Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

PI Controller: Design01:24

PI Controller: Design

1.1K
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
1.1K
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

390
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
390
PID Controller01:19

PID Controller

639
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
639
Multimachine Stability01:25

Multimachine Stability

539
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:
539
Load-frequency control01:28

Load-frequency control

608
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
608
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

358
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
358

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Enhanced power management in PV-Integrated hybrid energy storage systems using fuzzy 2DOF-PI control optimized by hippopotamus algorithm.

Scientific reports·2026
Same author

Menstrual cycle inspired latent diffusion model for image augmentation in energy production.

Scientific reports·2025
Same author

A dual GAN with identity blocks and pancreas-inspired loss for renewable energy optimization.

Scientific reports·2025
Same author

Rotor angle stability enhancement using DDPG reinforcement agent with Gorilla troops optimized input scaling factors.

Scientific reports·2025
Same author

Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction.

Scientific reports·2025
Same author

A novel 8-connected Pixel Identity GAN with Neutrosophic (ECP-IGANN) for missing imputation.

Scientific reports·2024

相关实验视频

Updated: Jan 12, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

1.0K

通过使用机器学习技术,加强微电网中的IP控制.

Eman Abo-Elkhair1, Ahmed E B Abu-Elanien2, Gamal M Mahmoud3

  • 1Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt.

Scientific reports
|November 1, 2025
PubMed
概括

机器学习通过动态调整比例整合 (PI) 控制器来增强微网控制. 这提高了可再生能源整合的稳定性和可靠性,优于传统方法.

关键词:
人工神经网络 (ANN) 的人工神经网络机器学习 (ML) 是指机器学习.微电网控制控制的方法强化学习 (RL) 是一种强化学习.可再生能源 (RES) 是一种可再生能源.总波扭曲 (THD) 是指总的波扭曲.

更多相关视频

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

1.0K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

相关实验视频

Last Updated: Jan 12, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

1.0K
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

1.0K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 控制系统 控制系统

背景情况:

  • 将可再生能源整合到电力系统中需要先进的控制策略来保持稳定性.
  • 传统的比例整合 (PI) 控制器在可再生能源 (RES) 的最佳参数调整方面遇到了困难.
  • 低于最佳的PI收益可能导致微电网的不稳定性和性能降低.

研究的目的:

  • 开发和评估用于微电网控制的机器学习 (ML) 增强框架.
  • 将人工神经网络 (ANN) 和强化学习 (RL) 与 PI 控制器相结合,以提高性能.
  • 解决分布式能源资源 (DER) 微电网中PI控制器的参数调节挑战.

主要方法:

  • 使用三种控制策略模拟DERs的微电网:传统PI,基于ANN的PI和基于RL的PI.
  • 基于实时操作数据和历史表现的PI控制器收益的动态调整.
  • 评价电压总波扭曲 (THD),沉降时间和频率稳定性.

主要成果:

  • 基于RL的PI控制器将电压THD降低到0.43% (与传统PI的16.99%相比).
  • 基于ANN的PI控制器实现了0.58%的THD,比传统方法提高了96.6%.
  • 用ML增强的控制器提高了75%的沉降时间和93%的频率稳定性,超过IEEE 1547标准.

结论:

  • 机器学习和深度学习技术显著提高了微电网的稳定性和可靠性.
  • 拟议的ML增强框架为先进的可再生能源管理提供了实际解决方案.
  • 使用ANN和RL进行动态增益调整,克服了传统PI控制器的局限性.