A novel Multi-LSTM based deep learning method for islanding detection in the microgrid

Özcanlı, Asiye Kaymaz; Baysal, Mustafa

doi:10.1016/j.epsr.2021.107574

Cited by 34 publications

(7 citation statements)

References 40 publications

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“…Özcanlı and Baysal [27] introduced a unique passive Islanding Method (IDM) for inverter-interfaced microgrids (MGs). Microgrids, vital for future energy generation, integrate renewable sources like photovoltaic, wind, and hydropower in islanding or grid-connected modes.…”

Section: Current Research Workmentioning

confidence: 99%

Multiple distributed generators islanding detection using GBDT-JS techniques

Goriparthy,

Balakrishnan

2024

STET

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Photovoltaic arrays, Wind Distributed generators (DG) are progressively utilized as a renewable energy source (RES) in contemporary society. Whenever a DG is detached from the grid, it often results in islanding. In accordance with the standards of the Institute for Electrical and Electronics Engineers, islanding shall be identified within a period of two seconds. This work presents a reliable algorithm-based islanding detection approach to distributed generating systems that utilizes both solar power(SPV) and wind systems.. Gradient Boosting Decision Trees and Jelly Fish Techniques are paired together to create the intelligent algorithm known as GBDT-JS algorithm. The proposed technique focuses on the rate of change in phase angle (RCPP) at the target DG position initially. Islanding and grid disturbances can be assessed by employing discrete wavelet transformations (DWTs).Furthermore, the GBDT-JS method is utilized for the categorization of islands and grid disturbances to determine if the particular system can be adopted or not in terms of various loading and switching capabilities. MATLAB/Simulink platform and Python programming are used to validate the recommended approach

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Section: Current Research Workmentioning

confidence: 99%

Multiple distributed generators islanding detection using GBDT-JS techniques

Goriparthy,

Balakrishnan

2024

STET

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“…Then, a grey wolfoptimized artificial neural network trained through energy and standard deviation was deployed for islanding detection in an inverter-dominated distribution system. In [29,30], the authors used long-short term memory networks and pattern recognition methods for islanding detection. A Fourier transform and machine learning-based passive islanding detection schemes were proposed in [31].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The existing approaches confronted the islanding detection problem in modern grid-tied networks in a comprehensive manner. However, the existing research has some limitations: (i) Noisy measurements are not considered in some of the existing islanding detection schemes [15,36]; (ii) some of the islanding detection strategies have a very high computational burden [22][23][24][25][26][27][28][29][30][31][32][33]; (iii) threshold setting in some schemes is difficult to implement [15,21]; and (iv) NDZ is large in few passive schemes, especially during low power miss-match moreover, some islanding detection indexes get failed [17,20].…”

Section: Limitations Of Existing Schemesmentioning

confidence: 99%

1D recursive median filter based passive islanding detection strategy for grid‐connected distributed generations network

Mumtaz

Imran

Rehman

et al. 2023

IET Renewable Power Gen

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In modern distribution networks, integration of distributed generations (DGs) at consumer premises is common. However, islanding detection in such a grid‐tied distributed generation (GTDG) network is the topmost challenge to ensure power quality and reliable operation. This paper presents a modified passive islanding detection scheme (MPIDS) for the GTDG network, using a One‐D recursive Median filter (ODRMF) algorithm. Firstly, at the point of common coupling (PCC), 3‐phase voltage is acquired. Next, the ODRMF is employed to extract 3rd harmonic and residuals from the acquired voltage signal. Afterward, two novel islanding detection indices developed are: (1) First change detection (FCD), which is calculated from the residuals, and (2) delta selected harmonic distortion (∆SHD), which is computed from 3rd harmonic content of the voltage signal. Finally, variations in the two indices are compared with the threshold to identify islanding. To validate the efficacy of the presented MPIDS, rigorous simulations in MATLAB/ Simulink are conducted on the UL‐1741 and IEEE 13‐bus GTDGs systems under different conditions. The results illustrate that the presented strategy is highly reliable in all assessed conditions and can detect the islanding as well as non‐islanding incidents effectively under balanced and unbalanced load/generation conditions with a very low non‐detection zone.

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“…Non-detection zone (NDZ) is the most widely-used graphical tool to evaluate the performance of the islanding detection relays [15]- [17]. This region presents the amount of active and reactive power mismatches where the IDS fails to detect the islanding condition within the permissible time [18]- [19]. In addition, NDZ should be extracted to adjust the relay thresholds, especially for conventional techniques [15]- [20].…”

Section: A Non-detection Zonementioning

confidence: 99%

A Comprehensive Survey on Islanding Detection Methods of Synchronous Generator-Based Microgrids: Issues, Solutions and Future Works

Bekhradian

Sanaye‐Pasand

2022

IEEE Access

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Nowadays, the penetration of distributed generations (DGs) and microgrids (MGs) has been significantly increased due to the technical, social, environmental, and financial reasons. One of the most common types of DGs which is widely used in different networks is small-scale synchronous generators (SSSGs). Synchronous-based microgrids (SGBMGs) and SSSGs can operate under both grid-connected and islanded conditions. To guarantee a stable, uninterruptible, and safe autonomous operation, the unintentional islanding events should be quickly detected and then, appropriate control and precaution actions should be performed. Various islanding detection techniques have been proposed in the two past decades including remote and local methods. Meanwhile, local methods can be divided into two major groups, i.e. passive and active methods. To select the most suitable islanding detection scheme (IDS), various factors including the DG type and its technology should be considered. Therefore, the IDS for SGBMGs may be completely different from that for inverter-based microgrids. In this paper, the suitable IDS which can be practically applied to the SGBMGs are comprehensively investigated. To do so, at first, various suitable indices are introduced which can be properly employed by the system engineers and researchers to compare various IDS and select the most appropriate one. Then, various types of existing IDS are carefully investigated using these indices. Based on these investigations, it is concluded that local passive methods are more appropriate for SGBMGs due to their technical and economic benefits. Moreover, to enhance the existing IDS, some ideas are provided in this paper which can be considered in the future research works.INDEX TERMS Small-scale synchronous generator (SSSG), Synchronous generator-based microgrid (SGBMG), Islanding, Islanding detection.

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A novel Multi-LSTM based deep learning method for islanding detection in the microgrid

Cited by 34 publications

References 40 publications

Multiple distributed generators islanding detection using GBDT-JS techniques

Multiple distributed generators islanding detection using GBDT-JS techniques

1D recursive median filter based passive islanding detection strategy for grid‐connected distributed generations network

A Comprehensive Survey on Islanding Detection Methods of Synchronous Generator-Based Microgrids: Issues, Solutions and Future Works

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