A decision tree based approach for microgrid islanding detection

Azim, Riyasat; Zhu, Yongli; Saleem, Hira; Sun, Kai; Li, Fangxing; Shi, Di; Sharma, Ratnesh

doi:10.1109/isgt.2015.7131809

Cited by 19 publications

(9 citation statements)

References 16 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%

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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%

“…The islanding decision is shown in Fig. 10 compared to other techniques such as DT [22], support vector machine (SVM) [23] and ANN [21]. The results show that all DGs are islanded from the utility grid due to the disconnection of main circuit breaker.…”

Section: A) Case 1: Opening the Main Cbmentioning

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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“…Consider measurement vector [25]. The resulting measurement set D, for the training stage, having PMU measurements and corresponding source location information can be formed as in (4).…”

Section: Classification Models For Oscillation Source Locationmentioning

confidence: 99%

Data‐driven disturbance source identification for power system oscillations using credibility search ensemble learning

Banna

Solanki

2019

IET Smart Grid

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Low-frequency oscillations in power system degrade power quality and may trigger blackouts. This study identifies the source location of these oscillations using measurements from phasor measurement unit (PMU), offline credibility estimation and classification models. The performance of these classification models is ranked for each reported feature to use highly ranked models during the online stage. This proposed framework named as credibility search ensemble learning was tested and validated with promising results using western interconnection power system in North America (WECC-179). The reliability and robustness of the proposed framework were checked against measurement errors in PMUs as well as for practical topology change scenarios. Experimental results and performance comparison with average weight-based approach proved that the proposed approach is capable enough to predict the source location of oscillations with good accuracy. An interfacing tool, for MATLAB-WEKA, was developed and employed in this work for validation and testing of the proposed approach.

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“…Passive IDMs proposed in the literature include over/under voltage [9], over frequency protection/under frequency protection (OFP/UFP) [9], rate-ofchange of frequency (ROCOF) [10], rate-of-change of voltage (ROCOV) [11], vector surge relays [12], rate-of-change of phase angle deviation [13], and voltage unbalance/total harmonic distortion (VU/THD) [14]. Several methods based on data mining approaches have also been proposed in [15][16][17][18]. Passive IDMs offer faster detection time and do not degrade power quality.…”

Section: Introductionmentioning

confidence: 99%

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter‐based distributed generations

Azim

Xue

et al. 2017

IET Generation, Transmission & Distribution

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A decision tree based approach for microgrid islanding detection

Cited by 19 publications

References 16 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

Data‐driven disturbance source identification for power system oscillations using credibility search ensemble learning

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter‐based distributed generations

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