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相关概念视频

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

109
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
109
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

68
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
68
The Swing Equation01:21

The Swing Equation

343
The Swing Equation is a fundamental tool in power system dynamics, especially for analyzing the behavior of generating units like three-phase synchronous generators. This equation emerges from applying Newton's second law to the rotor of a generator, encompassing factors such as inertia, angular acceleration, and the interplay between mechanical and electrical torques.
In a steady-state operation, the mechanical torque (Τm) supplied to the generator is balanced by the electrical torque...
343
Turbine-Governor Control01:17

Turbine-Governor Control

176
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
176
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

187
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
187
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

87
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
87

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相关实验视频

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A Rapid Method for Modeling a Variable Cycle Engine
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识别直升机轴发动机非线性动态模型的智能方法

Serhii Vladov1, Arkadiusz Banasik2, Anatoliy Sachenko3,4

  • 1Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6, Peremohy Street, 39605 Kremenchuk, Ukraine.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
概括

这项研究开发了一个准确的直升机轮轴发动机动态模型,用于关键启动和加速模式. 改进的Elman神经网络在这些短暂阶段识别发动机参数时实现了99.88%的准确性.

关键词:
埃尔曼循环神经网络具有动态堆内存.准确度 准确度 准确度 准确度 准确度动态模型模型的动态模型发动机启动和加速.飞机轮轴发动机直升机轮轴发动机识别识别是为了确定.传感器 传感器 传感器培训培训培训培训培训培训培训

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科学领域:

  • 航空航天工程 航空航天工程
  • 计算流体动力学的流体动力学.
  • 人工智能的人工智能

背景情况:

  • 直升机轮轴发动机主要在稳定状态模式下运行 (85%).
  • 很大一部分 (15%) 运行在关键的不稳定和短暂模式,如启动和加速.
  • 准确的动态建模对于这些短暂模式对于性能和安全至关重要.

研究的目的:

  • 开发和增强直升机轮轴发动机的动态多模式模型.
  • 为了准确地识别不稳定和短暂模式 (启动和加速) 中的发动机行为.
  • 提高动态建模技术的性能和稳定性.

主要方法:

  • 利用车载传感器数据 (旋转转速,气体温度,燃料消耗) 进行模型开发.
  • 实现了一个改进的Elman循环神经网络与动态堆内存.
  • 应用时间延迟考虑和Butterworth波器预处理到训练算法.

主要成果:

  • 在启动和加速度期间识别发动机动态时达到99.88%的准确性.
  • 增强的Elman网络显示性能比传统网络提高了2.7倍.
  • 在120个训练时代中将模型的损失函数从2.5%降低到0.12%.

结论:

  • 开发的动态模型准确地捕捉了直升机轮轴发动机在短暂模式中的行为.
  • 改进的Elman神经网络为发动机监控提供了更强大的稳定性和性能.
  • 通过先进的建模,这些发现有助于通过先进的建模使直升机运行更安全,更有效.