A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems

Robles, Oswaldo Isaac Cortes; Barocio, E.; Segundo, J. P.; Guillén, Daniel; Olivares-Galván, J. C.

doi:10.1016/j.measurement.2019.107453

Cited by 17 publications

(9 citation statements)

References 37 publications

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“…Recently, due to the difficulty for detecting various and more complex disturbances that may appear in the electrical network, techniques with the ability to handle and process large volumes of data and find relations among the types of disturbances have been considered. For instance, classical machine learning techniques like support vector machines (SVM) [97][98][99], artificial neural networks (ANN) [85,[100][101][102], deep learning (DL) [103,104], and other machine-learning techniques. Notwithstanding, several studies identify a combination of power disturbances described in the standards [105].…”

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

confidence: 99%

“…Otherwise, in [90] a Some examples of the works reported for detecting and classifying electric power disturbances are described in next. The work developed in [98] describes a scheme in which the input signal is first decomposed through the variational mode decomposition (VMD), then the recurrence quantification analysis (RQA) for defining the frequency and duration of the disturbances is performed. This method achieves, by means of data-driven, an adequate parameterization of the present disturbances.…”

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

confidence: 99%

“…The study presented in [93] analyses the transient characteristics of induction motors under the influence of sag disturbances using a multi-slice field-circuit-motion integrating timestepping finite element method. Additionally, in [98], the propagation produced by the induction motors in sags disturbances is analyzed. In [97], the first part of a study is presented where the interaction of induction motors against voltage sags disturbances is presented.…”

Section: Techniques For Power Quality Related To Electrical Machines ...mentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

Section: Techniques For Power Quality Related To Electrical Machines ...mentioning

confidence: 99%

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

et al. 2022

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“…Wang Y et al [ 24 ] applied SVM with Multi Resolution Analysis of DWT to categorise different PQEs. Cortes Robles et al [ 25 ] proposed multi-scale recurrence quantification decomposition (MSRQD) method along with SVM classifier for classification of complex PQEs in grid connected MG system. Furthermore, SVM with different kinds of kernel functions can be used to enhance the classifier performance while solving the non-linear nature of classification problems in PQ study [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

Vinayagam¹,

Othman

Veerasamy

et al. 2022

PLoS ONE

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This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.

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“…Furthermore, Ref. [12] is presents a method based on variational mode decomposition of the original signal and the recurrence quantification analysis which reaches a proper characterization of the electrical signals prior to their identification; it follows a data-driven approach. In [13], a study focused on feature selection explores the performance achieved by different subset through features extracted from commonly signal processing techniques.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

et al. 2021

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Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

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A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems

Cited by 17 publications

References 37 publications

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

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

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

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