Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm

Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm

Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding c...

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Main Authors: Bin Li, Jingpeng Wang, Hailong Bao, Huiying Zhang
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/186360
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record_format Article
spelling doaj-d47ca0994d1d4e7c88fc766fd5f79a142020-11-24T22:39:50ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/186360186360Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter AlgorithmBin Li0Jingpeng Wang1Hailong Bao2Huiying Zhang3Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaIslanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.http://dx.doi.org/10.1155/2014/186360
collection DOAJ
language English
format Article
sources DOAJ
author Bin Li
Jingpeng Wang
Hailong Bao
Huiying Zhang
spellingShingle Bin Li
Jingpeng Wang
Hailong Bao
Huiying Zhang
Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
Journal of Applied Mathematics
author_facet Bin Li
Jingpeng Wang
Hailong Bao
Huiying Zhang
author_sort Bin Li
title Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
title_short Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
title_full Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
title_fullStr Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
title_full_unstemmed Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
title_sort islanding detection for microgrid based on frequency tracking using extended kalman filter algorithm
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.
url http://dx.doi.org/10.1155/2014/186360
work_keys_str_mv AT binli islandingdetectionformicrogridbasedonfrequencytrackingusingextendedkalmanfilteralgorithm
AT jingpengwang islandingdetectionformicrogridbasedonfrequencytrackingusingextendedkalmanfilteralgorithm
AT hailongbao islandingdetectionformicrogridbasedonfrequencytrackingusingextendedkalmanfilteralgorithm
AT huiyingzhang islandingdetectionformicrogridbasedonfrequencytrackingusingextendedkalmanfilteralgorithm
_version_ 1725707331866460160

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