基于多指标面板数据特征提取的同调机组分群方法

张 智; 陈艳波; 宋新甫; 刘建琴; 李高望; 曾 鉴; 陈 浩

引用本文:	张智,陈艳波,宋新甫,等.基于多指标面板数据特征提取的同调机组分群方法[J].电力系统保护与控制,2020,48(17):27-36.
	ZHANG Zhi,CHEN Yanbo,SONG Xinfu2,et al.Coherent clustering method based on feature extraction for multi-index panel data[J].Power System Protection and Control,2020,48(17):27-36

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基于多指标面板数据特征提取的同调机组分群方法
张智¹，陈艳波¹，宋新甫²，刘建琴³，李高望³，曾鉴⁴，陈浩¹
(1.华北电力大学电气与电子工程学院，新能源电力系统国家重点实验室，北京 102206； 2.国网新疆电力有限公司经济技术研究院，新疆乌鲁木齐 830000；3.国网经济技术研究院有限公司，北京 102200；4.国网四川省电力公司经济技术研究院，四川成都 610000)

摘要:

同调机群识别对电力系统的动态等值、主动解列控制具有重要的意义。针对已有同调机群识别方法存在的指标选取单一(仅依据发电机功角曲线特征)且特征提取不充分的问题，提出一种基于多指标面板数据特征提取的同调机组分群方法。首先，将反映机组同调性的功角、机端电压及转子角速度3个指标的量测数据形成面板数据。其次，基于面板数据的指标维度和时间维度特征，提取以上3个指标在时间序列上的“绝对值”特征、“波动”特征、“偏度”特征、“峰度”特征以及“趋势”特征。接着借鉴层次分析模型，利用AHP-熵权法求取每个指标及其各特征量权重，进而计算机组间的加权距离矩阵，然后利用系统聚类法实现同调机组分群。最后以EPRI-36节点系统和华北电网为例，验证了所提方法的有效性。

关键词: 同调机群面板数据多指标特征提取层次分析系统聚类

DOI：DOI: 10.19783/j.cnki.pspc.191302

分类号:

基金项目:国家电网有限公司总部科技项目资助(SGXJJY 00GHJS1800003)“含特/超高压直流接入的弱送端电网安全品质评价及网架加强方案优化研究”

Coherent clustering method based on feature extraction for multi-index panel data

ZHANG Zhi¹, CHEN Yanbo¹, SONG Xinfu2, LIU Jianqin3, LI Gaowang³, ZENG Jian⁴, CHEN Hao¹

(1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China; 2. Power Economic Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830000, China; 3. State Grid Power Economic Research Institute Co., Ltd., Beijing 102200, China; 4. Power Economic Research Institute, State Grid Sichuan Electric Power Corporation, Chengdu 610000, China)

Abstract:

Coherent cluster identification plays an important role in the dynamic equivalent and active splitting control of power systems. At present, coherent clustering mainly depends on the generator's power angle curve features. There are problems with single selection indicators and insufficient feature extraction. For these problems, this paper proposes a Coherent cluster identification plays an important role in the dynamic equivalent and active splitting control of power systems. At present, coherent clustering mainly depends on the generator's power angle curve features. There are problems with single selection indicators and insufficient feature extraction. For these problems, this paper proposes a coherent clustering method based on multi-index panel data feature extraction. First, it selects three indicators that can reflect the coherence of power angle increment, terminal voltage and rotor angular velocity of the generator to form panel data. Secondly, based on the characteristics of the panel data with both the cross-sectional and the time-series dimensions, it extracts the "absolute value", "fluctuation", "skew degree", "kurtosis" and “trend” features of the three indices in the time series. Then it builds a hierarchical analysis cluster model. It uses the AHP-entropy weight method to calculate the weight of each index and each feature, and calculates the weighted distance matrix between generator groups, to achieve the final coherent group by system clustering. Finally, the EPRI-36 node system and North China Power Grid are taken as examples to verify the effectiveness of the proposed method. This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. SGXJJY00GHJS1800003): “Study on Safety Quality Evaluation and Grid Strengthening Scheme Optimization of Weak Delivery Network with UHV/EHVDC”.

Key words: coherency identification panel data multiple indicator feature extraction analytic hierarchy system clustering

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