arXiv subjects

Gaoxi Xiao

Publications and source records attributed to Gaoxi Xiao.

35 records · Page 2Linked to original sources

arXiv2018

Risk Identification of Power Transmission System with Renewable Energy

This paper aims to investigate the risk identification problem of power transmission system that is integrated with renewable energy sources. In practice, the fluctuation of power generation from renewable energy sources can lead to severe consequences to power transmission network. By treating the fluctuation of power generation as the control input, the risk identification problem is formulated with the aid of optimal control theory. Thus, a control approach is developed to identify the fluctuation of power generation that results in the worst-case cascading failures of power systems. Theoretical analysis is also conducted to obtain the necessary condition for the worst fluctuations of power generation. Finally, numerical simulations are implemented on IEEE 9 Bus System to demonstrate the effectiveness of the proposed approach.

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arXiv2018

Cooperative Control of TCSC to Relieve the Stress of Cyber-physical Power System

This paper addresses the cooperative control problem of Thyristor-Controlled Series Compensation (TCSC) to eliminate the stress of cyber-physical power system. The cyber-physical power system is composed of power network, protection and control center and communication network. A cooperative control algorithm of TCSC is developed to adjust the branch impedance and regulate the power flow. To reduce computation burdens, an approximate method is adopted to estimate the Jacobian matrix for the generation of control signals. In addition, a performance index is introduced to quantify the stress level of power system. Theoretical analysis is conducted to guarantee the convergence of performance index when the proposed cooperative control algorithm is implemented. Finally, numerical simulations are carried out to validate the cooperative control approach on IEEE 24 Bus Systems in uncertain environments.

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arXiv2017

A Model Predictive Approach to Preventing Cascading Failures of Power Systems

Power system blackouts are usually triggered by the initial contingency and then deteriorate as the branch outage spreads quickly. Thus, it is crucial to eliminate the propagation of cascading outages in its infancy. In this paper, a model predictive approach is proposed to protect power grids against cascading blackout by timely shedding load on buses. The cascading dynamics of power grids is described by a cascading outage model of transmission lines coupled with the DC power flow equation. In addition, a nonlinear convex optimization formulation is established to characterize the optimal load shedding for the mitigation of cascading failures. As a result, two protection schemes are designed on the basis of the optimization formulation. One scheme carries out the remedial action once for all, while the other focuses on the consecutive protection measures. Saddle point dynamics is employed to provide a numerical solution to the proposed optimization problem, and its global convergence is guaranteed in theory. Finally, numerical simulations on IEEE 57 Bus Systems have been implemented to validate the proposed approach in terms of preventing the degradation of power grids.

math.OC↗

arXiv2017

Comparing Different Models for Investigating Cascading Failures in Power Systems

This paper centers on the comparison of three different models that describe cascading failures of power systems. Specifically, these models are different in characterizing the physical properties of power networks and computing the branch power flow. Optimal control approach is applied on these models to identify the critical disturbances that result in the worst-case cascading failures of power networks. Then we compare these models by analyzing the critical disturbances and cascading processes. Significantly, comparison results on IEEE 9 bus system demonstrate that physical and electrical properties of power networks play a crucial role in the evolution of cascading failures, and it is necessary to take into account these properties appropriately while applying the model in the analysis of cascading blackout.

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arXiv2017

Identifying Critical Risks of Cascading Failures in Power Systems

Potential critical risks of cascading failures in power systems can be identified by exposing those critical electrical elements on which certain initial disturbances may cause maximum disruption to power transmission networks. In this work, we investigate cascading failures in power systems described by the direct current (DC) power flow equations, while initial disturbances take the form of altering admittance of elements. The disruption is quantified with the remaining transmission power at the end of cascading process. In particular, identifying the critical elements and the corresponding initial disturbances causing the worst-case cascading blackout is formulated as a dynamic optimization problem (DOP) in the framework of optimal control theory, where the entire propagation process of cascading failures is put under consideration. An Identifying Critical Risk Algorithm (ICRA) based on the maximum principle is proposed to solve the DOP. Simulation results on the IEEE 9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the effectiveness of the algorithm.

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arXiv2017

Opinion diversity and community formation in adaptive networks

It is interesting and of significant importance to investigate how network structures co-evolve with opinions. The existing models of such co-evolution typically lead to the final states where network nodes either reach a global consensus or break into separated communities, each of which holding its own community consensus. Such results, however, can hardly explain the richness of real-life observations that opinions are always diversified with no global or even community consensus, and people seldom, if not never, totally cut off themselves from dissenters. In this article, we show that, a simple model integrating consensus formation, link rewiring and opinion change allows complex system dynamics to emerge, driving the system into a dynamic equilibrium with co-existence of diversified opinions. Specifically, similar opinion holders may form into communities yet with no strict community consensus; and rather than being separated into disconnected communities, different communities remain to be interconnected by non-trivial proportion of inter-community links. More importantly, we show that the complex dynamics may lead to different numbers of communities at steady state with a given tolerance between different opinion holders. We construct a framework for theoretically analyzing the co-evolution process. Theoretical analysis and extensive simulation results reveal some useful insights into the complex co-evolution process, including the formation of dynamic equilibrium, the phase transition between different steady states with different numbers of communities, and the dynamics between opinion distribution and network modularity, etc.

physics.soc-ph↗

arXiv2017

Modeling and Identification of Worst-Case Cascading Failures in Power Systems

Cascading failures in power systems normally occur as a result of initial disturbance or faults on electrical elements, closely followed by errors of human operators. It remains a great challenge to systematically trace the source of cascading failures in power systems. In this paper, we develop a mathematical model to describe the cascading dynamics of transmission lines in power networks. In particular, the direct current (DC) power flow equation is employed to calculate the transmission power on the branches. By regarding the disturbances on the elements as the control inputs, we formulate the problem of determining the initial disturbances causing the cascading blackout of power grids in the framework of optimal control theory, and the magnitude of disturbances or faults on the selected branch can be obtained by solving the system of algebraic equations. Moreover, an iterative search algorithm is proposed to look for the optimal solution leading to the worst case of cascading failures. Theoretical analysis guarantees the asymptotic convergence of the iterative search algorithm. Finally, numerical simulations are carried out in IEEE 9 Bus System and IEEE 14 Bus System to validate the proposed approach.

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arXiv2017

Influence of random opinion change in complex networks

Opinion formation in the population has attracted extensive research interest. Various models have been introduced and studied, including the ones with individuals' free will allowing them to change their opinions. Such models, however, have not taken into account the fact that individuals with different opinions may have different levels of loyalty, and consequently, different probabilities of changing their opinions. In this work, we study on how the non-uniform distribution of the opinion changing probability may affect the final state of opinion distribution. By simulating a few different cases with different symmetric and asymmetric non-uniform patterns of opinion changing probabilities, we demonstrate the significant effects that the different loyalty levels of different opinions have on the final state of the opinion distribution.

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arXiv2017

Preliminary Study on Bit-String Modelling of Opinion Formation in Complex Networks

Opinion formation has been gaining increasing research interests recently, and various models have been proposed. These models, however, have their limitations, among which noticeably include (i) it is generally assumed that adjacent nodes holding similar opinions will further reduce their difference in between, while adjacent nodes holding significantly different opinions would either do nothing, or cut the link in between them; (ii) opinion mutation, which describes "opinion changes not due to neighborhood influences" in real life, is typically random. While such models enjoy their simplicity and nevertheless help reveal lots of useful insights, they lack the capability of describing many complex behaviors which we may easily observe in real life. In this paper, we propose a new bit-string modeling approach. Preliminary study on the new model demonstrates its great potentials in revealing complex behaviors of social opinion evolution and formation.

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arXiv2016

The robustness of interdependent networks under the interplay between cascading failures and virus propagation

Cascading failures and epidemic dynamics, as two successful application realms of network science, are usually investigated separately. How do they affect each other is still one open, interesting problem. In this letter, we couple both processes and put them into the framework of interdependent networks, where each network only supports one dynamical process. Of particular interest, they spontaneously form a feedback loop: virus propagation triggers cascading failures of systems while cascading failures suppress virus propagation. Especially, there exists crucial threshold of virus transmissibility, above which the interdependent networks collapse completely. In addition, the interdependent networks will be more vulnerable if the network supporting virus propagation has denser connections; otherwise the interdependent systems are robust against the change of connections in other layer(s). This discovery differs from previous framework of cascading failure in interdependent networks, where better robustness usually needs denser connections. Finally, to protect interdependent networks we also propose the control measures based on the identification capability. The larger this capability, more robustness the interdependent networks will be.

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arXiv2015

Network infection source identification under the SIRI model

We study the problem of identifying a single infection source in a network under the susceptible-infected-recovered-infected (SIRI) model. We describe the infection model via a state-space model, and utilizing a state propagation approach, we derive an algorithm known as the heterogeneous infection spreading source (HISS) estimator, to infer the infection source. The HISS estimator uses the observations of node states at a particular time, where the elapsed time from the start of the infection is unknown. It is able to incorporate side information (if any) of the observed states of a subset of nodes at different times, and of the prior probability of each infected or recovered node to be the infection source. Simulation results suggest that the HISS estimator outperforms the dynamic message pass- ing and Jordan center estimators over a wide range of infection and reinfection rates.

physics.soc-ph↗

arXiv2013

Link-based formalism for time evolution of adaptive networks

Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.

physics.soc-ph↗

arXiv2012

Epidemic reemergence in adaptive complex networks

The dynamic nature of system gives rise to dynamical features of epidemic spreading, such as oscillation and bistability. In this paper, by studying the epidemic spreading in growing networks, in which susceptible nodes may adaptively break the connections with infected ones yet avoid getting isolated, we reveal a new phenomenon - \emph{epidemic reemergence}, where the number of infected nodes is incubated at a low level for a long time and then bursts up for a short time. The process may repeat several times before the infection finally vanishes. Simulation results show that all the three factors, namely the network growth, the connection breaking and the isolation avoidance, are necessary for epidemic reemergence to happen. We present a simple theoretical analysis to explain the process of reemergence in detail. Our study may offer some useful insights helping explain the phenomenon of repeated epidemic explosions.

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arXiv2011

Dynamics of competing ideas in complex social systems

Individuals accepting an idea may intentionally or unintentionally impose influences in a certain neighborhood area, making other individuals within the area less likely or even impossible to accept other competing ideas. Depending on whether such influences strictly prohibit neighborhood individuals from accepting other ideas or not, we classify them into exclusive and non-exclusive influences, respectively. Our study reveals for the first time the rich and complex dynamics of two competing ideas with neighborhood influences in scale-free social networks: depending on whether they have exclusive or non-exclusive influences, the final state varies from multiple coexistence to founder control to exclusion, with different sizes of population accepting each of the ideas respectively. Such results provide insights helpful for better understanding the spread (and the control of spread) of ideas in human society.

physics.soc-ph↗

arXiv2011

Effects of fear factors in disease propagation

Upon an outbreak of a dangerous infectious disease, people generally tend to reduce their contacts with others in fear of getting infected. Such typical actions apparently help slow down the spreading of infection. Thanks to today's broad public media coverage, the fear factor may even contribute to prevent an outbreak from happening. We are motivated to study such effects by adopting a complex network approach. Firstly we evaluate the simple case where connections between individuals are randomly removed due to fear factor. Then we consider a different case where each individual keeps at least a few connections after contact reduction. Such a case is arguably more realistic since people may choose to keep a few social contacts, e.g., with their family members and closest friends, at any cost. Finally a study is conducted on the case where connection removals are carried out dynamically while the infection is spreading out. Analytical and simulation results show that the fear factor may not easily prevent an epidemic outbreak from happening in scale-free networks. However, it significantly reduces the fraction of the nodes ever getting infected during the outbreak.

physics.soc-ph↗

arXiv2006

On Degree-Based Decentralized Search in Complex Networks

Decentralized search aims to find the target node in a large network by using only local information. The applications of it include peer-to-peer file sharing, web search and anything else that requires locating a specific target in a complex system. In this paper, we examine the degree-based decentralized search method. Specifically, we evaluate the efficiency of the method in different cases with different amounts of available local information. In addition, we propose a simple refinement algorithm for significantly shortening the length of the route that has been found. Some insights useful for the future developments of efficient decentralized search schemes have been achieved.

cs.PF↗

arXiv2006

On Intentional Attacks and Protections in Complex Communication Networks

Being motivated by recent developments in the theory of complex networks, we examine the robustness of communication networks under intentional attack that takes down network nodes in a decreasing order of their nodal degrees. In this paper, we study two different effects that have been largely missed in the existing results: (i) some communication networks, like Internet, are too large for anyone to have global information of their topologies, which makes the accurate intentional attack practically impossible; and (ii) most attacks in communication networks are propagated from one node to its neighborhood node(s), utilizing local network-topology information only. We show that incomplete global information has different impacts to the intentional attack in different circumstances, while local information-based attacks can be actually highly efficient. Such insights would be helpful for the future developments of efficient network attack/protection schemes.

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