A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network

Wang, Shouxiang; Chen, Haiwen

doi:10.1016/j.apenergy.2018.09.160

Cited by 302 publications

(134 citation statements)

References 36 publications

Supporting

Mentioning

131

Contrasting

Unclassified

Order By: Relevance

“…When tested on real ground performance degrades in comparison to ideal case. The concept of long short term memory combined with convolution neural network [21] is reported for monitoring of power quality but it have number of drawbacks such as it is layered network which add the complexity in processing as the number of layers increase memory requirement also ramp up and diverse training dataset cause over fitting problem. The separated features applied to subsequent stages for classification of power disturbances and above mentioned techniques mostly designed for classification of single events and having large calculations when applied in the actual scenario of power quality.…”

Section: Previous Workmentioning

confidence: 99%

Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

Rahul

2020

Advcd Theory and Sims

View full text Add to dashboard Cite

This article proposes an application of Dual Tree Complex Wavelet Transform (DTCWT) with non-dominated sorting genetic algorithm III (NSGA III) based multi-objective optimization and directed acyclic graph support vector machine (DAG-SVM) for recognition and classification of power quality events. The proposed method first employs DTCWT for extraction of features then NSGA III algorithm for an effective optimization of extracted features. After that, DAG-SVM based classifier are used to predict the classes of PQ disturbances. The NSGA III algorithm generates optimalsolutions based on multi objective optimization and then fitness function is generated with the help of Pareto front to obtain unique features set from power signals. The NSGA III not only optimizes selected features with DTCWT but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of Directed Acyclic Graph-SVM to classify the power quality disturbances. The short event detection, lesser computational timing, superior classification accuracy, and high anti-noise performance are the main advantages of the proposed method.

show abstract

Section: Previous Workmentioning

confidence: 99%

Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

Rahul

2020

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…The nonlinear activation function is often chosen to be the sigmoid function or hyperbolic tangent function ("S "-shaped functions). Most recent deep neural networks use rectified linear units (ReLU) [69], which output 0 if the input is less than 0, and raw output otherwise, i.e., S (x) = max(0, x). The present study adopts the ReLU activation function to develop deep neural networks for temperatures and species concentrations retrieval.…”

Section: Machine Learning For Inverse Radiation Calculationmentioning

confidence: 99%

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

et al. 2019

View full text Add to dashboard Cite

Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO 2 , H 2 O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of φ =1, φ = 0.8, and φ = 1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry. performed to address the inverse radiation problems using gradient-based [14] optimization methods. Griffith et al. [15,16] were the first to recognize that measurements of the transmissivity or emissivity of rotational spectral lines of a gas can reveal its temperature. In order to extract temperature, a nonlinear least-square method was used to fit the integrated transmissivity minima. Best et al. [17,18] combined tomography and Fourier transform infrared (FTIR) spectrometer transmission and emission spectra to extract temperature, concentration and soot volume fraction fields. By measuring spectral intensity of the CO 2 4.3 µm band, temperature profiles were retrieved in a number of ways [19,20]. At their time these results were not accurate enough due to lack of an accurate radiation prediction model and robust inverse algorithms. Song et al. [21,22] developed a spectral remote sensing technique to reconstruct CO 2 temperature profiles based on radiative intensity measurements. An accurate narrow band radiation model was employed and several Newton-type regression methods were tested. Due to the nonlinearity and ill-posedness of the problem, regularization of the inverse problems was applied to enforce some degree of smoothness to the solution. It is always difficult to select an appropriate regularization parameter and empirical values for the regularization parameter were employed. Ren and Modest [23] applied the Levenberg-Marquardt optimization method with Tikhonov regularization to reconstruct CO 2 temperature profiles and average concentrations from synthetic line-of-sight spectral intensity data. Two types of temperature profiles were tested for different gas path lengths and different CO 2 spectral bands. A new regularization selection method based on the combination of the L-curve criterion and the discre...

show abstract

“…In the real power system network, multiple power quality (MPQ) disturbances have been occurred due to power failure, capacitors switching, power electronic circuits, etc . Many methods have been revealed for the detection and classification of single PQD signal . In recent years, few classification methods have been explored for multiple PQDs .…”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

show abstract

A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network

Cited by 302 publications

References 36 publications

Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

Classification of multiple power quality events via compressed deep learning

Contact Info

Product

Resources

About