Deep Learning Method With Manual Post-Processing for Identification of Spectral Patterns of Waveform Distortion in PV Installations

Oliveira, Roger Alves de; Ravindran, Vineetha; Rönnberg, Sarah; Bollen, Math

doi:10.1109/tsg.2021.3107908

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…The proposed method achieved an overall accuracy of 99.5%. Ge et al (2021) proposed an unsupervised DL and analysis approach for harmonic variation patterns using big data from multiple locations [81]. The proposed method achieved an overall accuracy of 97.2% A DAE is a type of NN that is used for unsupervised learning.…”

Section: Deep Autoencoder (Dae)mentioning

confidence: 99%

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A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

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Section: Deep Autoencoder (Dae)mentioning

confidence: 99%

“…The proposed method achieved an overall accuracy of 97.2% for identifying harmonic patterns. De Oliveira et al ( 2021) proposed a DL method with manual postprocessing for identifying spectral patterns of waveform distortion in PV installations [81]. They achieved an overall accuracy of 97.8% using the proposed method.…”

Section: Deep Autoencoder (Dae)mentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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“…To overcome these drawbacks, the artificial intelligence techniques, the heuristic techniques, and deep learning are being used every time more frequently. The reason is very simple, these techniques are more suitable for treating problems where the prior knowledge of the system is not required, a big amount of data need to be processed, high accuracy is required, data with non-linear behavior, between other advantages [107][108][109]. Several works in the state of the art that address the tasks of detecting and clasifying power disturbances mention that methodologies based on data-driven could be considered to provide excellent results for the PQ analysis [110].…”

Section: Techniques For Power Quality Detection Identification and Mi...mentioning

confidence: 99%

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

et al. 2022

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The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

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“…GANs are applied to the classification of PQ events [40]. DAE is applied in the feature extraction in the classification of PQ events [27], unsupervised feature learning for clustering of daily-harmonics variations [41,42], and spectral data [43].…”

Section: Introductionmentioning

confidence: 99%

A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances

et al. 2022

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This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.

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Deep Learning Method With Manual Post-Processing for Identification of Spectral Patterns of Waveform Distortion in PV Installations

Cited by 10 publications

References 28 publications

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances

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