Islanding detection in distributed generation using supervised learning techniques

Biaz, Bruno Martins de; Lopes, Thales Terrola e; Fortes, Márcio Zamboti; Ferreira, Vitor Hugo

doi:10.1109/sbse.2018.8395904

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…The back-propagation learning algorithm is the most widely used learning algorithm for MPNN. The back-propagation learning algorithm is a widely used and well-established algorithm for training MPNN, effective in handling complex and non-linear relationships in data, but may suffer from slow convergence [87]. The Boltzmann learning algorithm is suitable for training Boltzmann machines (a type of recurrent neural network) and it can capture dependencies between variables over time but it has low computational efficiency [85].…”

Section: Discussion Of Input Data Types Of Artificial Neural Network ...mentioning

confidence: 99%

“…Software Papers PSCAD-EMTDC [64,74,76,79,80] DIgSILENT [49][50][51]81] ATP [52] PSIM [53] MATLAB [54,59,78,[82][83][84][85][86][87][88][89][90][91] No name [44,65,69,72,73,92]…”

Section: Papersmentioning

confidence: 99%

“…Discrete wavelet transform (DWT) [45,47,49,54,55,59,65,66,72,75,80,83,[88][89][90][91][92][93][94][95][96][97] Wavelet transform (WT) [48,59,62,73,78,87,89,98,99] S-transform (ST) [41,47,52,54,100] Wavelet packet transform (WPT) [77] Fast Fourier transform (FFT) [58] Discrete Fourier transform (DFT) [44,65,85,101] Slantlet transform (SLT) [41,54,84] Hilbert transform (HT) [82,84] Continuous wavelet transform (CWT) [56] Tunable Q wavelet transform (TQWT) [69] Ph...…”

Section: Transform Papersmentioning

confidence: 99%

“…Multilayer perceptron neural network (MPNN) [46][47][48][49]52,58,60,61,63,65,66,[68][69][70][74][75][76]78,79,82,83,86,87,[90][91][92]95,[97][98][99][102][103][104] Probabilistic neural network (PNN) [41,50,55,64,65,77,84,96,100] Adaptive neuro-fuzzy information system (ANFIS) [47,59,62,72,73,75,78,97] Self-organizing map (SOM) neural network…”

Section: Type Of Artificial Neural Network Papersmentioning

confidence: 99%

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Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

Kaluđer,

Fekete,

Čvek

et al. 2023

Applied Sciences

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Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.

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Section: Discussion Of Input Data Types Of Artificial Neural Network ...mentioning

confidence: 99%

“…Software Papers PSCAD-EMTDC [64,74,76,79,80] DIgSILENT [49][50][51]81] ATP [52] PSIM [53] MATLAB [54,59,78,[82][83][84][85][86][87][88][89][90][91] No name [44,65,69,72,73,92]…”

Section: Papersmentioning

confidence: 99%