A passive islanding detection technique for distributed generations

Anudeep, Bhatraj; Nayak, Paresh Kumar

doi:10.1109/icpes.2017.8387386

Cited by 15 publications

(4 citation statements)

References 20 publications

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“…In NDZ, the variation in these parameters is small to detect the islanding due to small APM and RPM. All the existing passive methods [2][3][4][5][6][7][8] considered only one of these parameters as an identification metric but none of the methods used a combined effect of these parameters. A better metric can be obtained by considering the combination of these parameter variations.…”

Section: Literature Reviewmentioning

confidence: 99%

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A novel CSoDoA based passive islanding detection technique for multi‐DER AC microgrid control

2023

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Micro‐grid islanding methods are gaining importance as the presence of renewable energy resources is becoming increasingly popular in power distribution. Among all the islanding methods, passive methods are easy to implement and applicable to all types of DERs provided the NDZ is minimized. This article presents a novel passive islanding detection method based on the CSoDoA of ellipse formed by the successive phase voltages to address the NDZ problem. The peak magnitude and phase angle difference of voltages are the parameters to be estimated to compute the area of the ellipse. State‐of‐the‐art passive methods consider the effect of either magnitude or phase angle or frequency as an islanding detection metric. The CSoDoA metric combines the effect of both magnitude and phase angle thereby reducing the NDZ. The proposed method is validated on both synchronous and inverter‐interfaced DERs. The test systems considered are both single‐DER based MG and modified IEEE 13 bus multi‐DER MG. The CSoDoA algorithm is implemented on a Raspberry‐Pi controller and validated using HIL workbench with real‐time simulator OP‐4510. Various islanding and non‐islanding events are simulated, tested and it is evident from the results that it has a very small NDZ.

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Section: Literature Reviewmentioning

confidence: 99%

“…Over/under voltage relay, over/under frequency relay, ROCOF 2 and ROCOV 3 are a few examples of passive IDTs. The main drawback of passive methods [4][5][6][7][8] is that they fail to detect islanding in NDZ. NDZ is a condition where the power generated by all the DERs is equal/close to the total load demand.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel CSoDoA based passive islanding detection technique for multi‐DER AC microgrid control

2023

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“…However, there are various challenges associated with the DG source among which islanding detection is one of the most important protection issue and it should be isolated as quickly as possible. According to IEEE standard 1547, micro grid should be disconnected from main grid within 2 seconds to reduce damage occurring due to failure in the grid [4,5].…”

Section: Introductionmentioning

confidence: 99%

Islanding Detection o f Distributed Generat ion In The Presence Of Fault Events With C ritical Load And Non Critical Load

Waghare*,

Bhasme

2019

IJEAT

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Incredible growth in demand for electricity with reliable, eco-friendly and efficient power has motivated the use of Distributed Generation (DG) all over the world. The problems associated with distributed generation system is unintentional islanding which causes power quality issues in the grid. During islanding operation, renewable energy sources such as solar, wind are used as distributed generation sources, supplies power to the local area. Islanding detection can be divided into remote and local methods; the later method is again classified into passive and active methods. In this paper, Passive technique is used for detecting the islanding condition which is carried out by controlling parameters like voltage and frequency. This is modeled and simulated in MATLAB/Simulink software.

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“…Islanding detection techniques are classified into three types (i) passive islanding detection (ii) active islanding detection and (iii) remote islanding detection. In the passive islanding technique, deviation of MG parameters like voltage, frequency, and phase angle are used to detect islanding [20]. The performance of passive islanding detection techniques depends on threshold selection.…”

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confidence: 99%

Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

et al. 2021

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Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards. INDEX TERMSCloud computing, context aware policy, edge device, hierarchical control, IoT, machine learning, microgrid, smartgrid. NOMENCLATURE f frequency I Current I d d-component Current I q q-compnent Current K P Active Power droop coefficient K Q Reactive Power droop coefficient V VoltageThe associate editor coordinating the review of this manuscript and approving it for publication was Zhouyang Ren .

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A passive islanding detection technique for distributed generations

Cited by 15 publications

References 20 publications

A novel CSoDoA based passive islanding detection technique for multi‐DER AC microgrid control

A novel CSoDoA based passive islanding detection technique for multi‐DER AC microgrid control

Islanding Detection o f Distributed Generat ion In The Presence Of Fault Events With C ritical Load And Non Critical Load

Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

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