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相关概念视频

Multimachine Stability01:25

Multimachine Stability

104
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:
104
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

112
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
112
Survival Tree01:19

Survival Tree

42
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
42
Transient and Steady-state Response01:24

Transient and Steady-state Response

122
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
122
Stability01:28

Stability

67
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
67
Pole and System Stability01:24

Pole and System Stability

219
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
219

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相关实验视频

Updated: May 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

434

一种基于共享特征提取的自适应动力系统暂时稳定性评估方法.

Jiexiang Hu1,2, Le Zheng2, Wei Ai3

  • 1Changsha Power Supply Branch, State Grid Hunan Electric Power Co., Ltd., Changsha 410035, China.

iScience
|April 14, 2025
PubMed
概括

本研究介绍了一种使用共享特征提取的自适应电力系统暂时稳定性评估 (TSA) 方法. 尽管数据丢失和情景发生变化,它仍然保持高准确度,提高了稳定性和通用性.

关键词:
能源系统 能源系统工程 工程师 工程师 工程师

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 使用机器学习的电力系统暂时稳定性评估 (TSA) 在动态操作条件下与性能恶化作斗争.
  • 现有的方法通常需要大量的标记数据,并且缺乏适应不断变化的系统场景的适应性.

研究的目的:

  • 为电力系统开发一个强大且可转移的自适应式TSA方法.
  • 为了提高机器学习模型的可通用性和可持续性,在不同的运营环境中为TSA.

主要方法:

  • 使用域对抗对齐网络来训练共享特征提取器.
  • 从操作变化之前和之后对齐的动力系统数据,以捕获关键稳定性特征.
  • 实施了用于持续学习的同时数据和模型知识传输.

主要成果:

  • 在IEEE 39总线和2179节点系统上实现了超过96%的预测准确度,数据损失为30%.
  • 在不断变化的场景中保持97.99%的准确性,优于传统方法.
  • 证明了对新情景的广泛标记数据的依赖减少.

结论:

  • 拟议的自适应式TSA方法为现实世界电力系统应用提供了增强的通用性和稳定性.
  • 这种方法可以实现可持续的学习能力,有效地适应系统变化和数据稀缺.
  • 这种方法为可靠和高效的电力系统稳定性监测带来了重大进步.