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1.中国科学院电工研究所,北京市 100190
2.中国科学院大学电子电气与通信工程学院,北京市 100049
3.国网北京市电力公司,北京市 100031
Received:16 August 2023,
Revised:2024-01-30,
Published Online:09 May 2024,
Published:25 June 2024
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王冠淇,裴玮,李洪涛等.基于FPGA的两阶段配电网拓扑实时辨识算法[J].电力系统自动化,2024,48(12):100-108.
WANG Guanqi,PEI Wei,LI Hongtao,et al.Real-time Two-stage Topology Identification Algorithm for Distribution Network Based on Field Programmable Gate Array[J].Automation of Electric Power Systems,2024,48(12):100-108.
王冠淇,裴玮,李洪涛等.基于FPGA的两阶段配电网拓扑实时辨识算法[J].电力系统自动化,2024,48(12):100-108. DOI: 10.7500/AEPS20230816002.
WANG Guanqi,PEI Wei,LI Hongtao,et al.Real-time Two-stage Topology Identification Algorithm for Distribution Network Based on Field Programmable Gate Array[J].Automation of Electric Power Systems,2024,48(12):100-108. DOI: 10.7500/AEPS20230816002.
对配电网拓扑进行准确的实时辨识是电力系统安全稳定运行的基础,但随着新能源的接入以及配电网规模不断增大,配电网拓扑结构的动态变化愈加频繁且难以辨识。然而,现有配电网拓扑辨识算法所使用的历史数据需要人工对其进行拓扑标注,且拓扑辨识时间长,难以实现配电网拓扑实时辨识。因此,文中提出了一种基于现场可编程逻辑门阵列(FPAG)的两阶段配电网拓扑结构实时辨识算法。该算法不需要预先给出配电网拓扑类别的数量,即可对已有历史数据进行相应的拓扑标注及分类,并且基于FPGA实现了对配电网拓扑的实时辨别。该算法分为2个阶段:第1阶段采用变分贝叶斯高斯混合模型,对已有历史数据进行相应的拓扑标注及分类;第2阶段采用麻雀搜索算法,使得支持向量机快速收敛得到最优参数,以实现对配电网拓扑结构的精准辨识。基于该算法,利用FPGA并行架构以及高速高密度特性建立了实时拓扑结构辨识平台。最后,通过算例分析验证了所提辨识方法的有效性和优越性。
Accurate real-time topology identification of distribution networks is the basis for safe and stable operation of power systems
but with the access of renewable energy and the increasing scale of distribution network
the dynamic changes of the distribution network topology are more frequent and difficult to identify. However
the historical data used by the existing topology identification algorithms need to be labeled manually
and the topology identification time is long. So it is difficult to achieve real-time topology identification for distribution networks. Therefore
a real-time two-stage topology identification algorithm for distribution networks based on field programmable logic gate array (FPAG) is proposed. The proposed algorithm does not need to know the number of distribution network topology categories in advance
and can label and classify the existing historical data
and realize the real-time topology identification for distribution networks based on FPGA. The algorithm is divided into two stages. The first stage uses the variational Bayesian Gaussian mixture model to label and classify the existing historical data. In the second stage
the sparrow search algorithm is used to make the support vector machine converge quickly to get the optimal parameters
so as to realize the accurate topology identification for distribution networks. Based on this algorithm
a real-time topology identification platform is established by using FPGA parallel architecture and high speed and high density characteristics. Finally
the effectiveness and superiority of the proposed identification method are verified by case analysis.
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