Classification of Electrical Disturbances in Real Time Using Neural Networks

Monedero, Íñigo; León, Carlos; Ropero, Jorge; Garcia, A.; Elena, J.M.; Montañó, J.C.

doi:10.1109/tpwrd.2007.899522

Cited by 120 publications

(62 citation statements)

References 8 publications

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“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

Section: Reference Year Application Fieldmentioning

confidence: 99%

“…An MLP neural network, an RBF network and an SVM with different kernels (linear, polynomial and Gaussian) were tested in this classification problem, obtaining performances in accuracy (depending on the type of device that generated the harmonics) between 70% and 98%. In [49], an MLP with three hidden layers is used as the classifier to detect anomalies in the voltage, frequency and harmonic components of electric signals. The input (predictive) variables have been extracted from an electrical pattern simulation and include the application of wavelets in order to obtain different levels of signal analysis.…”

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

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A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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Section: Reference Year Application Fieldmentioning

confidence: 99%

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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“…In the same former line, using the WT as a preprocessing tool for ANN, the second work by Iñigo Monedero et al [22] developed a neural perceptron-based (three hidden layers) real-time first system prototype, which was trained using a disturbance generator for pseudosynthetic waveforms. They obtained a performance of 89% success combining simulated and real-time data.…”

Section: Ann For Pq Analysis: Towards the Hos Paradigmmentioning

confidence: 99%

HOS network-based classification of power quality events via regression algorithms

Palomares-Salas

Rosa

Fernández

et al. 2015

EURASIP J. Adv. Signal Process.

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This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 power-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility between all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. These results verify that the proposed technique is capable of offering interesting results for power quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.

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“…A combination of wavelet transform along with both ANN and fuzzy logic classifier has been implemented for the PQ events classification in [13].Artificial neural network (ANN) based real time electric power quality disturbance classification has been illustrated in [14]. Support vector machine (SVM) based electrical voltage disturbance classification has been illustrated in [15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System

Sundaram¹,

Neela²

2016

IJCA

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An efficient method for power quality disturbances recognition and classification is presented in this paper. The method used is based on the Kalman filter and fuzzy expert system. Various classes of disturbances are generated using Matlab parametric equations. Kalman filter is used for extracting the input features of various power disturbances. The extracted features such as amplitude and slope are applied as inputs to the fuzzy expert system that uses some rules on these inputs to classify the PQ disturbances. Fuzzy classifier has been implemented and tested for various types of power quality disturbances. The results clearly indicate that the proposed method has the ability to detect and classify PQ disturbances accurately. The performance of the proposed method has been evaluated by comparing the results against Kalman filter based neural classifier.

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Classification of Electrical Disturbances in Real Time Using Neural Networks

Cited by 120 publications

References 8 publications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

HOS network-based classification of power quality events via regression algorithms

Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System

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