Power quality event classification using optimized Bayesian convolutional neural networks

Ekici, Sami; Uçar, Ferhat; Dandil, Besir; Arghandeh, Reza

doi:10.1007/s00202-020-01066-8

Cited by 23 publications

(19 citation statements)

References 25 publications

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“…In this paper, CWT is chosen for analysis of PQDs. However, CWT [7] and DWT [8,9] in many PQDs classification papers have been proposed as an effective signal processing technique. In CWT, the signal of all time and scales is made compatible with the main wavelet by means of compression, expansion, and transformation processes.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

“…In the feature extraction stage, many signal processing techniques have been proposed to analyze PQDs. Fast Fourier transform (FFT) [5], short-time Fourier transform [6], wavelet transform (WT) [7][8][9][10], S-transform (ST) [1,[11][12][13][14], Hilbert-Huang transform [15,16], Kalman filter (KF) [17], and curvelet transform (CT) [18] are used for feature extraction of PQDs. In the classification stage, types of the PQDs are determined by using the features obtained from feature extraction stage.…”

Section: Introductionmentioning

confidence: 99%

“…Later, many methods such as support vector machine (SVM) [8,20], fuzzy expert system (FES) [17,21], k-nearest neighbor [22], and genetic algorithm [23] have been developed for intelligent classification systems and these systems have been widely used in the classification stage of PQDs. Nowadays, deep learningbased classifiers used as a popular approach in intelligent image recognition algorithms are used in recognition systems for PQDs [3,7,9,18,24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Eristi

Eri̇şti̇

2022

International Transactions on Electrical Energy Systems

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Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

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Section: Continuous Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Eristi

Eri̇şti̇

2022

International Transactions on Electrical Energy Systems

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“…Technological developments, especially in the field of artificial intelligence, 2 have brought great advantages to researchers who focus on PQD classification 3‐6 . Instruments incorporating new functions in the performance of PQD classification have been developed over the last decades 7‐10 …”

Section: Introductionmentioning

confidence: 99%

“…[3][4][5][6] Instruments incorporating new functions in the performance of PQD classification have been developed over the last decades. [7][8][9][10] Because statistical records of the distribution of power quality events are not available in most cases, non-parametric methods are believed to be more suitable for these applications. 11 There are several non-parametric methods that are used in the classification of PQDs, including artificial neural networks, 12 fuzzy logic, 13 and support vector machines, 14,15 as well as the combined use of such approaches like deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

CNN / Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images

Özer

Efe

Özbay

2021

Int Trans Electr Energ Syst

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This paper, using an inverse signal approach, presents a novel deep learning algorithm based on a convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) together with spectrograms for the classification of power quality disturbances (PQDs). The proposed method focuses on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal. By collecting this sine wave together with the original signal, spectrograms of both signals were obtained and converted into red/green/blue (RGB) images that were then combined. Finally, classification was carried out via CNN/Bi‐LSTM. In this context, 29 different disturbance events in both single and combined structures were used. The proposed model was applied to the disturbance events and 99.33% classification accuracy was obtained.

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Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks

Shukla

Panigrahi

Ray

2021

Int Trans Electr Energ Syst

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This paper presents a novel hybrid approach combining Gramian Angular Summation Field (GASF) method with a convolutional neural network (CNN) to classify power quality disturbances. Firstly, a 1-D Power quality disturbance signal is transformed into a 2-D image file using GASF. Subsequently, CNN is implemented for features extraction and image classification. In this work, the synthetic power quality (PQ) disturbances are considered including nine single disturbances and five mixed disturbances. Further, to capture multi-scale aspects of power quality disturbances problem and reduce overfitting, a unit is designed using 2-D convolutional, pooling, and batch-normalization layers.The classification study is further supported by experimental signals obtained on a prototype setup of PV system. The obtained results demonstrate the efficiency and reliability of the proposed method. The proposed method is compared with the other advanced CNNs and other conventional methods to illustrate its effectiveness.

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Power quality event classification using optimized Bayesian convolutional neural networks

Cited by 23 publications

References 25 publications

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

CNN / Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images

Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks

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