PQ disturbance detection and classification combining advanced signal processing and machine learning tools

Shafiullah, Md; Khan, Meer Abdul Mateen; Ahmed, Shakir D.

doi:10.1016/b978-0-12-823346-7.00012-8

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…Furthermore, the complex nature of this classifier allows the use of the DFT instead of the Wavelet transform in the preprocessing stage, obtaining comparable classification results. The Wavelet transform is also used as a feature extraction technique in [27,28] with excellent results. Compared to the classification techniques proposed in [28], the use of the CNN presented in this paper shows a different implementation of convolutional layers combined with STFT.…”

Section: Combination Of Univariate Randomly Optimized Neural Network ...mentioning

confidence: 99%

Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines

et al. 2023

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The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed here uses the Fourier transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a multilayered neural network with multivalued neurons (MLMVN). In this case, the classifier structure has three layers and a small number of neurons in the hidden layer. This allows complex-valued inputs to be processed without the need for pre-coding, thus reducing computational cost and keeping training time short. The second technique involves the use of the short-time Fourier transform (STFT) and a convolutional neural network (CNN) with 2D convolutions in each layer for feature extraction and dimensionality reduction. The voltage waveform perturbations taken into consideration are: voltage sag, voltage swell, harmonic pollution, voltage notch, and interruption. The comparison between the two proposed techniques is developed in two phases: initially, the simulated data used during the training phase are considered and, subsequently, various experimental measurements are processed, obtained both through an artificial disturbance generator and through a variable load. The two techniques represent an innovative approach to this problem and guarantee excellent classification results.

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Section: Combination Of Univariate Randomly Optimized Neural Network ...mentioning

confidence: 99%

Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines

et al. 2023

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“…As demonstrated by Yılmaz et al (2022), five features are extracted from the decimated wavelet transform, including the mean, standard deviation, variance, entropy, and energy. In addition, 24 features were derived from the discrete wavelet transform for each PQD case by Shafiullah et al (2021). It will highly speed up the detection of PQDs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

Wu,

Liu,

Zhu

et al. 2024

Front. Energy Res.

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In the evolving landscape of power systems, the integration of various renewable energy resources (RERs) introduces complex challenges, particularly in maintaining power quality, which are paramount for system stability. To address this issue, an adaptive power quality disturbance (PQD) detection framework is implemented in this paper. First, the optimal mode decomposition (OMD) is developed to decompose the compound PQDs into sub-ingredients to make them more visible based on the optimal energy ratio. Subsequently, we propose an improved attention convolutional neural network (IACNN), an advanced neural network architecture that leverages an enhanced attention mechanism to expedite the identification of PQDs. Importantly, the sub-ingredients can be strengthened based on the established PQD detection framework. Finally, a series of experiments are conducted under different noise levels and various types of PQDs. The results demonstrate that the proposed framework has profound detection effectivity with about 99.2% accuracy under the simulation condition of 20 dB noise level. In addition, the experimental verification analysis proves a satisfactory real-time performance. This underscores the potential of the proposed framework as a significant advancement in the realm of power quality management, offering a robust solution to the challenges posed by the integration of RERs into modern power systems.

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“…By denoising the signal, the signal localization accuracy is improved. And by compressing and reconstructing the signal, the main fluctuation features of different types of signals are extracted so as to overcome the limitation of single feature parameters and achieve accurate localization of disturbed signals [4][5].…”

Section: Introductionmentioning

confidence: 99%

Big data-based method for automatic localization of power quality disturbance signal of PV access distribution network

Zhen¹,

Han²,

Wang³

et al. 2023

J. Phys.: Conf. Ser.

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At present, the conventional automatic localization method of the power quality disturbance signal of the distribution network mainly extracts the feature vector of the disturbance signal. It constructs the target localization function, which leads to poor localization accuracy because the noise part of the disturbance signal is ignored. In this regard, the automatic localization method of PV access power quality disturbance signal based on big data is proposed. By using the wavelet decomposition algorithm, the noise part of the power quality disturbance signal is removed, and the measurement matrix and reconstruction function are combined with compressing and reconstructing the disturbance signal. Finally, the automatic positioning of the disturbance signal is achieved by extracting the characteristics of the disturbance signal data and clustering analysis processing. In the experiment, the designed signal localization method is tested for the localization effect. The results can prove that when the proposed method is used to automatically localize the disturbed signal, the localization results are consistent with the spatial feature distribution of the signal and have a more desirable automatic localization effect.

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PQ disturbance detection and classification combining advanced signal processing and machine learning tools

Cited by 12 publications

References 24 publications

Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines

Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

Big data-based method for automatic localization of power quality disturbance signal of PV access distribution network

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