电源学报

基于数据驱动子空间的光伏并网发电系统孤岛检测方法

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陈恩怜 ¹ , 陈建民 ² , 张依恋 ¹ , 范勤勤 ³

电源学报 | 新能源发电与储能 2026,24(4): 98-109

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电源学报 |新能源发电与储能 2026 , 24 (4) : 98 -109

基于数据驱动子空间的光伏并网发电系统孤岛检测方法

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陈恩怜¹, 陈建民², 张依恋¹, 范勤勤³

作者信息

^1.上海海事大学航运技术与控制工程交通运输行业重点实验室，上海　201306

^2.中国舰船研究设计中心，武汉　430060

^3.上海海事大学物流研究中心，上海　201306

通讯作者:

张依恋（1987—　），女，通信作者，博士，讲师。研究方向：配电网风险评估与主动防御技术。E-mail：zhangyl@shmtu.edu.cn。

作者简介:

陈恩怜（1999—　），女，硕士研究生。研究方向：光伏并网系统孤岛检测。E-mail：202130510049@stu.shmtu.edu.cn。

陈建民（1986—　），男，博士，高级工程师。研究方向：船舶电力系统及能量管理。E-mail：cjm100@126.com。

范勤勤（1986—　），男，博士，副教授。研究方向：智能电网系统优化算法。E-mail：qqfan@shmtu.edu.cn。

Data-driven Subspace-based Islanding Detection Method for Grid-connected Photovoltaic Power Generation System

Enlian CHEN¹, Jianmin CHEN², Yilian ZHANG¹, Qinqin FAN³

Affiliations

^1.Key Laboratory of Transport Industry of Marine Technology and Control Engineering, Shanghai Maritime University, Shanghai 201306, China

^2.China Ship Development and Design Center, Wuhan 430060, China

^3.Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China

出版时间: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.98

文章导航

摘要

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近年来，光伏并网发电技术迅速发展，随之也带来了孤岛效应等威胁。实现对孤岛效应的实时监测对于保障光伏发电系统运行的稳定性具有重要意义。因此，针对光伏并网发电系统孤岛故障，提出了1种基于数据驱动子空间的孤岛检测方案。首先，以公共耦合节点处电压基波与电压的2次谐波信号作为检测孤岛的依据，设计基于子空间辨识方法的电压基波输出预测器；然后设计孤岛检测自适应滤波器实时跟踪电压基波信号并放大电压基波信号生成残差信号，进而根据残差信号和电压2次谐波信号设计孤岛故障检测器，判断系统是否遭受孤岛故障；最后通过仿真实验验证所提方法的有效性。

关键词

孤岛检测 / 数据驱动 / 子空间辨识 / 光伏并网发电系统

Abstract

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The grid-connected photovoltaic power generation technology has developed quickly in recent years, but it also poses risks such as the islanding effect. The realization of real-time monitoring of islanding effect is of significance for ensuring the stable operation of photovoltaic power generation systems. Therefore, aimed at the islanding fault of grid-connected photovoltaic power generation systems, a data-driven subspace-based islanding detection scheme is proposed. First, the fundamental wave from the voltage signal and the secondary harmonic signal of voltage at the point of common coupling are taken as the basis for islanding detection, and a voltage fundamental wave output predictor based on the subspace identification approach is designed. An adaptive filter for islanding detection is designed to track the fundamental wave from the voltage signal in real time, and the fundamental wave from the voltage signal is amplified to generate a residual signal. On this basis, an islanding fault detector is designed according to the residual signal and the secondary harmonic signal of voltage, so as to judge whether the system suffers the islanding fault. Finally, the effectiveness of the proposed approach was verified by simulation results.

Key words

Islanding detection / data-driven / subspace identification / grid-connected photovoltaic power generation system

引用本文

陈恩怜, 陈建民, 张依恋, 范勤勤. 基于数据驱动子空间的光伏并网发电系统孤岛检测方法. 电源学报, 2026 , 24 (4) : 98 -109 . DOI: 10.13234/j.issn.2095-2805.2026.4.98

Enlian CHEN, Jianmin CHEN, Yilian ZHANG, Qinqin FAN. Data-driven Subspace-based Islanding Detection Method for Grid-connected Photovoltaic Power Generation System[J]. Journal of Power Supply, 2026 , 24 (4) : 98 -109 . DOI: 10.13234/j.issn.2095-2805.2026.4.98

基金

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国家自然科学基金资助项目(62176150)
上海市高校能力建设资助项目(20040501400)
上海市浦江人才计划资助项目(22PJD030)

参考文献

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2026年第24卷第4期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.4.98

接收时间：2023-10-13
首发时间：2026-10-29
出版时间：2026-04-30

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出版历史

收稿日期：2023-10-13
修回日期：2024-03-03
录用日期：2024-03-09

基金

National Natural Science Foundation of China(62176150)

国家自然科学基金资助项目(62176150)

Shanghai University Capacity Building Project(20040501400)

上海市高校能力建设资助项目(20040501400)

Shanghai Pujiang Talent Program(22PJD030)

上海市浦江人才计划资助项目(22PJD030)

作者信息

^1.上海海事大学航运技术与控制工程交通运输行业重点实验室，上海　201306

^2.中国舰船研究设计中心，武汉　430060

^3.上海海事大学物流研究中心，上海　201306

通讯作者:

张依恋（1987—　），女，通信作者，博士，讲师。研究方向：配电网风险评估与主动防御技术。E-mail：zhangyl@shmtu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.4.98

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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