Islanding detection for PV and DFIG using decision tree and AdaBoost algorithm

Madani, Seyed Sohail; Abbaspour, Ali; Beiraghi, Mojtaba; Dehkordi, P. Z.; Ranjbar, A.M.

doi:10.1109/isgteurope.2012.6465818

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…The use of ensemble learning methods is another practical way of achieving higher detection accuracy. Recently, ensemble classifiers have been used in many detection problems and have shown promising results [26][27][28][29][30][31][32][33]. Ensemble classifiers make use of multiple learning algorithms in order to achieve a prediction efficiency higher than any of their base learners [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ahmad

2019

Wireless Communications and Mobile Computing

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Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user's (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU's signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

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Section: Introductionmentioning

confidence: 99%

“…In [31], ensemble classifier is used for weather radar anomalous propagation echo detection. Ensemble classifier and AdaBoost [38] based islanding detection under smart grid environment is proposed in [33].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ahmad

2019

Wireless Communications and Mobile Computing

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“…In recent years, in order to improve the performance of passive techniques and reduce NDZ, passive methods based on the combination of soft computing with modern signal processing techniques have been applied. For example, a decision tree in combination with adaptive boosting has been proposed in order to improve the islanding detection accuracy [26]. However, the proposed method's sensitivity to outliners and the noisy conditions is considerable [26].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a decision tree in combination with adaptive boosting has been proposed in order to improve the islanding detection accuracy [26]. However, the proposed method's sensitivity to outliners and the noisy conditions is considerable [26]. Support vector machine (SVM) with wavelet transform has been utilized to detect islanding [27].…”

Section: Introductionmentioning

confidence: 99%

An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

et al. 2018

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This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

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“…In [88], islanding instances are classified with DT algorithm. Adaptive boosting technique is employed to reduce the classification error rate.…”

Section: Dtmentioning

confidence: 99%

Shifting of research trends in islanding detection method - a comprehensive survey

Dutta

Sadhu

Reddy

et al. 2018

Prot Control Mod Power Syst

117

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The augmentation in electricity demand, power system privatization as well as efficacy of renewable resources has paved the way for power system companies and researchers to exploit the field of grid connected distributed generation (DG) and its issues, islanding being a dominant one. Several research works have been conducted to mitigate the issues of islanding detection (ID). In context of this, the paper gives a comprehensive review of islanding issues, standard test systems, criteria and shifting of research trends in islanding detection methods (IDMs). The significant contributions pertain to categorization of IDMs, evaluation of non-detection zone (NDZ) for each test system, disquisition on evolution and advancement of IDMs and its comparisons based on criteria such as NDZ, run on time, nuisance tripping percentage, applicability in multi DG system and implementation cost to draw out the strength and shortcomings of individual methods that will come to aid to the companies or researchers for establishing the applicability and appropriateness of such method for their concerned domain.

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Islanding detection for PV and DFIG using decision tree and AdaBoost algorithm

Cited by 18 publications

References 25 publications

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Shifting of research trends in islanding detection method - a comprehensive survey

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