Related Experiment Video
Updated: Dec 19, 2025
11:54
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
5.0K
An asymmetric interdependent networks model for cyber-physical systems.
Jiang Jiang1, Yongxiang Xia2, Sheng Xu1
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Chaos (Woodbury, N.Y.)
|June 4, 2020
Summary
Cyber-physical systems (CPSs) enhance smart grids but increase complexity. This study models CPS robustness, finding communication network coupling improves power system resilience, with asymmetric interdependencies highlighting parameter importance.
Area of Science:
- Complex Systems Science
- Cyber-Physical Systems Engineering
- Power Systems Analysis
Background:
- Cyber-physical systems (CPSs) integrate information technology and physical processes, with smart grids being a key application.
- The increasing complexity of interconnected CPSs, like smart grids, poses challenges for robustness assessment.
- Understanding the dynamics and interdependencies within CPSs is crucial for reliable operation.
Purpose of the Study:
- To propose a novel complex network model for assessing the robustness of cyber-physical systems.
- To analyze the real dynamics of cyber and physical components and their asymmetric interdependencies.
- To investigate the impact of communication network integration on smart grid robustness.
Main Methods:
- Development of a new CPS model from a complex network perspective.
- Incorporation of real dynamics for both cyber and physical system components.
- Analysis of asymmetric interdependencies between the power and communication networks.
Main Results:
- Coupling a smart grid with its communication network demonstrably enhances the power system's robustness.
- The interdependencies between power and communication networks are asymmetric, influencing overall system resilience.
- Specific system parameters significantly impact the robustness of the integrated cyber-physical system.
Conclusions:
- Complex network modeling provides valuable insights into CPS robustness.
- Strategic integration of communication networks can improve smart grid resilience.
- Careful consideration of asymmetric interdependencies and system parameters is vital for robust CPS design.
Related Concept Videos
01:26Signal and System
1.5K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.5K
01:21Mechanistic Models: Overview of Compartment Models
290
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
290
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
202
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
202
01:21Nervous System
3.1K
The nervous system coordinates body functions through its complex network of nerve cells, enabling sensation and movement. It is divided into two primary parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS is composed of the brain and the spinal cord. The brain acts as the body's control center, processing sensory information and coordinating responses. The spinal cord functions as a major signaling pathway for the brain and the rest of the body.
3.1K
01:26Classification of Systems-I
493
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
493
01:25Multimachine Stability
488
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
488