. Intralimb muscular couplings underlie these postural strategies are likely impaired after incomplete spinal cord injury (iSCI), leading to inappropriate postural reactions and increased fall risk; yet, the nature of these changes is unknown. Identify changes occurring in intralimb coupling following a perturbation in individuals with iSCI. Ten men with iSCI and eight age-matched controls (CTRL) stood on a force-platform that was randomly tilted forward or backward. Electromyographic (EMG) activity of the lower limb muscles was recorded, and coactivation or simultaneous facilitation/suppression between pairs of muscles was analyzed. Onset and duration of coupling latency, intralimb coupling delay, and amplitude ratios were measured in the distal (soleus [SOL]/tibialis anterior [TA]), proximal (biceps femoris [BF]/vastus lateralis [VL]), anterior (TA-VL), and posterior (SOL-BF) muscle couplings. In forward tilt, the main coupling was TA-SOL co-contraction for both groups, but the latency was longer and the duration shorter in SCI participants. In backward tilt, the TA-VL co-activation was the main coupling in CTRL (88 %), although it was also expressed by 60 % of SCI participant with a delayed latency. The facilitation/suppression of TA-SOL was the main coupling in SCI group (80 % vs 63 % in CTRL). Delayed coupling latencies were more pronounced in individuals with cervical iSCI and were correlated with the strength of lower limbs. Similar muscular couplings are present in both groups but are delayed, which might contribute to postural reaction deficits in individuals with iSCI.

Mixed ℋ -Infinity and Passive Synchronization of Markovian Jumping Neutral-Type Complex Dynamical Networks with Randomly Occurring Distributed Coupling Time-Varying Delays and Actuator Faults

This article examines mixed ℋ -infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network (MJNTCDN) models with randomly occurring coupling delays and actuator faults. The randomly occurring coupling delays are considered to design the complex dynamical networks in practice. These delays complied with certain Bernoulli distributed white noise sequences. The relevant data including limits of actuator faults, bounds of the nonlinear terms, and external disturbances are available for designing the controller structure. Novel Lyapunov–Krasovskii functional (LKF) is constructed to verify the stability of the error model and performance level. Jensen’s inequality and a new integral inequality are applied to derive the outcomes. Sufficient conditions for the synchronization error system (SES) are given in terms of linear matrix inequalities (LMIs), which can be analyzed easily by utilizing general numerical programming. Numerical illustrations are given to exhibit the usefulness of the obtained results.

Novel Heterogeneous Mode-Dependent Impulsive Synchronization for Piecewise T-S Fuzzy Probabilistic Coupled Delayed Neural Networks

This article investigates the heterogeneous impulsive synchronization for T-S fuzzy probabilistic coupled delayed neural networks (CDNNs) with mode-dependent parameters and piecewise membership functions. To begin with, a novel CDNNs model with adjustable coupling strength and probabilistic coupling delays is designed to ensure the accuracy of the CDNNs model. Meanwhile, the generalized isolated node with four types of mismatched parameters, named heterogeneous isolated delayed neural network, is first considered to extend the synchronization problem. Then, the mode-dependent fuzzy rules are introduced to design the novel model, which implies that switching signals and fuzzy processes are interdependent and can share information to communicate. To improve the hybrid controller’s reliability, the mode-dependent impulses are also developed here, in which the impulsive effects with different properties can occur at any moment in the switching interval. The exponential synchronization conditions are derived by means of the method of auxiliary state variables, Lyapunov–Krasovskii functional, average switching dwell period, and mode-dependent average impulsive dwell period. Moreover, the improved mode-dependent piecewise approximated membership functions are proposed to reduce the main results’ conservatism. Finally, a numerical example is provided to illustrate the effectiveness of the main results.

Circuit Model of an Overhead Transmission Line Considering the TEM‐Mode Formation Delay

When lightning strikes a transmission tower or overhead line, the lightning current generates a spherically expanding electromagnetic field. The propagation mode of an expanding electromagnetic wave is different from that of a transverse electromagnetic (TEM) wave, which is applied to the modeling of transmission lines. This paper presents an equivalent circuit model of an overhead transmission line that considers the TEM‐mode formation delay. The TEM‐mode formation delay is approximately represented by the first‐order delay functions in surge impedances and time delays in the mutual coupling from one line to the others. The proposed circuit model of the overhead line is introduced to the Alternative Transients Program version of Electromagnetic Transients Program using the MODELS language through a formulation in the phase domain based on Dommel's method. The results of the proposed model are validated through a comparison with the results of a three‐dimensional numerical electromagnetic analysis. Furthermore, the effect of the delay of the TEM‐mode formation on the voltages across the insulator strings of a lightning‐struck tower is described by referencing a previous experimental study conducted using an actual 500 kV transmission tower. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Interlayer synchronization of duplex time-delay network with delayed pinning impulses

This paper mainly focuses on interlayer synchronization in a duplex network setting with the intralayer coupling delay and the interlayer transmission delay via pinning impulsive control. Firstly, based on Lyapunov stability theory, a general pinning impulsive strategy guaranteeing interlayer synchronization is obtained. Then, the minimum number of pinned nodes and the maximum impulse interval needed theoretically for interlayer synchronization are obtained. Moreover, when some parameters, including the impulse strengths, the ratio of pinned nodes and the impulse interval, are fixed certain values, their influences on the stable region are further discussed. It is found that the stable region becomes smaller as the ratio of pinned nodes decreases, and gets larger as the impulse strength with no delay is closer to −1 and that with delay is closer to 0. At the same time, the small intralayer coupling delay and interlayer transmission delay can enlarge the stable region. Finally, numerical simulations are provided to verify the effectiveness and correctness of our results.