Detection and diagnosis of islanding using artificial intelligence in distributed generation systems

Mohapatra, Sushree Shataroopa; Maharana, Manoj Kumar; Pradhan, A. V.; Pati, Sudhansu Bhusan

doi:10.1016/j.segan.2021.100576

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Figure 5 depicts the process used in computational intelligent islanding detection. There are several different computational intelligence-based methodologies, including support vector machine, fuzzy logic, decision trees, and artificial neural networks [23]- [25].…”

Section: Computational Intelligent Based Methodsmentioning

confidence: 99%

Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids

Abu Sarhan,

Bien,

Barczentewicz

2024

IJECE

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One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on several factors, including implementation costs, non-detected zones, declining power quality, and response times using the analytical hierarchy process (AHP). The multi-criteria decision-making analysis shows that the overall weight of passive methods (24.7%), active methods (7.8%), hybrid methods (5.6%), remote methods (14.5%), signal processing-based methods (26.6%), and computational intelligent-based methods (20.8%) based on the comparison of all criteria together. Thus, it can be seen from the total weight that hybrid approaches are the least suitable to be chosen, while signal processing-based methods are the most appropriate islanding detection method to be selected and implemented in power system with respect to the aforementioned factors. Using Expert Choice software, the proposed hierarchy model is studied and examined.

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Section: Computational Intelligent Based Methodsmentioning

confidence: 99%

Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids

Abu Sarhan,

Bien,

Barczentewicz

2024

IJECE

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“…A Fourier transform and machine learning-based passive islanding detection schemes were proposed in [31]. Various intelligent islanding detection strategies were developed by using different intelligent classifiers [32,33]. In [34], the Kalman filter extracted harmonic features from the voltage recorded at PCC.…”

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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“…Software Papers PSCAD-EMTDC [64,74,76,79,80] DIgSILENT [49][50][51]81] ATP [52] PSIM [53] MATLAB [54,59,78,[82][83][84][85][86][87][88][89][90][91] No name [44,65,69,72,73,92]…”

Section: Papersmentioning

confidence: 99%

“…Depending on which type of ANN is used for islanding detection, it has a corresponding learning algorithm. Boltzmann learning algorithm [81,85] Bayesian function [41,55,64,77,96] Differential evolution [41,51,78,84] Fuzzy logic [59,62,72,73,75,78,92,97] Vector quantization [44,45] Machine learning [50,63,67,88,98] Optimized learning algorithm [65,76,82,83,95]…”

Section: Types Of Learning Algorithmsmentioning

confidence: 99%

Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

Kaluđer,

Fekete,

Čvek

et al. 2023

Applied Sciences

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Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.

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Detection and diagnosis of islanding using artificial intelligence in distributed generation systems

Cited by 6 publications

References 14 publications

Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids

Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids

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

Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

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