A detection algorithm for islanding-prevention of dispersed consumer-owned storage and generating units

Pai, Fu‐Sheng; Huang, Shyh-Jier

doi:10.1109/60.969474

Cited by 128 publications

(14 citation statements)

References 13 publications

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“…Furthermore, test results have shown that for a small power mismatch between the DG and local loads, rate of change of frequency over power criterion is more sensitive than ROCOF over time criterion [13]. Furthermore, test results have shown that for a small power mismatch between the DG and local loads, rate of change of frequency over power criterion is more sensitive than ROCOF over time criterion [13].…”

Section: (B) Rate Of Change Of Frequency (Rocof) Relaymentioning

confidence: 99%

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Islanding detection approach with negligible non-detection zone based on feature extraction discrete wavelet transform and artificial neural network

Hashemi

Mohammadi

2016

Int. Trans. Electr. Energ. Syst.

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Summary The paper presents a novel detection method based on feature extraction discrete wavelet transform (DWT) combined with artificial neural network (ANN) for identification of islanding condition in distributed generation (DG) system. Islanding detection methods can be classified into two major categories as active and passive methods. The main disadvantages of the passive methods are determined threshold value and related to their large non‐detection zone. The emphasis of the proposed approach is eliminating the aforementioned drawbacks. The DWT allows revealing various hidden features of the signal. The aim of this paper is to determine the best wavelet basis function in order to identify islanding occurrence with higher accuracy and lower decomposition level. The proposed approach requires the measure rate of change of frequency at the DG's terminal, and various features are extracted from DWT then these features used as input into ANN. Also, the performance of the different structures of ANN such as feed‐forward neural network, radial basis function, and probabilistic neural network are compared for islanding detection purpose. The proposed method is simulated and tested in various operation conditions such as islanding conditions, motor starting, capacitor bank switching, and nonlinear load switching. The test results showed that the proposed method correctly detects the islanding operation and does not mal‐operate in the other situations. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: (B) Rate Of Change Of Frequency (Rocof) Relaymentioning

confidence: 99%

“…This method is proposed for increasing detection accuracy and reduction of NDZ. Furthermore, test results have shown that for a small power mismatch between the DG and local loads, rate of change of frequency over power criterion is more sensitive than ROCOF over time criterion [13].…”

Section: (D) Rate Of Change Of Frequency Over Powermentioning

confidence: 99%

Islanding detection approach with negligible non-detection zone based on feature extraction discrete wavelet transform and artificial neural network

Hashemi

Mohammadi

2016

Int. Trans. Electr. Energ. Syst.

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“…However, if the loading of the DG does not change significantly after an islanding situation takes place, then it cannot be detected by monitoring fundamental parameters such as voltage change or frequency deviation so utilizing other parameters such as voltage unbalance or total harmonic distortion (THD) has been proposed [85]. In [86] another method which utilizes the rate of change of frequency over power has been introduced.…”

Section: Islanding Detectionmentioning

confidence: 99%

Smart Inverters for Microgrid Applications: A Review

et al. 2019

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In a microgrid, with several distributed generators (DGs), energy storage units and loads, one of the most important considerations is the control of power converters. These converters implement interfaces between the DGs and the microgrid bus. In order to achieve higher functionality, efficiency and reliability, in addition to improving the control algorithms it is beneficial to equip the inverters with “smart” features. One interpretation of “smartness” refers to minimizing the requirement of communication and therefore switching from centralized to decentralized control. At the same time, being equipped with efficient and state of the art communication protocols also indicates “smartness” since the requirement of communication cannot be completely omitted. A “smart inverter” should offer some features such as plug and play, self-awareness, adaptability, autonomy and cooperativeness. These features are introduced and comprehensively explained in this article. One contribution discussed here is the possibility of achieving long-range wireless communication between inverters to empower various control schemes. Although current efforts aim to modify and improve power converters in a way that they can operate communication free, if a suitable and functional communication protocol is available, it will improve the accuracy, speed and robustness of them.

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“…Passive techniques are very cost effective, as they do not require large modifications in the protection system [51][52][53][54][55][56]. Few common passive technique include the rate of change of frequency [63][64][65], ratio of frequency variation to load variation [66,67], rate of change of output power [60], rate of change of frequency over power [66], voltage unbalance [68,69], change of source impedance [70], harmonic distortion [68,71], voltage magnitude variation [72] and impedance based method to provide interlocking ROCOF relay [73]. Fig.…”

Section: Passive Islanding Detection Techniquesmentioning

confidence: 99%

Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

Laghari

Mokhlis

Karimi

et al. 2014

Energy Conversion and Management

100

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A detection algorithm for islanding-prevention of dispersed consumer-owned storage and generating units

Cited by 128 publications

References 13 publications

Islanding detection approach with negligible non-detection zone based on feature extraction discrete wavelet transform and artificial neural network

Islanding detection approach with negligible non-detection zone based on feature extraction discrete wavelet transform and artificial neural network

Smart Inverters for Microgrid Applications: A Review

Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

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