LPL——Sprint Plan

-- 2021-11-21 00:34:56

The Link Your Class

https://bbs.csdn.net/forums/MUEE308FZ

The Link of Requirement of This Assignment

https://bbs.csdn.net/topics/603251839

The Aim of This Assignment

Alpha Sprint

Team's name

FZU—LPL

 STU ID and FZU STU ID

19103476-831901206  Cao Sicai

19103573-831901223  Jie ZhiLing 

19104448-831901201  Yu MengZe

19104171-831901301  Liu GuanRong

19103077-831901218  Chen wenqi

 

 

Previous Work Summary

In our previous trials, we did a lot of functional work, and it wasn't perfect, but we basically got our work done.  Through previous experience, our strength has also been partially improved .

 

Advantages:

  1. Our members work together to perform their own duties,which improved our team work efficiency.
  2. We have a clear goal for our project, and we know how to achieve these features.
  3. In the previous study, we learned the process of making small programs, from which we can gain experience  

 

 

Disadvantages: 

  1. Our technical foundation is not very solid, so some deviations often occur in the practice process .
  2. We had to temporarily change the expected results because some of our preset goals were not met for technical reasons.

 

 

Improvements:

1.Look for information online and arrange for group members to learn related skills. 

2.   Refine the division of labor among team members to further improve the team's work efficiency. 

3. Improve the program, optimize the previous shortcomings.

 

 

 

 

Team division 

front-end designYu mengze

back-end designJie zhiling

RepositoryLiu guanrong

PPT:Cao sicai

Blog:Chen wenqi  

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内容概要:本文围绕电力系统在负荷发生突变情形下的三相状态估计问题,深入研究了基于无迹卡尔曼滤波(UKF)和自适应无迹卡尔曼滤波(AUKF)的非线性状态估计算法。通过Matlab代码实现,构建了适用于动态负荷扰动场景的仿真模型,系统对比了UKF与AUKF在非线性、非高斯环境下的状态估计性能。研究表明,AUKF通过实时自适应调整过程噪声与观测噪声的协方差矩阵,显著提升了滤波器对突变工况的跟踪能力与鲁棒性,有效降低了估计偏差与均方根误差,在负荷突变条件下展现出更优的动态响应特性和估计精度。该方法为现代电力系统在高比例可再生能源接入、负荷波动剧烈等复杂运行环境下的实时状态感知提供了可靠的技术支撑。; 适合人群:具备电力系统分析、现代控制理论及状态估计基础知识,熟悉Matlab/Simulink仿真工具,从事电力系统运行与控制、智能电网、状态估计算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于含高比例新能源、电动汽车等导致负荷剧烈波动的现代主动配电网状态估计;②提升配电管理系统(DMS)在非平稳、突发扰动工况下的态势感知与预警能力;③为故障诊断、系统恢复、优化调度等高级应用提供高精度、高鲁棒性的实时状态输入数据。; 阅读建议:建议读者结合提供的Matlab代码深入理解UKF与AUKF的算法实现流程,重点剖析AUKF中噪声协方差的自适应更新机制与收敛判据设计,并通过设置不同类型的负荷突变场景(如阶跃、脉冲、斜坡变化)进行仿真对比,全面评估两种算法的性能差异与适用边界,进而掌握其在实际工程中的优化配置方法。

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