Faster Detection of Microgrid Islanding Events using an Adaptive Ensemble Classifier

Khamis, Aziah; Xu, Yan; Zhang, Dong

doi:10.1109/tsg.2016.2601656

Cited by 74 publications

(77 citation statements)

References 28 publications

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“…In [21], the ANN was used with FT to detect the occurrence of islanding but it was applied on a MG with single DG based on wind turbine. Many other artificial intelligent techniques are used to detect the islanding such as the DT [22], SVMs [23], neuro-fuzzy logic [24], the adaptive ensemble classifier [25], Hilbert-Huang transform, machine learning techniques [26], and the modified Slantlet transform [27].…”

Section: Nomenclaturementioning

confidence: 99%

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

Abdelsalam¹,

Salem²,

Oda³

et al. 2020

IEEE Access

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This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

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Section: Nomenclaturementioning

confidence: 99%

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

Abdelsalam¹,

Salem²,

Oda³

et al. 2020

IEEE Access

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“…This paper also proposes new performance indices for islanding detection. The average decision speed is 1.11 cycles with an average accuracy of 98.87% [105]. Apart from islanding detection, supervised learning can also be used for predictive detection of an islanding event.…”

Section: Adaptive Ensemble Classifiermentioning

confidence: 99%

Comprehensive review of IDMs in DG systems

Manikonda

Gaonkar

2019

IET Smart Grid

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Distributed generation (DG) offers solution to the ever increasing energy needs by generating energy at the consumer end, in most cases, by means of renewable energy sources. Islanding detection is an important aspect of interconnecting a DG to the utility. This study presents comprehensive review of various islanding detection techniques along with their relative advantages and disadvantages. A broad classification of islanding detection methods (IDMs) is laid out as classical methods, signal processing (SP)-based methods, and computational intelligence-based methods with a focus on SP-based methods and computational intelligence-based methods. The evolution of SP techniques used for islanding detection is presented along with the merits and shortcomings of each technique. Furthermore, the advent of computational intelligence methods based IDMs are discussed along with their merits and demerits. An insight into various islanding methods based on quantitative measures of performance indices such as detection time, detection accuracy, and efficiency are tabulated and presented. Finally, the prospective direction of research for IDMs is also presented.

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“…Hence, anti-islanding with fast response time is essential for a DG connected grid system [10,16]. To overcome the challenges from islanding DGs, researchers proposed a numerous model that deals with the consequences of intentional islanding, and clears it as fast as possible [11,12,[17][18][19][20][21][22][23][24][25][26][27][28][29]. Different organizations such as the IEEE, IEA and IEC also set the standards and were updated regularly to emphasize the importance of islanding detection in a DG connected grid, so that a DG connected system will operate smoothly [14,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems

Shrestha

Kattel

Dachhepatic

et al. 2019

ASI

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The issue of unintentional islanding in grid interconnection still remains a challenge in grid-connected, Distributed Generation System (DGS). This study discusses the general overview of popular islanding detection methods. Because of the various Distributed Generation (DG) types, their sizes connected to the distribution networks, and, due to the concern associated with out-of-phase reclosing, anti-islanding continues to be an issue, where no clear solution exists. The passive islanding detection technique is the simplest method to detect the islanding condition which compares the existing parameters of the system having some threshold values. This study first presents an auto-ground approach, which is based on the application of three-phase, short-circuit to the islanded distribution system just to reclose and re-energize the system. After that, the data mining-decision tree algorithm is implemented on a typical distribution system with multiple DGs. The results from both of the techniques have been accomplished and verified by determining the Non-Detection Zone (NDZ), which satisfies the IEEE standards of 2 s execution time. From the analysis, it is concluded that the decision tree approach is effective and highly accurate to detect the islanding state in DGs. These simulations in detail compare the old and new methods, clearly highlighting the progress in the field of islanding detection.

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Faster Detection of Microgrid Islanding Events using an Adaptive Ensemble Classifier

Cited by 74 publications

References 28 publications

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

Comprehensive review of IDMs in DG systems

Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems

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