A novel instrument for power quality monitoring based in higher-order statistics: a  dynamic triggering index for the smart grid

Florencias-Oliveros, Olivia; Agüera-Pérez, Agustín; González-de-la-Rosa, Juan-José; Palomares-Salas, José Carlos; Sierra-Fernández, José María

doi:10.24084/repqj15.212

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In this regard, several techniques and methodologies, mainly, focused on transformations as Wavelet based, Fourier or S transform as traditional methods [7]. Nevertheless, others approaches, as the work presented in [8], propose new PQ indexes based on higher order statistic measurements. Thus, it is required often specific work in the feature calculation and feature reduction stages for a proper classification of PQ disturbances, with the aim to avoid problems referred to the loss of information or limits in data variability.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Monitoring and Disturbances Classification based on Autoencoder and Neural Network for Electrical Power Supply

Gonzalez-Abreu¹,

Viol²,

Delgado-Prieto³

et al. 2020

REPQJ

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The analysis and monitoring of Power Quality (PQ) are a research topic that has concerned with the scientific community in recent years. Several studies have been realized, in which, the object of study regard different approaches; the impact of deficient power supply, the effects caused by the charges in the system or methodologies for the detection, identification and classification of the phenomena that are referred as PQ disturbances, these problematics must be faced to counter the negative effects generated. In this paper is presented two approaches for the characterization and classification of various PQ disturbances, the techniques are involved in the application of subjects related to artificial intelligence; machine learning and deep learning, which in recent years have been used in different areas of study with a good performance for the applications developed. A comparative from these two techniques is performed; the first, machine learning technique, linear discriminant analysis, and the second technique proposed is a deep learning tool called autoencoder. The methodology is tested with a case study with real signals that contain variations in the voltage signal, the results from each technique are presented and the conclusions indicate the comparative realized.

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Section: Introductionmentioning

confidence: 99%

Power Quality Monitoring and Disturbances Classification based on Autoencoder and Neural Network for Electrical Power Supply

Gonzalez-Abreu¹,

Viol²,

Delgado-Prieto³

et al. 2020

REPQJ

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“…Florencias-Oliveros et al [42] present the analysis of recorded signals representing different disturbances. The proposed index realizes a comparison of the variance values, skewness, and kurtosis connected with each cycle, versus the ideal signal.…”

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confidence: 99%

Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

et al. 2020

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This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.

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A single index for voltage quality ranking in the distribution power networks using multiple criteria decision‐making

Bajaj

Singh

2021

Int Trans Electr Energ Syst

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Summary The quality of voltage is a fundamental characteristic that forms a crucial part of reliability in smart distribution power grids and subsequently being cautious to that is obligatory. Voltage quality (VQ) issues can be categorized into manifold categories such as voltage fluctuation, voltage harmonic distortion, voltage unbalance etc, depending upon the type of deviation from the standard sinusoidal one in terms of magnitude and frequency. For evaluating the VQ at a distribution system's bus, classically all the crucial phenomena associated indices are obligatory to be discretely measured and benchmarked for guaranteeing the global healthy VQ performance, which makes the assessment of the same a quite cumbersome. This paper attempts to streamline the overall VQ evaluation process by the application of multiple criteria decision‐making. The scores of all VQ phenomena are computed by applying the analytic hierarchy process considering the multiple criteria of impacts on the consumer. The steps of the proposed methodology result in the formulation of an overall VQ index, which is alone adequate for raking the VQ performance of all the buses in a distribution power network. The proposed approach's application importance is checked and validated using data collected from a modified IEEE 13‐bus test distribution system with and without the inclusion of distributed generation systems using MATLAB programming.

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A novel instrument for power quality monitoring based in higher-order statistics: a dynamic triggering index for the smart grid

Cited by 6 publications

References 11 publications

Power Quality Monitoring and Disturbances Classification based on Autoencoder and Neural Network for Electrical Power Supply

Power Quality Monitoring and Disturbances Classification based on Autoencoder and Neural Network for Electrical Power Supply

Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

A single index for voltage quality ranking in the distribution power networks using multiple criteria decision‐making

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