Intelligent Islanding Detection Method for Grid-connected Photovoltaic Power System Based on Improved Adaboost Algorithm

被引：0

作者：

Jia K. ^{[1
]}

Zhu Z. ^{[1
]}

Yang Z. ^{[1
]}

Fang Y. ^{[1
]}

Bi T. ^{[1
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Changping District, Beijing

来源：

Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Adaboost algorithm; Data mining; Intelligent detection method; Islanding detection;

D O I：

10.13335/j.1000-3673.pst.2018.2228

中图分类号：

学科分类号：

摘要：

Universal islanding detection methods for photovoltaic (PV) power systems require manual threshold setting. That would lead to a certain non-detection zone (NDZ). Moreover, disturbance signals injected with active methods may adversely affect power quality. Aiming at above problems, this paper proposes a passive intelligent islanding detection method for parallel multi-PV system based on improved adaptive boosting (Adaboost) algorithm. Using Adaboost algorithm to generate classification models for islanding detection can theoretically avoid the NDZs of passive methods. The proposed method takes advantage of the electrical connection between characteristic parameters to adjust the classification model and improves detection ability by redistributing the weight of each characteristic parameter. Simulation results show that when adopted to a multi-PV system, the proposed method can effectively distinguish islanding operation in the NDZs of conventional passive islanding detection methods. The method can also achieve accurate detection in the case of short-term power quality fluctuations, line faults and disturbance signal interference injected with active methods. © 2019, Power System Technology Press. All right reserved.

引用

页码：1227 / 1235

页数：8

共 18 条

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