2,191 research outputs found

    Petri net models of microgrids with distributed generators

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    This thesis introduces some basic concepts and control methods about a microgrid. Then, two hot issues are investigated. One is how to control multiple distributed generators; and another is how to model both discrete event and continuous behaviors of a microgrid. To address these two issues, this thesis work applies Petri nets to both modeling and control of a microgrid. Ordinary Petri nets, hybrid Petri nets, and finite capacity Petri nets, are introduced with their examples targeted at modeling the behavior of a microgrid. Coordination control of multiple distributed generators based on a Petri net model is proposed. Compared with multi-V/f control, the Petri net based control enables the system to operate with a longer stable time interval. Finally, a hybrid Petri net model is constructed to model both discrete event and continuous behaviors of an on-load tap changing transformer system. Compared with an algebraic method, the hybrid Petri net offers a clear and easy-to-understand method to describe such a system

    Power Management of A Microgrid with A Distributed Energy Storage in Grid Connected and Islanded Modes

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    Control and operation of a microgrid can be operated at grid connected or islanded modes. In this paper, the microgrid consists of a Diesel, PV modules with a distributed energy storage system, loads, and inverter. The purpose of power management is to control the stability of the system to cope with changes in load and interconnection with other networks. The stability of the microgrid is also obtained by setting the load connected to the system. Power management is also controls the operation of each plant based on the condition of the energy sources used as a source of generation. In islanded mode, the main goal of power management is to stabilize the system, in terms of frequency and voltage. In grid connected mode, typical objectives are to minimize the price of energy import at the point of common coupling (PCC)

    In-silico-Systemanalyse von Biopathways

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    Chen M. In silico systems analysis of biopathways. Bielefeld (Germany): Bielefeld University; 2004.In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways. This dissertation addresses these problems of in silico systems analysis of biopathways. We developed a software system to integrate the access to different databases, and exploited the Petri net methodology to model and simulate metabolic networks in cells. It develops a computer modeling and simulation technique based on Petri net methodology; investigates metabolic networks at a system level; proposes a markup language for biological data interchange among diverse biological simulators and Petri net tools; establishes a web-based information retrieval system for metabolic pathway prediction; presents an algorithm for metabolic pathway alignment; recommends a nomenclature of cellular signal transduction; and attempts to standardize the representation of biological pathways. Hybrid Petri net methodology is exploited to model metabolic networks. Kinetic modeling strategy and Petri net modeling algorithm are applied to perform the processes of elements functioning and model analysis. The proposed methodology can be used for all other metabolic networks or the virtual cell metabolism. Moreover, perspectives of Petri net modeling and simulation of metabolic networks are outlined. A proposal for the Biology Petri Net Markup Language (BioPNML) is presented. The concepts and terminology of the interchange format, as well as its syntax (which is based on XML) are introduced. BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to exchange biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators. A web-based metabolic information retrieval system, PathAligner, is developed in order to predict metabolic pathways from rudimentary elements of pathways. It extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. The system also provides a navigation platform to investigate metabolic related information, and transforms the output data into XML files for further modeling and simulation of the reconstructed pathway. An alignment algorithm to compare the similarity between metabolic pathways is presented. A new definition of the metabolic pathway is proposed. The pathway defined as a linear event sequence is practical for our alignment algorithm. The algorithm is based on strip scoring the similarity of 4-hierarchical EC numbers involved in the pathways. The algorithm described has been implemented and is in current use in the context of the PathAligner system. Furthermore, new methods for the classification and nomenclature of cellular signal transductions are recommended. For each type of characterized signal transduction, a unique ST number is provided. The Signal Transduction Classification Database (STCDB), based on the proposed classification and nomenclature, has been established. By merging the ST numbers with EC numbers, alignments of biopathways are possible. Finally, a detailed model of urea cycle that includes gene regulatory networks, metabolic pathways and signal transduction is demonstrated by using our approaches. A system biological interpretation of the observed behavior of the urea cycle and its related transcriptomics information is proposed to provide new insights for metabolic engineering and medical care

    Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata

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    Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules

    Model-based dependability analysis : state-of-the-art, challenges and future outlook

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    Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

    On the Petri Nets Control of the Multicellular Converter

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    International audienceThe serial multicellular structure of static converters is widely used for high power applications. However, some controls of these converters are unreliable and are sensitive to fluctuations in some of its parameters. In this article is proposed a control by Petri nets that has overcome these deficiencies by making the converter robust vis-à-vis fluctuations that can affect the load or the input voltage. This has been successfully tested by digital simulation in the case of a two-cell converter connected to a non-strictly dissipative non-linear load

    Comparison of Sliding Mode and Petri Nets Control for Multicellular Chopper

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    International audienceIn this paper a new class of power converters, serial multicellular chopper, will be studied. After recalling the dynamical equations of the converter, its hybrid dynamical behavior and properties are highlighted. This hybrid system induces new and difficult control problems. Then, two control strategies are proposed. The first is a sliding mode control and the second is a Petri Nets control. Some simulations are carried out to prove the efficiency and the robustness of the two controls for the case of a two cells converter connected to a nonlinear load. The comparison between sliding mode control and Petri nets control is carried out. Simulation results are provided to verify the conclusions
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