Experimental Performance of an Islanding Detection Scheme Based on Modal Components

Makwana, Yogesh M.; Bhalja, Bhavesh R.

doi:10.1109/tsg.2017.2757599

Cited by 62 publications

(28 citation statements)

References 30 publications

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“…An importance value of the ordered module preference sets is defined as {1/2, 1/4, 1/8, …, 1/2 x }. Thereafter, the importance of the module x by users u is finally obtained as in (15), where ρ x i is the module x importance value for the ith user subjected to the constraints ∑ I x = 1 and 0 < I x < 1…”

Section: Importance Estimation Of the Modulesmentioning

confidence: 99%

μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

et al. 2020

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With the increased climatic change and modern grid complexity, extreme grid power outage events caused by natural calamity and human interruptions have led to an urgency to enhance the grid resiliency. Microgrids (MGs) have proved to be a concrete solution to these situations. However, these events are quite uncertain, leading to the unintentional island of MGs that has adverse effects. Thus, as a first step toward increasing grid resiliency with MG, informing the distributed generations about the unintentional island is a critical task. Hence, there is a need to develop a quick and reliable unintentional island detection scheme. Micro phasor measurement units (μPMUs) are becoming popular in MG. Given this, this study proposes an inadvertent island detection scheme in an MG using an intelligent μPMU. With the μPMU, the voltage at solar generator bus is measured, three features are extracted through spectral kurtosis and random forest classifier is employed for island detection. After island detection, a control methodology is proposed to circumvent the post-effects. The method has zero non-detection zone, 99.83% accuracy and a detection time of 20 ms. The reliability of the algorithm is ascertained using the analytical hierarchical approach and software fault-tree analysis.

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Section: Importance Estimation Of the Modulesmentioning

confidence: 99%

μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

et al. 2020

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“…1, pp. [22][23][24][25][26][27][28][29][30][31] This is an open access article published by the IET and Tianjin University under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) presented in [20]. The scheme is successfully tested for islanding detection under perfect power balance condition; however, it is not verified under the noisy scenario.…”

Section: Introductionmentioning

confidence: 99%

Islanding detection using total variation‐based signal decomposition technique

Mishra

Bhende

Manikandan

2019

IET Energy Systems Integration

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This study presents a passive islanding detection technique based on the approach of total variation filtering (TVF) for inverter-based distributed generation under noisy environment. Contamination of noise in the signal makes the islanding detection threshold less reliable. Therefore, the modal voltage signal is analysed through TVF-based decomposition procedure and several statistical features are obtained from its output. For decision making, the features are compared with the prespecified threshold. To tackle the challenge of islanding detection in the presence of the high-quality factor load, phase angle of the positive sequence voltage is considered as the second level of detection criterion. The proposed scheme is validated on IEEE 34-bus standard distribution system. The scheme is tested under islanding as well as all possible non-islanding network disturbances. Hardware-in-loop results are also presented in support of the theory. A comparative analysis of the proposed algorithm with the well-established S-transform is presented. The proposed scheme is found to be working effectively and can detect islanding condition within three cycles of its inception.

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“…The most important issue of the DG systems connected to the power grid is the islanding phenomenon [3,4]. Islanding occurs when DG systems and loads are disconnected from the power grid but continue to dispatch the generated power energy to the loads [5,6]. In other words, when the load power and the generated power of the DG systems are balanced, i.e., the load power is fully supplied by DG systems, the islanding phenomenon occurs.…”

Section: Introductionmentioning

confidence: 99%

“…However, the serious drawback of the passive methods is that when the generated power of the inverter-based DG systems and the load power are balanced, a large non-detection zone (NDZ) will make the passive method fail [12,13]. On the other hand, the active methods involve injecting a perturbation signal into the inverter-based DG systems via suitable control algorithms [3,4,6]. Although the perturbation signals are injected into the inverter-based DG system which operates in grid-connected mode, the active islanding detection methods have no significant influence on the system [6].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the active methods involve injecting a perturbation signal into the inverter-based DG systems via suitable control algorithms [3,4,6]. Although the perturbation signals are injected into the inverter-based DG system which operates in grid-connected mode, the active islanding detection methods have no significant influence on the system [6]. Moreover, when the power grid breaks down, the injected perturbation signals will cause the voltage and the frequency to deviate.…”

Section: Introductionmentioning

confidence: 99%

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DG System Using PFNN Controllers for Improving Islanding Detection and Power Control

Tan

Lan

2019

Energies

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In this study, an intelligent controlled distributed generator (DG) system is proposed for tracking control and islanding detection. First, a DC/AC inverter with DC power supply is adopted to emulate a DG system and control the active and reactive power outputs. Moreover, in order to comply with the standard for interconnection with the power grid, a novel active islanding detection method is proposed for the inverter-based DG system. In the proposed active islanding detection method, a perturbation signal is designed to inject into the d-axis current of the DG system which causes the frequency at the terminal of the RLC load to deviate when the power grid breaks down. The feasibility of the proposed active islanding detection method is verified according to the UL 1741 test configuration. Furthermore, in order to improve the tracking control of the active and reactive powers of the inverter-based DG system, and to effectively reduce the detection time of islanding phenomenon, two probabilistic fuzzy neural network (PFNN) controllers are adopted to take the place of the conventional proportional-integral (PI) controllers. In addition, the network structure and the online learning algorithm of the adopted PFNN are presented in details. Finally, some experimental results of the proposed active islanding detection method using PFNN controllers are proposed to validate the effectiveness and feasibility of the tracking control and islanding detection.

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Experimental Performance of an Islanding Detection Scheme Based on Modal Components

Cited by 62 publications

References 30 publications

μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

Islanding detection using total variation‐based signal decomposition technique

DG System Using PFNN Controllers for Improving Islanding Detection and Power Control

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