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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

724
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
724
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

582
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
582
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
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
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

829
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes...
829
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K

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

A V<sub>2</sub>CT<sub>x</sub>/V<sub>2</sub>O<sub>5</sub>/SnO<sub>2</sub> Ternary Heterostructure<i>-</i>Based Gas Sensor for Highly Selective Detection of Electrolyte Leakage in a Lithium<i>-</i>Ion Battery.

ACS sensors·2026
Same author

A Multiobjective Evolutionary Algorithm Based on Bipopulation With Uniform Sampling for Neural Architecture Search.

IEEE transactions on neural networks and learning systems·2026
Same author

Model-free and finite-time sliding-mode tracking control based on a second-order adaptive disturbance observer.

ISA transactions·2025
Same author

Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift.

IEEE transactions on neural networks and learning systems·2022
Same author

Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems.

IEEE transactions on cybernetics·2021
Same author

Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost.

IEEE transactions on cybernetics·2020
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
Same journal

Output Prediction-Based Event-Triggered Interval Estimation for Continuous-Time Switched Systems.

IEEE transactions on cybernetics·2026
Same journal

Differentially Private Distributed Algorithms for Aggregative Games Over Directed Graphs With Linear Convergence.

IEEE transactions on cybernetics·2026
Same journal

Communication Delay-Based Under-Actuated MASVs Distributed Formation Tracking Control With Unknown Ocean Disturbances and Input Quantization.

IEEE transactions on cybernetics·2026
查看所有相关文章
  1. 首页
  3. 一种基于时间划分的受限多目标优化方法,用于煤矿集成能源系统调度问题.
  1. 首页
  3. 一种基于时间划分的受限多目标优化方法,用于煤矿集成能源系统调度问题.

相关实验视频

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

一种基于时间划分的受限多目标优化方法,用于煤矿集成能源系统调度问题.

Kangjia Qiao, Jing Liang, Dunwei Gong

    IEEE transactions on cybernetics
    |January 6, 2026

    在PubMed 上查看摘要

    概括
    此摘要是机器生成的。

    本研究介绍了一种基于时间划分的新算法,以解决复杂的煤矿综合能源系统调度问题. 新方法提高了对高维能量系统的优化性能.

    更多相关视频

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.1K
    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

    相关实验视频

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.4K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.1K
    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

    科学领域:

    • 工程 工程师 工程师 工程师
    • 优化优化 优化优化
    • 能源系统 能源系统

    背景情况:

    • 煤矿综合能源系统调度问题 (CMIES-DP) 是一个复杂的,高维的,受约束的多目标优化问题 (CMOP).
    • 现有的受约束的多目标进化算法 (CMOEA) 难以处理高维变量,缺乏对CMIES-DP目标和约束的具体分析.

    研究的目的:

    • 提出一个基于时间划分的新型CMOEA (TDCEA),适用于CMIES-DP.
    • 解决当地最佳的挑战,并提高CMIES-DP.的搜索能力.

    主要方法:

    • 根据目标和约束的时间关系,将CMIES-DP分解为更小的子问题.
    • 顺序解决子问题和随机连接决策变量.
    • 组合解决方案集的优化,以找到可行的帕雷托最佳解决方案.
    • 对约束-目标关系的分析,以指导人口演变.

    主要成果:

    • 拟议的TDCEA被应用于一个真实的CMIES-DP案例.
    • 实验结果显示,TDCEA在多样性,融合和分布方面优于先进的算法.
    • 该算法有效地处理了问题的高维和多目标性质.

    结论:

    • 与现有的方法相比,TDCEA为解决CMIES-DP提供了一种优越的方法.
    • 时间划分策略和指导人口演变显著提高了优化性能.
    • 这项研究为复杂的能源系统调度问题提供了有针对性的技术.