Diagnosis of a class of distributed discrete-event systems

Discrete-event modeling can be applied to a large variety of physical systems, in order to support different tasks, including fault detection, monitoring, and diagnosis. The paper focuses on the model-based diagnosis of a class of distributed discrete-event systems, called active systems. An active system, which is designed to react to possibly harmful external events, is modeled as a network of communicating automata, where each automaton describes the behavior of a system component. Unlike other approaches based on the synchronous composition of automata and on the off-line creation of the model of the entire system, the proposed diagnostic technique deals with asynchronous events and does not need any global diagnoser to be built. Instead, the current approach features a problem-decomposition/solution-composition nature whose core is the online progressive reconstruction of the behavior of the active system, guided by the available observations. This incremental technique makes effective the diagnosis of large-scale active systems, for which the one-shot generation of the global model is almost invariably impossible in practice. The diagnostic method encompasses three steps: (1) reconstruction planning; (2) behavior reconstruction; and (3) diagnosis generation. Step 1 draws a hierarchical decomposition of the behavior reconstruction problem. Reconstruction is made in Step 2, where an intensional representation of all the dynamic behaviors which are consistent with the available system observation is produced. Diagnosis is eventually generated in Step 3, based on the faulty evolutions incorporated within the reconstructed behaviors. The modular approach is formally defined, with special emphasis on Steps 2 and 3, and applied to the power transmission network domain     

EDEN: An Intelligent Software Environment for Diagnosis of Discrete-Event Systems

A software environment, called EDEN, that prototypes a recent approach to model-based diagnosis of discrete-event systems, is presented. The environment integrates a specification language, called SMILE, a model base, and a diagnostic engine. SMILE enables the user to create libraries of models and systems, which are permanently stored in the model base, wherein both final and intermediate results of the diagnostic sessions are hosted as well. Given the observation of a physical system gathered during its reaction to an external event, the diagnostic engine performs the a posteriori reconstruction of all the possible evolutions of the system over time and, then, draws candidate diagnoses out of them. The diagnostic method is described using a simplified example within the domain of power transmission networks. Strong points of the method include compositional modeling, support for model update, ability to focus on any sub-system, amenability to parallel execution, management of multiple faults, and broad notions of system and observation.     

一类异步网络控制系统的分析与建模

针对具有异步时序和状态反馈的异步网络化控制系统，分析了在短时延单包传输、多包传输、单包传输有数据包丢失、多包传输有数据包丢失和长时延单包传输等不同网络条件下网络化控制系统的特点，在此基础上首次提出了各种不同条件下网络化控制系统的统一建模方法。这种包含系统噪声的离散状态空间模型的建立，为网络化控制系统的准
准确辨识和有效控制奠定了基础。     

A diagnostic environment for automaton networks

Automated diagnosis of communicating‐automaton networks (CANs) is a complex task, which is typically faced by model‐based reasoning, where the behavior of the network is reconstructed based on its observation. This task may take advantage of knowledge‐compilation techniques, where a large amount of reasoning is anticipated off‐line (when the diagnostic process is not active), by simulating the behavior of the network and by constructing suitable data structures embedding diagnostic information. This (general‐purpose) compiled knowledge is exploited on‐line (when the diagnostic process becomes active), so as to generate the solution to the problem. Additional reusable (special‐purpose) compiled knowledge is generated on‐line when solving new problems. A software environment for the diagnosis of CANs has been developed in the C programming language with the support of the PostgreSQL relational database management system, under the Linux operating system. It supports the modeling and preprocessing of CANs as well as the solution of diagnostic problems, including on‐line knowledge compilation. The environment has been tested through a variety of experiments. Results are encouraging and provide a valuable feedback for further work. Copyright © 2006 John Wiley & Sons, Ltd.     

基于稀疏贝叶斯算法的演化博弈网络重构

演化博弈是自然和社会系统中一种常见的互动类型,探知演化博弈网络的拓扑结构是理解其功能和集体行为的基础。对于演化博弈网络,个体的博弈行为通常难以用动力学方程进行描述,而且相关的时序信息一般数量有限并且是离散的,因此在有限的个体博弈信息下重构网络的结构有着重要的研究意义。本文基于稀疏贝叶斯学习方法进一步发展了演化博弈网络的重构方法,通过在随机网络和小世界网络上的数值模拟验证该方法的有效性。与先前的基于L1范数的方法相比,该方法同样能够在较少的个体博弈信息下实现网络的重构,并且具有更高的重构效率和更强的噪声鲁棒性。     

离散事件系统基于模型诊断的研究进展

近年来,针对离散事件系统的基于模型诊断方法在大型通讯网络、电网故障诊断等领域获得了成功应用,成为人工智能与控制工程领域的热门研究课题。介绍了针对离散事件系统的基于模型诊断的基本思想与建模方法,从不同的角度对使用自动机建模的各种主要诊断方法进行了评析与比较;讨论了系统可诊断性判定方法的研究进展。从系统建模、分布式在线诊断、不完备模型下的诊断以及系统实现等方面,展望了针对离散事件系统的基于模型诊断领域中有待解决的问题。     

A knowledge-based architecture for distributed fault analysis in power networks

Power industry around the world is facing several changes since deregulation with constant pressure put on improving security, reliability and quality of the power supply. Computational fault analysis and diagnosis of power networks have been active research topics with several theories and algorithms proposed. This paper proposes a distributed diagnostic algorithm for fault analysis in power networks. Distributed architecture for power network fault analysis (DAPFA) is an intelligent, model-based diagnostic algorithm that incorporates a hierarchical power network representation and model. The architecture is based on the industry’s substation automation implementation standards. The structural and functional model is a multi-level representation with each level depicting a more complex grouping of components than its predecessor in the hierarchy. The distributed functional representation contains the behavioral knowledge related to the components of that level in the structural model.The diagnostic algorithm of DAPFA is designed to perform fault analysis in pre-diagnostic and diagnostic levels. Pre-diagnostic phase provides real-time analysis while the diagnostic phase provides the final diagnostic analysis. The diagnostic algorithm incorporates knowledge-based and model-based reasoning mechanisms with one of the model levels represented as a network of neural nets. The relevant algorithms and techniques are discussed. The resulting system has been implemented on a New Zealand sub-system and the results are analyzed.     

具有长时延和丢包的网络控制系统稳定性分析

针对同时出现长网络诱导时延和丢包的网络控制系统,研究了带不确定性的网络控制系统的稳定性问题。基于一定的数据包丢失率,系统被建模成带结构事件率约束的异步动态系统。利用李亚普诺夫方法和线性矩阵不等式相关知识,结合异步动态系统的稳定性理论,推出了使不确定网络控制系统指数稳定的充分条件,并给出了保证系统指数稳定的数据传输成功率满足范围;最后利用MATLAB仿真的数值例子验证了此方法的有效性和可行性。     

Basins of attraction in fully asynchronous discrete-time discrete-state dynamic networks

This paper gives a formulation of the basins of fixed point states of fully asynchronous discrete-time discrete-state dynamic networks. That formulation provides two advantages. The first one is to point out the different behaviors between synchronous and asynchronous modes and the second one is to allow us to easily deduce an algorithm which determines the behavior of a network for a given initialization. In the context of this study, we consider networks of a large number of neurons (or units, processors, etc.), whose dynamic is fully asynchronous with overlapping updates . We suppose that the neurons take a finite number of discrete states and that the updating scheme is discrete in time. We make no hypothesis on the activation functions of the nodes, so that the dynamic of the network may have multiple cycles and/or basins. Our results are illustrated on a simple example of a fully asynchronous Hopfield neural network.     

Network Expansion to Mitigate Market Power

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.     

Forecasting and Prediction Applications in the Field of Power Engineering

Within the field of power engineering, forecasting and prediction techniques underpin a number of applications such as fault diagnosis, condition monitoring and planning. These applications can now be enhanced due to the improved forecasting and prediction capabilities offered through the use of artificial neural networks. This paper demonstrates the maturity of neural network based forecasting and prediction through four diverse case studies. In each case study the authors have developed diagnostic, monitoring or planning applications (within the power engineering field) using neural networks and industrial data. The engineering applications discussed in the paper are: condition monitoring and fault diagnosis applied to a power transformer; condition monitoring and fault diagnosis applied to an industrial gas turbine; electrical load forecasting; monitoring of the refuelling process within a nuclear power station. For each case study the data sources, data preparation, neural network methods and implementation of the resulting application is discussed. The paper will show that the forecasting and prediction techniques discussed offer significant engineering benefits in terms of enhanced decision support capabilities.     

A bridged diagnostic method for the monitoring of polymorphic discrete-event systems.

Diagnosis of discrete-event systems (DESs) is a challenging problem that has been tackled both by automatic control and artificial intelligence communities. The relevant approaches share similarities, including modeling by automata, compositional modeling, and model-based reasoning. This paper aims to bridge two complementary approaches from these communities, namely, the diagnoser approach and the active system approach, respectively. The more significant shortcomings of such approaches are, on the one side, the need for the generation of the global system model and, on the other, the lack of monitoring capabilities. The former makes the application of the diagnoser approach prohibitive in real contexts, where the system model is too large to be generated, even offline. The latter requires the completion of the system observation before starting the diagnostic task, thereby, making the monitoring of the system. impossible. The bridged diagnostic method subsumes, to a large extent on the peculiarities of the two approaches and is capable of coping with an extended class of DESs that integrate both synchronous and asynchronous behavior. The bridge is built by extending the active system approach by means of several enhanced techniques, which eventually, allow the efficient monitoring of polymorphic DESs. Upon the occurrence of each system message, two pieces of diagnostic information are generated, namely, the snapshot and historic diagnostic sets. While the former accounts for the faults pertinent to the newly generated message only, the latter is based on the whole sequence of messages yielded by the system during operation.     

电力大数据在配电网规划中的应用及效果

当前配电网规划中存在的规划存在不合理、数据处理效果不佳、系统故障诊断效率偏低等问题,为提高配电网规划水平,本文结合大数据在电力系统应用的时代背景,提出电力大数据在配电网规划中的应用对策,并分析电力大数据在配电网规划中的应用效果。电力大数据在配电网规划中的应用具有重要意义,能够实现对配电数据的精准处理,对电网状态进行准确评估,同时也为配电网结构优化提供技术支持。具体应用过程中,应该在主动配电网数据调度、主动配电网数据规划管理、配电网电压数据规划管理中运用电力大数据,并把握技术要点,合理进行配电网规划。实际应用表明,电力大数据满足配电网规划需要,能够提高配电系统数据信息处理效果和系统故障诊断效率。     

Computational intelligence in management of ATM networks

Designing effective control strategies for asynchronous transfer mode (ATM) networks is known to be difficult because of the complexity of the structure of networks, nature of the services supported, and variety of dynamic parameters involved. Additionally, the uncertainties involved in identification of the network parameters cause analytical modeling of ATM networks to be almost impossible. This renders the application of classical control system design methods (which rely on the availability of these models) to the problem even harder. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling techniques that have the ability to cope with these difficulties to devise effective, robust ATM network management schemes. Those schemes employ artificial neural networks, fuzzy systems and design methods based on evolutionary computation. In this survey, the current state of ATM network management research employing these techniques as reported in the technical literature is summarized. The salient features of the methods employed are reviewed.     

On modeling of electrical cyber-physical systems considering cyber security

This paper establishes a new framework for modeling electrical cyber-physical systems (ECPSs), integrating both power grids and communication networks. To model the communication network associated with a power transmission grid, we use a mesh network that considers the features of power transmission grids such as high-voltage levels, long-transmission distances, and equal importance of each node. Moreover, bidirectional links including data uploading channels and command downloading channels are assumed to connect every node in the communication network and a corresponding physical node in the transmission grid. Based on this model, the fragility of an ECPS is analyzed under various cyber attacks including denial-of-service (DoS) attacks, replay attacks, and false data injection attacks. Control strategies such as load shedding and relay protection are also verified using this model against these attacks.     

A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater

This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.     

New polynomial time algorithms to compute a set of Pareto optimal paths for multi-objective shortest path problems

Multi-objective shortest path problem (MOSPP) is an active area of research because of its application in a large number of systems such as transportation systems, communication systems, power transmission systems, pipeline distribution systems of water, gas, blood and drainage, neural decision systems, production planning and project planning. In these networks it becomes necessary to find the best path from one node to a specified or all other nodes. The computational complexity of the existing algorithms in the literature to compute all Pareto minimum paths from a specified source node to all other nodes in an MOSPP is of exponential order in the worst case. Instead of generating all the values of the Pareto minimum paths in exponential time, we propose an algorithm to find a set of values of the Pareto minimum paths in polynomial time, which is very significant in many contexts. If an MOSPP of a network is having negative cycle, all the existing algorithm only indicate the existence of the negative cycle, that too after exponential number of operations. However, applying the proposed algorithm, we can find a set of Pareto minimum paths of any MOSPP of a network even if it contains negative cycles. The proposed algorithm is illustrated with examples.     

Characterizing asynchronous variable latencies through probability distribution functions

Asynchronous systems are attracting the interest of the designer community because of several useful features for sub-micron technologies: process-variation tolerant, low-power, removal of the clock tree generation, etc. One of the main problems for the simulation of these systems is the variable computation delays of their modules, that compute as fast as possible under the actual conditions of the system. This behavior complicates the high-level simulation of such systems and it is the main reason for the lack of simulation tools devoted to asynchronous microarchitectures. In this paper we present a modeling method useful for this kind of systems that describes the variable computation delay of an asynchronous circuit by using probability distribution functions. This method is deployed in an architectural simulator of a 64-bit superscalar asynchronous microarchitecture where the computation delay of each one of the modules of the microarchitecture was characterized through a probability distribution function. The experimental results show that the asynchronous behavior is successfully modeled, and the architectural simulations of standard benchmarks is affordable in terms of wall-clock simulation time.     

可变频变压器的建模仿真与分析

采用高压直流输电方式连接两个异步电网存在谐波干扰、换相失败、难以实现功率平滑调节、无功功率不能随有功功率传输等问题。文章介绍了可变频变压器的的基本结构及原理,研究了可变频变压器的数学模型及控制模型,给出了可变频变压器传输功率与直流驱动电动机提供的驱动转矩之间的数学关系式,在此基础上采用PSCAD软件搭建了可变频变压器模型并进行了仿真研究。仿真结果表明,可变频变压器可以实现两个异步电网之间的连接并进行功率双向平滑传输,响应速度快;通过转速控制器控制直流驱动电动机的转速可使旋转变压器的转速恒定,以弥补两个电网的频差,维持系统平衡。     

向量复杂网络及其在复杂系统建模中的应用

本文在传统的复杂网络建模基础上提出了一种基于向量复杂网络的建模方法,该方法能够针对节点的异质性对复杂的系统进行整体化建模.基于业务特性,通过分层建模的思想对不同业务导向的网络进行建模,采用基于业务驱动的复杂系统建模算法,将不同网络模型进行组网.进而研究具有多种类型的复杂系统的建模.最后,以智能电网为例验证了该方法的有效性.