Recognition of Islanding Data for Multiple Distributed Generation Systems with ROCOF Shore Up Analysis

Reddy, Ch. Rami; Reddy, K. Harinadha; Reddy, K. Venkata Siva

doi:10.1007/978-981-13-1921-1_54

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Local detection methods can be categorized into passive, active, hybrid, and intelligent methods, which operate by measuring SG parameters, such as voltage, current, frequency, and harmonic distortion on the microgrid side [5]. Passive methods include under/overvoltage relays (UVR/OVR), under/over-frequency relays (UFR/OFR) [5], rate of change of frequency (ROCOF) [6], methods based on rate of change of active power (ROCOAP) and rate of change of reactive power (ROCORP) [7], a technique based on two criteria of transient index value (TIV) and positive sequence of current angle at common connection point [8], detection methods using signal processing techniques [9], methods based on unbalance voltage (UV) [2], methods based on total harmonic distortion (THD) [10], and methods based on differential transient rate of change of frequency (DTROCOF) [11].…”

Section: Introductionmentioning

confidence: 99%

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Khonakdari

Kamarposhti

2021

Preprint

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Background: Nowadays, in microgrids based on renewable energy resources (RESs), the uncertainties of load and power generation of distributed generation (DGs) resources is inevitable, which if not taken into account, will lead to errors in network analysis. Results: In this paper, a new method based on type-2 fuzzy logic isproposed to detect microgrid islanding; in whichthe power system does not misoperateduring complex operations, can correctly discriminate the microgrid islandingand other network events at the proper time, and prevent the undesirable performance of DGs. This controller detects islanding in the fastest time under different conditions, and the uncertainties in the system will not considerablyaffect the controller's performance. Conclusions: The proposed method is simulated on the sample system and examined in different scenarios. Then, a comparison is made in different conditions and scenarios between the suggested method and some common methods that have been presented so far to determine the ability of the proposed method to detect islanding.

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Section: Introductionmentioning

confidence: 99%

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Khonakdari

Kamarposhti

2021

Preprint

View full text Add to dashboard Cite

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“…The active method detects islanding by injecting a continuous perturbance and observing the changes in it [11]. These methods [12], Sandia frequency shift [13], Slip mode frequency shift [14], Negative sequence current injection [15], Reactive power injection [16] are some of the active methods. The Hybrid method combines the features of both active and passive methods [17].…”

Section: Introductionmentioning

confidence: 99%

Islanding detection of integrated distributed generation with advanced controller

Reddy

Pandian

Dhanasekaran³

et al. 2020

IJEECS

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Grid integration of non conventional energy resources is increasing in day to day life to supply the global energy utilization requirement. The major problem with such integrated Distributed Generation (DG) is islanding. The islanding is originated in the integrated system when a part of the power system is disconnected from the grid and continue to feed the local load. The islanding is not safe to field persons and equipment. As per IEEE 1547 standards, the islanding should be detected within 2 seconds with the equipments associated with it. In this paper, a new islanding detection method is proposed with fuzzy rule based approach with inputs as the change in frequency and power. This method classifies the islanding and non islanding events efficiently compared to other passive methods. The simulations are carried on Matlab/ Simulink 2018b environment.

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Automatic Generation of Abstracts in Scientific Articles Based on Natural Language Processing for Early Education Professionals and Speech Therapists

Quisi-Peralta¹,

Robles-Bykbaev²,

Galán-Mena³

et al. 2020

Advances in Intelligent Systems and Computing

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Recognition of Islanding Data for Multiple Distributed Generation Systems with ROCOF Shore Up Analysis

Cited by 5 publications

References 23 publications

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Islanding detection of integrated distributed generation with advanced controller

Automatic Generation of Abstracts in Scientific Articles Based on Natural Language Processing for Early Education Professionals and Speech Therapists

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Recognition of Islanding Data for Multiple Distributed Generation Systems with ROCOF Shore Up Analysis

Cited by 5 publications

References 23 publications

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO&nbsp;Algorithm

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO&nbsp;Algorithm

Islanding detection of integrated distributed generation with advanced controller

Automatic Generation of Abstracts in Scientific Articles Based on Natural Language Processing for Early Education Professionals and Speech Therapists

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Product

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Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm

Real-time Detection of Microgrid Islanding Considering Sources of Uncertainty Using Type-2 Fuzzy Logic and PSO Algorithm