A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN)

Bhavani, R.; Prabha, N. Rathina

doi:10.1109/itcosp.2017.8303073

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…When the grid and one or more DG(s) supply power in synchronism to end-users, the DG is in nonislanded (grid-connected) mode, while when the DG is disengaged from the grid and still actively supplying consumers, the DG is in islanded mode [16].Islanding (unintentional islanding) is a significant problem that power engineers are faced with. Islanding can severely impact power quality and can lead to an unsafe working environment [17]. Islanding also hurts protection device coordination.…”

Section: Theoretical Background/ Reviewmentioning

confidence: 99%

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

Adenugba

Orovwode

Ademola

2022

IOP Conf. Ser.: Earth Environ. Sci.

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In today’s power system, distributed generation (DG) penetration level has increased to match the ever-increasing demand for energy. DG integration introduces peculiar challenges to the entire power system. Due to the increased DG penetration, these challenges have become technically and economically very important. Effective and fast islanding detection is necessary to prevent power quality degradation, equipment loss, and human life loss. In this study, an islanding detection model based on convolutional neural network (CNN) is proposed. The method utilizes the ability of CNN to perform accurate image classification. It identifies islanding by classifying scalogram images obtained by applying wavelet transform to the concatenated voltage waveforms for each event. The islanding detection model is trained with well-processed images to improve classification accuracy. Noise is incorporated into the data to investigate the susceptibility of the method to noise. The results obtained prove that the proposed islanding detection model can handle the problem of islanding detection.

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Section: Theoretical Background/ Reviewmentioning

confidence: 99%

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

Adenugba

Orovwode

Ademola

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Since the hallmark of the CNN is image classification, the 2 dimensional version of the discrete convolution is used. To serve as a reminder, the 2 dimensional discrete convolution operation is shown in Equation (6). The convolutional layer works by adjusting the parameter ω for each backpropagation in order to maximize the features extracted by minimizing the error in classification.…”

Section: Convolutional Layermentioning

confidence: 99%

“…To do this, several techniques are currently exploited using machine learning algorithms [3]. Among the extensive inventories of deep learning algorithms, for the present work, the Long Short Term Memory (LSTM) and the Convolutional Neural Networks (CNN) are being used to detect and classify these disturbances [4][5][6]. Other algorithms are being used to address these problems like the Kalman Filter, Wavelet Transform and the Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

et al. 2020

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The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

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“…Statistical feature extractions are usually being carried out after signal analysis stage [15,16]. Statistical features such as mean, median, RMS, standard deviation, variance, and norm, are extracted as the output features before passing into next stage for higher-order feature extraction.…”

Section: Introductionmentioning

confidence: 99%

LSTM power quality disturbance classification with wavelets and attention mechanism

2022

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Efficient detection and classification of power quality disturbances is required with the increasing penetration of multi-energy systems such as microgrids and features from renewable energy resources. Machine learning approach is popular to generate useful and optimal features from data learning to improve the classification performance. This paper aims to analyse the classification performance using the hybrid model of multi-resolution analysis and long short-term memory network. The proposed model uses four-level decomposition wavelet transform to increase the resolution of input signals into multi-bands signal representation. Spatial and temporal feature representation of the wavelet coefficients are highlighted using attention mechanism before feeding into long short-term memory network for sequence feature extraction. The sequence feature output is then passed into multiple dense layer for the classification process. Synthetic disturbance signals are used as training samples. The performance test carried out includes the condition of 20–50 dB signal-to-noise ratio signals, where additive white Gaussian noise are added into the test samples.

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A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN)

Cited by 17 publications

References 17 publications

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

LSTM power quality disturbance classification with wavelets and attention mechanism

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