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相关概念视频

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

162
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...
162
Electro-mechanical Systems01:19

Electro-mechanical Systems

895
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
895
Multimachine Stability01:25

Multimachine Stability

117
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:
117
Torque On A Current Loop In A Magnetic Field01:13

Torque On A Current Loop In A Magnetic Field

3.7K
The most common application of magnetic force on current-carrying wires is in electric motors. These consist of loops of wire, which are placed between the magnets with a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate, thus converting electrical energy to mechanical energy.
Consider a rectangular current-carrying loop containing N turns of wire, placed in a uniform magnetic field. The net force on a current-carrying loop...
3.7K
Force On A Current Loop In A Magnetic Field01:17

Force On A Current Loop In A Magnetic Field

3.1K
Magnetic forces on wires carrying current are most frequently applied in motors. A DC motor is a device that converts electrical energy into mechanical work. In motors, wire loops are enclosed in a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate. The direction of the current is reversed once the loop's surface area is lined up with the magnetic field, causing a constant torque on the loop. During the process,...
3.1K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

89
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
89

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相关实验视频

Updated: May 17, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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基于LSOSMO算法的永磁同步电机的参数识别.

Songcan Zhang1, Zhuangzhuang Zhou1, Yi Pu2

  • 1College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括

本研究引入了一种新的LSOSMO算法,用于识别常磁同步电机 (PMSM) 参数,提高伺服系统控制的准确性和稳定性. 增强方法实现的识别错误低于1.1%.

关键词:
在PMSMs中使用PMSMs.适应性的t分布方法.混乱的地图绘制.基于反对的学习策略.参数识别 参数识别蜘蛛优化算法

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科学领域:

  • 电气工程 电气工程
  • 控制系统 控制系统
  • 优化算法 优化算法

背景情况:

  • 准确的参数识别对于伺服系统中永磁同步电机 (PMSM) 的最佳性能至关重要.
  • 像蜘蛛优化 (SMO) 算法这样的现有方法面临着挑战,包括缓慢的融合,局部最佳和不稳定的结果.
  • 需要精确识别的PMSM关键参数包括定子电阻 (R),dq轴电感 (Ld,Lq) 和流量连接 (ψf).

研究的目的:

  • 开发一种新的和改进的算法,用于精确识别PMSM电气参数.
  • 解决现有算法的局限性,特别是识别速度缓慢和倾向于落入局部最佳的趋势.
  • 为了提高参数识别的稳定性和准确性,以便更好地控制伺服系统.

主要方法:

  • 拟议的LSOSMO算法集成了逻辑-正弦混乱映射,动态概率自适应T分布和基于对立的学习.
  • 用物流-正弦混沌映射来提高SMO算法中初始人口的统一性.
  • 动态概率自适应T分布和基于对立的学习取代了贪的策略,以增强全球和本地搜索能力,提高融合速度.

主要成果:

  • 与其他五种算法相比,LSOSMO算法在识别PMSM参数方面表现出卓越的稳定性和准确性.
  • 四个关键参数 (R,Ld,Lq,psf) 的识别错误与真值相比始终低于1.1%.
  • 增强的算法显示了性能和融合速度的提高,验证了它的有效性.

结论:

  • LSOSMO算法为准确的PMSM参数识别提供了可靠和有效的解决方案.
  • 混沌映射,自适应T分布和基于对立的学习的整合显著提高了优化性能.
  • 这些发现证实了算法的潜力,可以提高依赖于PMSM的伺服系统的控制性能.