Modeling of combined discrete/continuous processes

Barton, Paul I.; Pantelides, C.C.

doi:10.1002/aic.690400608

Cited by 311 publications

(160 citation statements)

References 27 publications

(25 reference statements)

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“…Figure 3.5 shows the stabilization of the network dynamics after the loss of generator g 2 . In this case, stability is recovered by using reactive power compensation (c 1 and c 2 ), modification of the reference voltage of the on-load tap changer of tr 3 and load shedding (ld 2 and ld 3 ). In Section 3.3 and 3.4, an MPC-based method is proposed to determine the optimal corrective autonomously and automatically.…”

Section: Stabilizing Operationmentioning

confidence: 99%

“…Figures 3.17-3.18 show the evolution of the network variables after the loss of the generator. To manage the loss of this generator, the controller uses load shedding of ld 2 and transmission line l 9 is connected at time t = 60 s. At the next time step, i.e., t = 90 s, the controller disconnects 5% of load ld 3 . All the reactive compensation is used and the maximum excitation limit is reached for generator g 1 .…”

Section: Scenario 3: Generator Lossmentioning

confidence: 99%

“…Several object-oriented approaches have been developed over the years, e.g., [3,13,35,38,46]. These approaches typically support both high-level modeling by composition and detailed component modeling using equations.…”

Section: Simulation Toolsmentioning

confidence: 99%

See 2 more Smart Citations

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Leirens

Negenborn

2009

Intelligent Infrastructures

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Section: Stabilizing Operationmentioning

confidence: 99%

Section: Scenario 3: Generator Lossmentioning

confidence: 99%

See 1 more Smart Citation

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Leirens

Negenborn

2009

Intelligent Infrastructures

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“…It must also be pointed out that the implementation of control systems in real environments is always performed through digital or discrete control systems [3]. However, the discrete or hybrid modeling of chemical processing systems has only been considered recently [4].…”

Section: Introductionmentioning

confidence: 99%

Logic-Based Outer-Approximation Algorithm for Solving Discrete-Continuous Dynamic Optimization Problems

Ruiz‐Femenia

Flores‐Tlacuahuac

Grossmann

2014

Ind. Eng. Chem. Res.

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In this work we present an extension of the Logic Outer-Approximation algorithm for dealing with disjunctive discrete-continuous optimal control problems whose dynamic behavior is modeled in terms of differential-algebraic equations. Although the proposed algorithm can be applied to a wide variety of discrete-continouos optimal control problems we are mainly interested in problems where disjunctions are also present. Disjunctions are included to take into account only certain parts of the underlying model which become relevant under some processing conditions. By doing so the numerical robustness of the optimization algorithm improves since those parts of the model that are not active are discarded leading to a reduced size problem and avoiding potential model singularities. We test the proposed algorithm using 3 examples of different complex dynamic behavior. In all the case studies the number of iterations and the computational load required to get the optimal solutions is modest and the solutions are relatively easy to find

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“…The existing DAE based modelling languages such as gPROMS (Barton, 1992), VHDL-AMS (Heinkel, 2000, Christen, 1997, and MODELICA (Modelica Design Group, 2000) differ in many aspects. This work utilises an aircraft library (Moormann et al, 1999, Moormann, 2001) developed using MODELICA which allows to build domain-specific graphical component libraries and supports many features known from object-oriented programming such as inheritance, packages, etc.…”

Section: The Modelling and Simulation Approachmentioning

confidence: 99%

An Environment for the Integrated Modelling of Systems with Complex Continuous and Discrete Dynamics

Remelhe

Engell

Otter³

et al.

Modelling, Analysis, and Design of Hybrid Systems

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Abstract. The modelling and simulation of sophisticated technical systems is a demanding task. On the one hand, the physical part consists of a large number of subsystems which exhibit predominantly continuous dynamics, sometimes with (infrequent) discontinuities. On the other hand, the distributed computerised control systems constitute complex discretetime and discrete-event systems that require completely different modelling and simulation methods. For an evaluation of the behaviour and the performance of the overall system, both types of models have to be combined and simulated efficiently. This contribution presents the requirements for a modelling environment for such systems and discusses an approach that consists of object-oriented modelling and efficient simulation of the physical part using the physical systems modelling language MODELICA, a software environment for the definition of discrete-event models using various formalisms, and the integration of both parts of the system via model translation. The coordination of both parts is performed by the MODELICA simulator. The modelling environment called DES/M (discrete-event systems for Modelica) supports the interoperation of different domain specific discrete-event formalisms. To illustrate the usage of the environment, a laboratory batch plant model is presented. A more elaborate example is described in another contribution in this volume (Mosterman et al., 2002).

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Modeling of combined discrete/continuous processes

Cited by 311 publications

References 27 publications

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Logic-Based Outer-Approximation Algorithm for Solving Discrete-Continuous Dynamic Optimization Problems

An Environment for the Integrated Modelling of Systems with Complex Continuous and Discrete Dynamics

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