知识流的Petri网模型

Petri网是一种用以刻画通信机制的建模工具,在流程控制上得到了广泛应用。知识管理作为一种新的管理理论,其研究尚停留在对基本概念的剖析和激励方式的探索上。知识管理的对象是“知识”,对知识流动机制的刻画能够更好地描述企业的知识资产分布情况,方便监控并进行知识管理。用Petri网作为工具对知识的流动进行建模,从而得到知识流的概念模型。解决了对知识流动的共享性、并行性、复杂性、分布性等特性的刻画,为知识管理提供了知识流的建模工具,并为进一步对知识流实施监控提供了可能性。最后对知识流模型进行了应用研究,对Petri网对于知识流的建模能力予以说明。     

The Petri Net Kernel An infrastructure for building Petri net tools

The Petri Net Kernel is an infrastructure for building Petri net tools. It relieves the programmer of a Petri net tool from implementing standard operations on Petri nets and a graphical user interface. In this paper, we discuss the motivation, the concepts, and the implementation of the Petri Net Kernel. Published online: 25 July 2001     

A hierarchical optimization model for energy data flow in smart grid power systems

Environmental concerns and high prices of fossil fuels increase the feasibility of using renewable energy sources in smart grid. Smart grid technologies are currently being developed to provide efficient and clean power systems. Communication in smart grid allows different components to collaborate and exchange information. Traditionally, the utility company uses a central management unit to schedule energy generation, distribution, and consumption. Using centralized management in a very large scale smart grid forms a single point of failure and leads to serious scalability issues in terms of information delivery and processing. In this paper, a three-level hierarchical optimization approach is proposed to solve scalability, computational overhead, and minimize daily electricity cost through maximizing the used percentage of renewable energy. At level one, a single home or a group of homes are combined to form an optimized power entity (OPE) that satisfies its load demand from its own renewable energy sources (RESs). At level two, a group of OPEs satisfies energy requirements of all OPEs within the group. At level three, excess in renewable energy from different groups along with the energy from the grid is used to fulfill unsatisfied demands and the remaining energy are sent to storage devices.     

Planned further development of an integrated climatological data system for building energy modelling

Detailed hourly data for a number of meteorological variables is typically needed as inputs for dynamic simulation of building performance. The variables input vary with the aspect of performance being considered. This paper explains the meteorological data structure being developed by the Department of Building Science, University of Sheffield, UK on the SRC Rutherford interactive facility, to help modellers in different institutions to get more effective access to the specific meteorological data they need for their specific problem. A two stage facility is envisaged — an interactive search facility based on daily and monthly data to help users establish the data likely to be of value and a batch data transmission facility to transfer requested data to users for subsequent use in their own computing environment.     

A reliable opportunistic routing for smart grid with in-home power line communication networks

The topology of in-home power line communication (PLC) networks varies frequently, which makes traditional routing algorithms failure. To solve this problem, an end-to-end transmission time for remaining path (TTRP) metric-based opportunistic routing (TTRPOR) is proposed. Since a local broadcasting scheme is adopted, the algorithm can find the optimal path for forwarding packets in a dynamic PLC network. The closed-form of the outage probability for a PLC channel is derived to estimate the TTRP. It is proved that the average throughput can achieve maximum as the metric TTRP is utilized to sort candidate forwarding nodes. Numerical results show that the end-to-end throughput of networks with TTRPOR, outperforms that of the network adopting DSR and EXOR, especially for the case of varying-topology in-home PLC networks.     

Power quality enhancement in solar power with grid connected system using UPQC

The need to generate pollution free energy has triggered the effect towards the usage of solar energy interconnection with the grid. Consequently, the Photovoltaic (PV) panel interfaced with the grid causes the power quality problems such as a voltage harmonics and voltage distortion etc., Active power filters are the powerful tool for mitigation of harmonics. This work involves the use of single-phase Unified Power Quality Conditioner (UPQC) based on a unit vector template control algorithm for its functions with grid integration of photovoltaic, such as voltage sags/ swell, unit power factor correction, voltage and current harmonic cancelation. The unit vector template control algorithm includes a phase-locked loop (PLL) mechanism that is responsible for avoiding multiple zero crossings during highly distorted grid voltage detection. A unit vector template control with a PLL-based control algorithm is applied to the shunt and series inverters of PV grid connected UPQC. In addition to normalizing voltage and current disturbances, the proposed controller has the functions of phase detection and perfect grid synchronization. It is proposed that the system performance is fully verified by MATLAB simulation with the response of load variation, transient response, THD, voltage swell and sag. The Total Harmonic Distortions (THDs) of proposed grid integration of photovoltaic systems through single-phase unified power quality conditioner (UPQC) obtain the range of IEEE standard.     

Petri网与优化算法结合求解FMS调度研究综述

Petri网是基于图形的强有力的建模工具,被用于柔性制造系统调度问题的研究,然而,搜索整个可达树寻找最优调度方案是一个指数倍复杂的问题,由此人们想到利用人工智能算法搜索可达树的一部分获得近优解。该方法被认为是求解调度问题的极具前途的解决方案。从上世纪90年代初期以来,人们对此作了一些卓有成效的研究,对这些研究进行归纳总结,为采用该方法求解调度优化问题的研究提供参考。     

A smart grid incorporated with ML and IoT for a secure management system

The economy, national safety, and health care are tremendously dependent on the faithful supply of power. The communication technology integration and sensors in power systems have been authorized as a smart grid (SG) that is revolutionizing the model of power generation, distribution, monitoring, and control. To know the Smart Grid compatibility, many problems are required to be directed. The safety of the smart grid is the most challenging function and very crucial difficulties. This paper proposed, a safe demand-side management machine deploying machine learning for the Internet of Things authorized phase is recommended. The propounded demand-side management (DSM) machine protects the effective energy use based on their preferences. A particular flexibility sample was proposed to manage incursion into the smart grid. Anelastic agent prognosticates swindling companies, the ML classifiers are utilized. Promoted power management and intermediate control companies are recommended for processing power data to improve energy usage. The proposed project's effective simulation is implemented to examine the efficiency. The outcome of the analysis discloses that the planned demand-side management (DSM) machine is less susceptible to the incursion and it is sufficient to decrease the smart grid's energy consumption.     

面向应急指挥的跨区域电网一体化电力自动调度研究

为了提高跨区域电网一体化电力应急指挥能力,需要进行跨区域电网一体化电力自动调度研究,提出一种基于多源信息融合和均衡配置的面向应急指挥跨区域电网一体化电力自动调度方法。采用大数据信息重组方法进行跨区域电网一体化电力传输数据重构,采用分段均衡控制方法进行跨区域电网一体化电力传输调度的均衡配置,建立电力调度的输出链路均衡配置模型,采用空间区域重组方法实现跨区域的电网一体化信息融合处理,根据信息融合结果采用自适应链路均衡配置方法进行跨区域电网一体化电力自动调度的负载均衡设计,提高跨区域电网一体化电力自动调度的负载均衡性和调度信息融合性。仿真结果表明,采用该方法进行跨区域电网一体化电力自动调度的负载均衡性较好,调度的总线控制能力较强,提高了应急指挥能力。     

Security system architecture for data integrity based on a virtual smart meter overlay in a smart grid system

As an efficient and intelligent system for managing and monitoring electricity usage, the “smart grid” has many useful applications. It supports energy producers and energy consumers efficiently by supporting the estimation and provision of the proper amount of electricity at the proper time. The most serious threat to a smart grid system may be when information is altered or forged by attackers; the system could malfunction and could collapse. In some cases, the attackers could waste system resources on purpose and this could lead to disastrous results. In this paper, we propose security system architecture to provide the secure and reliable smart grid service. It includes a data authentication process to verify the aggregated data and virtual network management to detect a compromised area or node. It detects a false data injection attack and further prevents a denial of service attack efficiently with less overhead for the individual devices comprising the smart grid system.     

含风电电力系统的主从博弈经济调度

风力发电具有显著的随机性和波动性,对电力系统原有调度模式提出挑战.采用鲁棒优化处理风电不确定性,利用鲁棒优化蕴含的博弈思想,将风电场看作调度中心的一个虚拟博弈者,利用双层规划法建立了二者的主从博弈模型,将调度中心看作领导层,其决策目标为电网运行的成本最低,将风电场看作下属层,其决策目标是能保证系统实时安全运行的最大风电出力区间.由于考虑了火电机组的阀点效应,主从博弈模型呈现出非线性双层规划的数学特点,提出一种改进教与学算法与线性规划相嵌套的求解方法.最后,采用改进的10机39节点系统对模型以及求解方法的有效性进行了验证.     

Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network

Neural Computing and Applications - This paper proposes an intelligent active fault-tolerant system based on deep neural network. That is, an active fault-tolerant integrated navigation system is...     

计及混合潮流约束的热-电互联综合能源系统多目标优化调度

为满足多样化能源需求并提高能源网络的可靠性,研究多能源系统优化管理和混合潮流问题.针对多能源的网络约束及其耦合特性,构建整合分布式发电、热电联产、电力网络和区域供热网络的热-电互联综合能源系统模型.基于梯形模糊隶属函数构建模糊化软约束,量化电力网络节点电压和区域供热网络节点供给温度的技术不满意度.考虑系统的经济运行和网络节点的能源供给质量,提出一种计及混合潮流约束的热-电互联综合能源系统多目标优化调度策略,以最小化运行成本和网络节点状态变量的技术不满意度.采用epsilon约束算法精确求解该多目标优化问题的Pareto前沿.算例分析结果表明,所构建的模型和提出的算法可以有效提高系统能源供给质量和优化决策的准确性.研究成果进一步体现了所提出的多目标优化方案在兼顾经济性、能源供给质量以及复杂的运行约束,保证系统经济稳定运行等方面的效益.     

具有不可控变迁离散事件系统的Petri网控制器

考虑可用具有不可控变迁的受控Petri网建模的离散事件动态系统.提出了在这类系统中实现一组不等式约束的控制器的综合方法.所提出的控制器可通过给系统Petri网模型增加一些Petri网元素来实现,其计算是建立在本文提出的Petrl网的路增益概念基础上的.方法是系统、简单、计算量小.     

Orthogonal PSO algorithm for economic dispatch of thermal generating units under various power constraints in smart power grid

Particle swarm optimization (PSO) algorithm has been successfully applied to solve various optimization problems in science and engineering. One such popular one is called global PSO (GPSO) algorithm. One of major drawback of GPSO algorithm is the phenomenon of “zigzagging”, that leads to premature convergence by falling into local minima. In addition, the performance of GPSO algorithm deteriorates for high-dimensional problems, especially in presence of nonlinear constraints. In this paper we propose a novel algorithm called, orthogonal PSO (OPSO) that alleviates the shortcomings of the GPSO algorithm. In OPSO algorithm, the m particles of the swarm are divided into two groups: active group and passive group. The d particles of the active group undergo an orthogonal diagonalization process and are updated in such way that their position vectors become orthogonally diagonalized. In the OPSO algorithm, the particles are updated using only one guide, thus avoiding the conflict between the two guides that occurs in the GPSO algorithm. We applied the OPSO algorithm for solving economic dispatch (ED) problem by taking three power systems under several power constraints imposed by thermal generating units (TGUs) and smart power grid (SPG), for example, ramp rate limits, and prohibited operating zones. In addition, the OPSO algorithm is also applied for ten selected shifted and rotated CEC 2015 benchmark functions. With extensive simulation studies, we have shown superior performance of OPSO algorithm over GPSO algorithm and several existing evolutional computation techniques in terms of several performance measures, e.g., minimum cost, convergence rate, consistency, and stability. In addition, using unpaired t-Test, we have shown the statistical significance of the OPSO algorithm against several contending algorithms including top-ranked CEC 2015 algorithms.     

农村电网电能质量监测系统的设计与实现

随着电力系统中大量非线性负荷的出现,电能质量不断恶化.本文在研究电力系统电能质量指标的基础上,针对农村电网的特点,设计了一种能够监测和管理电能质量指标的新型的电能质量监测系统,并详细介绍了系统总体设计、监测终端设计与实现的方法.     

An integrated framework of user experience-oriented smart service requirement analysis for smart product service system development

The rapid deployment of digital technologies drives the conventional product service system (PSS) to shift into a new paradigm known as smart PSS. Empowered with smart capabilities, smart PSS has great potential in creating positive user experience (UX). The requirements analysis is an essential task for successful development of smart PSS with strong UX. Nevertheless, there is still a lack of research on the requirement analysis of smart PSS by incorporating smart-enabled UX perspective. Hence, this paper proposes a framework of user experience-oriented smart service requirement (UXO-SSR) analysis for smart PSS development. At first, the relevant factors (e.g., user personas and activity journey) are integrated to conceptualize the UX in smart PSS context. Second, a four-phase model is proposed for identifying UXO-SSR, which can provide an effective tool for acquiring UX requirements of smart PSS from holistic perspective. Third, a novel asymmetric trapezium cloud-based uncertain linguistic BWM approach is developed to evaluate the priority of the identified UXO-SSRs. The approach can appropriately handle the hybrid uncertainties (i.e., hesitancy, fuzziness and randomness) of linguistic evaluation information, thereby improving the accuracy of evaluation results. Finally, a case study of smart sleep service system is presented to demonstrate the feasibility and reliability of the proposed framework.     

An integrated catchment-coastal modelling system for real-time water quality forecasts

This paper presents a new marine water quality forecasting system for real-time and short-term predictions. The forecasting system comprises an integrated catchment-coastal model and a database management system. The integrated model is validated in an Irish catchment-coastal system using hydrodynamic and water quality data. The forecasting system was then used to provide short-term and real-time forecasts of Escherichia coli (E. coli) and Intestinal Enterococci concentrations (IE) in the near-shore coastal waters of Bray, Ireland. Two hind-cast scenarios were simulated: 5F in which predictions were based on rainfall forecasts only; and I-5F where forecasts of 5F were improved by incorporating real-time data. Results indicate that predictions of E. coli of scenario I-5F are improved. Also predicted IE concentrations by Scenario 5F were comparably higher than the I-5F predications, but due to the wide scatter of observed IE concentrations, the superiority of one scenario over the second could not be definitively determined.