Generalized synchronization-based partial topology identification of complex networks

summary:In this paper, partial topology identification of complex networks is investigated based on synchronization method. We construct the response networks consisting of nodes with sim-pler dynamics than that in the drive networks. By constructing Lyapunov function, sufficient conditions are derived to guarantee partial topology identification by designing suitable controllers and parameters update laws. Several numerical examples are provided to illustrate the effectiveness of the theoretical results

Protocol selection for second-order consensus against disturbance

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings

A decision-making method for the operation flexibility enhancement of hybrid cascaded MTDC

To enable the integration of large-scale renewable energy, hybrid HVDC technology, which combines the technical advantages of LCC-HVDC and VSC-HVDC, is being gradually deployed in the power grid nowadays. The operation of the Wu-dong-de Hybrid DC Project and the Jian-su Hybrid cascaded MTDC Project has proved its advantages. However, for the simultaneous application of different converter station technologies in the system, the control strategies become complex. Issuing appropriate control instructions to ensure system stability according to operational requirements is an issue that cannot be ignored in decision-making. Even under abnormal conditions, when the topology changes due to various failure scenarios, reasonable decision-making and precise control instruction definitions are required. To achieve flexible planning of the MTDC system, this paper presents a decision-making method for control strategies of a hybrid cascaded MTDC system, which analyzes the control strategy combinations selected for normal and abnormal conditions of the MTDC system. In addition, a control instruction calculating method and decision-making process for precise control in normal and abnormal control conditions is proposed. Simulation results based on a five-terminal hybrid cascaded MTDC in PSCAD/EMTDC have verified the effectiveness of the proposed method

Adaptive droop control based power flow regulation and optimization in multi-terminal high voltage DC system

Minimization of the total transmission loss of an interconnected AC-DC grid plays an important role in the economic operation of the AC-DC grid. Different from the conventional AC grid where the transmission loss is usually minimized by reactive power regulation, the transmission loss of a meshed AC-DC grid can be optimized by adjusting the active power exchange between the AC and DC grids. Additionally, smaller DC voltage deviation after grid disturbances is very desirable since it can bring less impact to the operations of AC-DC grid. This thesis firstly presents two improved sequential power flow algorithms for modular multilevel converters (MMCs) based AC-DC grid under DC power-voltage droop control. An optimization algorithm is then proposed to minimize the total loss of the AC-DC grid and the overall DC voltage deviation after the change of operating conditions. Adaptive droop control is used in the proposed optimization algorithm in which the power references are control variables solved from the optimal AC-DC power flow. Active power sharing and voltage regulation are two of the major control challenges in the operation of the voltage source converter based multi-terminal high-voltage DC (VSC-MTDC) system. Conventional droop control methods for power-sharing in an MTDC grid lead to voltage deviation from the nominal value. Moreover, the power-sharing is inaccurate in the droop-controlled MTDC system. This thesis proposes two novel autonomous control methods to regulate average DC voltage and share the power burden proportionally, using the adaptive droop control strategy. The proposed Method I utilizes DC grid lossy model with the local voltage droop control (LVDC) strategy, while the proposed Method II adopts a modified common voltage droop control (MCVDC) based on DC grid lossless model. The regulation of active power flowing through one or multiple DC lines plays an important role to guarantee secure and economic operations of MTDC grids. This thesis proposes a new method to regulate DC line power flow based on the adaptive DC voltage droop control strategy in which the voltage references of the voltage droop controllers vary autonomously at post-contingencies. The main advantage of the proposed method is that it can avoid installation of extra equipment and thus the associated losses and costs in the power-converter-based power flow control methods. The proposed control approach does not require solving online global AC-DC power flow equations, leading to autonomous control.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster Based on Fuzzy Logic Reasoning

The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm

Carrying Capacity of a Population Diffusing in a Heterogeneous Environment

The carrying capacity of the environment for a population is one of the key concepts in ecology and it is incorporated in the growth term of reaction-diffusion equations describing populations in space. Analysis of reaction-diffusion models of populations in heterogeneous space have shown that, when the maximum growth rate and carrying capacity in a logistic growth function vary in space, conditions exist for which the total population size at equilibrium (i) exceeds the total population that which would occur in the absence of diffusion and (ii) exceeds that which would occur if the system were homogeneous and the total carrying capacity, computed as the integral over the local carrying capacities, was the same in the heterogeneous and homogeneous cases. We review here work over the past few years that has explained these apparently counter-intuitive results in terms of the way input of energy or another limiting resource (e.g., a nutrient) varies across the system. We report on both mathematical analysis and laboratory experiments confirming that total population size in a heterogeneous system with diffusion can exceed that in the system without diffusion. We further report, however, that when the resource of the population in question is explicitly modeled as a coupled variable, as in a reaction-diffusion chemostat model rather than a model with logistic growth, the total population in the heterogeneous system with diffusion cannot exceed the total population size in the corresponding homogeneous system in which the total carrying capacities are the same

Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Abstract Action recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions