Quantification of complexity of power electronics based systems

El-Mezyani, Touria; Wilson, R.; Sattler, Michael; Srivastava, Sanjeev; Edrington, Chris S.; Cartes, D.A.

doi:10.1049/iet-est.2011.0019

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…In traditional symbolic sequence analysis, the process of hierarchical symbolization is prone to excessive symbol in a certain layer, and the overlapping symbols indirectly lead to the loss of information. Permutation entropy [20,21] is a special symbol sequence method based on information entropy [22] and phase space reconstruction theory. It can effectively quantify the complexity of a time sequence.…”

Section: Scope and Contribution Of This Studymentioning

confidence: 99%

Quantifying the Nonlinear Dynamic Behavior of the DC-DC Converter via Permutation Entropy

et al. 2018

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Quantifying nonlinear dynamic behaviors, such as bifurcation and chaos, in nonlinear systems are currently being investigated. In this paper, permutation entropy is used to characterize these complex phenomena in nonlinear direct current-direct current (DC-DC) converter systems. A mode switching time sequence (MSTS), containing the information from different periodic states, is obtained in a DC-DC converter by reading the inductor current when altering the switching mode. To obtain the nonlinear characteristics of this system, the concept of permutation entropy of symbolic probability distribution properties is introduced and the structure of the chaotic system is reproduced based on the theory of phase space reconstruction. A variety of nonlinear dynamic features of the DC-DC converter are analyzed using the MSTS and permutation entropy. Finally, a current-mode-controlled buck converter is reviewed as a case to study the quantification of nonlinear phenomena using permutation entropy as one of the system parameters changes.

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Section: Scope and Contribution Of This Studymentioning

confidence: 99%

Quantifying the Nonlinear Dynamic Behavior of the DC-DC Converter via Permutation Entropy

et al. 2018

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“…Since Shannon introduced the theory of information entropy in 1948 [ 36 ], it has been widely used in complexity measurement [ 37 , 38 , 39 , 40 , 41 ]. Entropy is also used to quantitatively describe the uncertainty and order degree of system structure, which can facilitate complexity evaluation [ 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Complexity Evaluation of an Environmental Control and Life-Support System Based on Directed and Undirected Structural Entropy Methods

Yang

et al. 2021

Entropy

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During manned space missions, an environmental control and life-support system (ECLSS) is employed to meet the life-supporting requirements of astronauts. The ECLSS is a type of hierarchical system, with subsystem—component—single machines, forming a complex structure. Therefore, system-level conceptual designing and performance evaluation of the ECLSS must be conducted. This study reports the top-level scheme of ECLSS, including the subsystems of atmosphere revitalization, water management, and waste management. We propose two schemes based on the design criteria of improving closure and reducing power consumption. In this study, we use the structural entropy method (SEM) to calculate the system order degree to quantitatively evaluate the ECLSS complexity at the top level. The complexity of the system evaluated by directed SEM and undirected SEM presents different rules. The results show that the change in the system structure caused by the replacement of some single technologies will not have great impact on the overall system complexity. The top-level scheme design and complexity evaluation presented in this study may provide technical support for the development of ECLSS in future manned spaceflights.

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“…The state of the system will no longer be given by the states of the components, but results from their nonlinear cyber-physical interactions. The specific evolution, which is a result of interactions in the system and does not coincide with any of the components' behaviors, is called emergent behavior [2].…”

Section: Introductionmentioning

confidence: 99%

“…Emergent behavior is in contradiction with linear system behavior because a linear superposition of subsystems' behaviors results again in a linear system with full predictability and easy calculation. In emergent systems, nonlinear interactions between system components preempt linearization of the system behavior [2], making classical small-signal modeling impossible. To analyze the system behavior of future distribution systems at a meaningful scale prior to their implementation in the real world, a scalable modeling and simulation approach is required which is able to create the emergent system behavior based on nonlinear interactions of the system's components.…”

Section: Introductionmentioning

confidence: 99%

Enabling the Analysis of Emergent Behavior in Future Electrical Distribution Systems Using Agent‐Based Modeling and Simulation

et al. 2018

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In future electrical distribution systems, component heterogeneity and their cyber-physical interactions through electrical lines and communication lead to emergent system behavior. As the distribution systems represent the largest part of an energy system with respect to the number of nodes and components, large-scale studies of their emergent behavior are vital for the development of decentralized control strategies. This paper presents and evaluates DistAIX, a novel agent-based modeling and simulation tool to conduct such studies. The major novelty is a parallelization of the entire model—including the power system, communication system, control, and all interactions—using processes instead of threads. Thereby, a distribution of the simulation to multiple computing nodes with a distributed memory architecture becomes possible. This makes DistAIX scalable and allows the inclusion of as many processing units in the simulation as desired. The scalability of DistAIX is demonstrated by simulations of large-scale scenarios. Additionally, the capability of observing emergent behavior is demonstrated for an exemplary distribution grid with a large number of interacting components.

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Quantification of complexity of power electronics based systems

Cited by 9 publications

References 14 publications

Quantifying the Nonlinear Dynamic Behavior of the DC-DC Converter via Permutation Entropy

Quantifying the Nonlinear Dynamic Behavior of the DC-DC Converter via Permutation Entropy

Complexity Evaluation of an Environmental Control and Life-Support System Based on Directed and Undirected Structural Entropy Methods

Enabling the Analysis of Emergent Behavior in Future Electrical Distribution Systems Using Agent‐Based Modeling and Simulation

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