Identification of generator coherency in power systems with wind farm

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks Pub Date : 2024-08-16 DOI:10.1016/j.segan.2024.101502

Jiajun Liu, Lipeng Liu, Ji Sun, Chenjing Li, Haokun Xu

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引用次数: 0

Abstract

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.

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风电场电力系统中发电机一致性的识别

在 "双碳 "目标的背景下，以风电为代表的新能源渗透率逐渐提高。风电的大规模并网使电力系统变得更加复杂和不确定。风电场的输出改变了系统流向，间接影响同步发电机的功率角特性，从而改变发电机之间的一致性。影响同步发电机同源性识别。针对现有的机组同源性识别方法没有考虑风电场对识别结果的不利影响这一问题，本文提出了一种针对含有风电场的电力系统的发电机同源性识别方法。将风电场对同步发电机同源性的影响转化为收缩导纳矩阵，作为静态电气距离指标；选取反映扰动后同步发电机同源性的动态数据，形成四种动态指标，测算各发电机之间的功率角增量曲线：利用组合加权法确定各指标权重，形成综合相似度矩阵，并利用模糊系统聚类和 F 统计值确定最优聚类结果。最后，以 EPRI-9 节点系统、EPRI-36 节点系统和 IEEE68 节点系统为例，验证了所提方法的有效性。保留所有权利。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology

CiteScore

7.90

自引率

13.00%

发文量

206

审稿时长

49 days

期刊介绍： Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.