构建新型电力系统背景下的微电网鲁棒简化建模
Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems
王大兴 1宁妍 2汪敬培 3徐洋 4毕峻 5周铭标 6王鹏4
作者信息
- 1. 国网四川省电力公司电力科学研究院,四川成都 610041
- 2. 浙江大有实业有限公司,浙江杭州 310009
- 3. 国网衢州供电公司,浙江衢州 324000
- 4. 电子科技大学机械与电气工程学院,四川成都 611731
- 5. 国网阿坝供电公司,四川阿坝 624000
- 6. 国网三明供电公司,福建三明 365000
- 折叠
摘要
发展高比例可再生能源接入的微电网是构建新型电力系统,实现中国能源安全和低碳发展的重要手段.在分析微电网所接入系统的动态特征时,现有等值模型存在鲁棒性不强的问题,即等值模型虽然可以很好地复现真实系统在训练故障下的动态特征,但却无法准确反映系统在未知故障(非训练故障)下的真实响应.为此,首先采用k-means++对微电网的典型运行方式进行有效区分,以表征系统的随机性和时变性特征;其次,采用基于关键参数筛选的参数辨识方法,避免了参数辨识过程中的多解问题;然后,针对系统不同典型运行方式,利用卷积神经网络对等值模型参数进行泛化;最后,基于Fisher判别准则实现了等值模型参数的在线匹配,并在某实际微电网模型中验证了所提方法的有效性.
Abstract
The development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions.However,amid the analysis of the dynamic characteristics of microgrid-integrated power system,the current equivalent models appear to be not robust enough.Specifically,these models can well reproduce the behaviors of actual system under the faults in training set,they may not be able to reflect actual system responses under other unknown faults(non-training faults).In regard to this,k-means++ is introduced first to effectively distinguish the typical operation condition of microgrid such that the randomness and time-varying characteristics of the system can be represented.Next,key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process.Then,the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions.Additionally,online matching of equivalent model parameters is achieved by virtue of Fisher discriminant analysis.Finally,the effectiveness of the proposed method has been verified in a real microgrid system in China.
关键词
微电网/等值建模/鲁棒性/k-means++聚类/卷积神经网络/Fisher判别准则Key words
microgrid/equivalent modeling/robustness/k-means++ clustering/convolutional neural network/Fisher discriminant analysis引用本文复制引用
基金项目
国家自然科学基金资助项目(52007024)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据