A model predictive Goertzel algorithm based active islanding detection for grid integrated photovoltaic systems
A microgrid is a cluster of several energy sources and interconnected loads which are synchronized to operate either in grid connected or islanded mode. To attain higher efficiency and improved power control the microgrid is integrated with power electronic devices. But various problems arise relate...
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Published in: | Microprocessors and microsystems Vol. 95; p. 104706 |
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Main Authors: | , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier B.V
01-11-2022
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Subjects: | |
Online Access: | Get full text |
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Summary: | A microgrid is a cluster of several energy sources and interconnected loads which are synchronized to operate either in grid connected or islanded mode. To attain higher efficiency and improved power control the microgrid is integrated with power electronic devices. But various problems arise related to protection among which the major issue to be focused on is islanding detection. Whenever an islanding condition arises the balance between load and generation in the islanded circuit can be violated, thereby leading to abnormal frequencies and voltages. This hazardous state observed in the system can be prevented by early detection of occurrence. In this paper, an active islanding detection with integrated PV system for grid based on Predictive Goertzel algorithm is proposed. The proposed single-phase single stage photovoltaic system injects a small harmonic component of the output of the grid and checks point of common coupling (PCC). The Predictive Goertzel algorithm is a discrete Fourier transform that resolve Non detection Zone (NDZ) and detection time. The highlight of this work is to demonstrate the performance of proposed islanding detection method, a single-stage single-phase grid integrated PV system embraces of 1-Φ full bridge inverter, LCL filter and local load (RLC parallel load) is considered. This algorithm is implemented in MATLAB Simulink environment. From the results obtained, the detection time for real time environment has reduced by 200ms compared to existing algorithm. |
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ISSN: | 0141-9331 1872-9436 |
DOI: | 10.1016/j.micpro.2022.104706 |