Islanding detection for multi DG system using inverter based DGs

Faqhruldin, O. N.; El-Saadany, Ehab F.; Zeineldin, H. H.

doi:10.1109/pesmg.2013.6672262

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Several detection techniques analysed and explained in references based on multiple features do not elaborate the process of selecting the set of best features from the list of features extracted. Extraction of a large number of features in real‐time environment increases the computational burden, and hence, the selection of minimum number of most appropriate features for detection becomes an important concern.…”

Section: Introductionmentioning

confidence: 99%

Hybrid islanding detection with optimum feature selection and minimum NDZ

Chandak

Mishra

Rout

2018

Int Trans Electr Energ Syst

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Summary Integration of the distributed generators into a distribution system encounters various system issues, and out of those islanding detection is 1 of the major protection problems to focus on. Many detection schemes have been proposed in the recent past, which possess a nondetection zone (NDZ) and usually neglect to provide a justification for the selected detecting features among all possible measures. Sensitive feature selection and minimization of NDZ are the 2 major objectives of this study. This paper comprises of 2 operational modes of designed IEEE 13‐bus test feeder (offline mode and online mode of operation). The offline mode of system operation focuses on selecting the optimal feature vectors using the proposed “modified multiobjective differential evolution algorithm” coupled with an extreme learning machine classifier. The modified multiobjective differential evolution algorithm‐extreme learning machine is applied to find out 2 optimum feature vectors, one by considering accuracy and minimal features and another one by dependability with a single feature as its objective functions. The online mode concentrates on the proposed new hybrid islanding detection method comprised of both passive and active detection techniques. Passive technique implements a decision tree designed by using the obtained accuracy‐based feature vector. Decision tree triggers the active method on suspecting the runtime instances as non‐islanding events to reduce the NDZ. Active method uses the obtained dependability‐based feature vector as an injecting parameter. The test results indicate the efficiency and accuracy of the proposed approach under different circumstances of power mismatch.

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Section: Introductionmentioning

confidence: 99%

Hybrid islanding detection with optimum feature selection and minimum NDZ

Chandak

Mishra

Rout

2018

Int Trans Electr Energ Syst

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“…In the literature, several detection techniques proposed to date focus on minimising the NDZ, which are deployed and tested for a particular type of DG [16, 17]. However, a challenging task arises while designing a passive anti‐islanding scheme protecting both the type of DGs simultaneously [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal feature selection for islanding detection in distributed generation

et al. 2018

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The integration of the distributed power generation into a distribution system comes with several system problems. One of the teething problems related to system protection is islanding detection. Various anti-islanding techniques based on feature evaluation were proposed in the recent past. However, they overlook the need for justifying the selection of a particular detection feature among all the possible measures. In this study, a wrapper feature selection approach is proposed where a modified multi-objective differential evolution algorithm is coupled with a kernel-based extreme learning machine classifier. To select the optimum features, five standard objective functions have been considered, such as dependability, security, accuracy, F-measure, and the number of features. About 1864 cases have been generated from the designed IEEE 13 bus system to extract the sensitive features. IEEE 1547 standards have been considered while designing and testing the IEEE 13 bus system against islanding. The selected optimal features detect the islanded condition decisively for both synchronous and inverterbased distributed generators. The features also validate their performance under noisy environment accurately with lesser computational time.

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A Novel Islanding Detection Method in a Distributed Generation Using Change in Phase Angle Difference Between Positive Sequence Current and Voltage

Bashir

Jena

2023

Trans Indian Natl. Acad. Eng.

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Islanding detection for multi DG system using inverter based DGs

Cited by 8 publications

References 11 publications

Hybrid islanding detection with optimum feature selection and minimum NDZ

Hybrid islanding detection with optimum feature selection and minimum NDZ

Optimal feature selection for islanding detection in distributed generation

A Novel Islanding Detection Method in a Distributed Generation Using Change in Phase Angle Difference Between Positive Sequence Current and Voltage

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