Comparative study of active anti‐islanding schemes compatible with MICs in the prospect of high penetration levels and weak grid conditions

Valsamas, Fotis; Voglitsis, Dionisis; Rigogiannis, Nick; Papanikolaou, Nick; Kyritsis, Anastasios

doi:10.1049/iet-gtd.2018.5636

Cited by 21 publications

(24 citation statements)

References 31 publications

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“…The standard also discusses the benchmark for individual odd harmonics levels, such as for class A and B, the third harmonic should not exceed 2.3% and 3.45% respectively and the fifth harmonic should not exceed 1.14% and 1.71% respectively, and so on. [48] and [49] discuss antiislanding techniques which demonstrate a detailed power quality analysis in terms of THD, voltage flickering, and grid penetration under weak grid conditions.…”

Section: Performance Indicatorsmentioning

confidence: 99%

Islanding Detection for Inverter-Based Distributed Generation Using Unsupervised Anomaly Detection

et al. 2021

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Islanding detection with the rising grid supporting inverter-based distributed generation is becoming more critical protection due to its high droop gains and overall decreased system inertia leading to rapid changes in the electrical parameters. Traditional methods for islanding detection in this regard are susceptible to significant problems such as non-detection zone, false-positive detection, and inefficient mode of validation. Therefore, to attenuate these problems, this paper proposes a hybrid islanding detection technique based on unsupervised anomaly detection using autoencoders. This technique uses the rate of change of frequency as primary and phase angles of the voltage and current as secondary detection parameters, demonstrating improved performance, reliability, and robustness due to its shared advantage of both active frequency drift and autoencoder. Furthermore, a dialectic model of offline and online validation schemes is also proposed to ensure the reliability of detection. Extensive simulations and validations have been carried out on multiple networks to generate data sets used to train, test, and validate the technique and compute its statistical significance, thereby confirming its effectiveness. The optimal islanding detection time for the base cases was recorded as 20 milliseconds with an F1-score of 0.991, dependability index of 0.998, security index of 0.995, with total harmonic distortion of 4.56% and zero non-detection zones, which complies with IEC 61000-3-2 and IEEE standard 1547's requirement of detection within two seconds after islanding.

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Section: Performance Indicatorsmentioning

confidence: 99%

Islanding Detection for Inverter-Based Distributed Generation Using Unsupervised Anomaly Detection

et al. 2021

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“…Valsamas et al [3] investigated the active anti-IDMs for module integrated converters with high DG penetration levels. Sivadas and Vasudevan [4] presented an active island detection method with more than one inverter operating parallelly in the microgrid environment and it is based on the injection of active power in predefined periodic step-change patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation

Subramanian¹,

Loganathan²

2020

Energies

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Distributed Generation (DG) has changed the power generation system to small-scale instead of large-scale generation. The demanding issue with the interconnection of DG is the detection of unintended islanding in a network. Several methods proposed in the literature show drawbacks such as high non-detection zones (NDZ) and higher tripping time. In this paper, the IEEE 13 bus distribution network with DGs like wind and solar power plants is integrated at two buses. Islanding is detected by utilizing data from a micro-synchrophasor located at the distribution grid and the DG. The micro-synchrophasor-based unintended islanding detection algorithm is based on parameters such as voltage, rate of change of voltage, frequency, rate of change of frequency, voltage phase angle difference and the rate of change of the voltage phase angle difference between the utility and the islanded grid. The proposed islanding detection algorithm discriminates between islanding and non-islanding conditions and is highly efficient under zero power mismatch conditions. The proposed method has null NDZ and satisfies the IEEE 1547 standard for DG tripping time. The effectiveness of the proposed IDM was verified when there are multiple DGs in the islanded grid. Also, the proposed method does not require additional hardware as it can be incorporated in digital relays with synchrophasor functionality.

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“…Local IDSs may be passive, active and hybrid in nature. In [3], authors presented a detailed study of active anti-islanding methods which can effectively be used for module integrated converters (MICs). These techniques are effective with high solar photovoltaic (PV) energy penetration level in the distribution grid operated at low voltages.…”

Section: Introductionmentioning

confidence: 99%

Identification of Islanding Events in Utility Grid With Renewable Energy Penetration Using Current Based Passive Method

Swarnkar

Mahela²,

Khan

et al. 2021

IEEE Access

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Renewable energy (RE) generation levels are increasing in modern power systems at a fast rate due to their advantages of clean and non-exhaustible nature of energy. However, this type of generation creates technical challenges in terms of operation and control due to uncertain and un-predictable nature of generation. Islanding is an operational scenario where there is a loss of grid and RE generators continue to feed power to the local load. This has harmful effects on the RE generators and operating personal. Hence, it is expected that islanding scenario is identified in minimum time and RE generators are disconnected within 2s duration after island formation. This paper designed an islanding identification scheme (IDS) by designing a current islanding detection indicator (CIDI) that combines the features computed by processing the current signals, negative sequence current (NSC) and negative sequence voltage (NSV) using the Stockwell transform (ST) and the Hilbert transform (HT). Information contained by the total harmonic distortions of voltage (T HD v ) and current (T HD i ) is also used while designing the CIDI. Islanding and non-islanding events of category-I & II are identified and discriminated from each other by comparison of peak magnitude of CIDI with the first threshold value (FTV) and second threshold value (STV). This IDS effectively recognizes the islanding events even in the noisy environment with minimum non-detection zone (NDZ) and minimum time. The efficiency is greater than 98% even with the noise of 20dB SNR (signal to noise ratio). The performance of proposed IDS is better compared to IDS using discrete wavelet transform (DWT), Empirical mode decomposition (EMD), Slantlet transform & Rdigelet probabilistic neural network (RPNN), and artificial neural network (ANN). The effectiveness of IDS is validated on IEEE-13 nodes test system using MATLAB software, practical distribution network and in real time scenario by use of real time digital simulator (RTDS). INDEX TERMSHilbert transform; Islanding; Renewable Energy; Stockwell transform; utility grid network.

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Comparative study of active anti‐islanding schemes compatible with MICs in the prospect of high penetration levels and weak grid conditions

Cited by 21 publications

References 31 publications

Islanding Detection for Inverter-Based Distributed Generation Using Unsupervised Anomaly Detection

Islanding Detection for Inverter-Based Distributed Generation Using Unsupervised Anomaly Detection

Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation

Identification of Islanding Events in Utility Grid With Renewable Energy Penetration Using Current Based Passive Method

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