Micro-genetic algorithms for detecting and classifying electric power disturbances

Jaen-Cuéllar, Arturo Yosimar; Morales-Velazquez, Luis; Romero-Troncoso, René de Jesús; Moriñigo-Sotelo, Daniel; Osornio-Ríos, Roque Alfredo

doi:10.1007/s00521-016-2355-z

Cited by 14 publications

(10 citation statements)

References 41 publications

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“…In such a way, in which parameterized models or functions are impossible to define, another application is the problem. The characteristics of that issue, such as a wide design space, nonlinearity, non-convexity, and multi-objectivity, make it impossible for classical methodologies to find an adequate solution [ 32 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization

Pardo-Zamora

Romero-Troncoso

Millán-Almaraz

et al. 2021

Sensors

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The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization

Pardo-Zamora

Romero-Troncoso

Millán-Almaraz

et al. 2021

Sensors

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“…Various combinations of this technique help to select the best features for classification of the PQ disturbances. This includes, Microgenetic algorithms [149], statistical approach [150], and online sequential learning algorithm in [151]. The optimal feature selection using DT in the presence of RE sources (SPV and WE) is presented in [152].…”

Section: A: Genetic Algorithm-based Optimization Techniquesmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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“…The GA technique is a method for solving constrained and unconstrained optimisation problems, and it is based on the Darwin theory of natural selection [32]. The GA iteratively modifies a population of individuals that represent potential solutions, until global convergence is reached when the provided solutions finally meet the problem studied.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances

Romero-Ramirez¹,

Elvira-Ortiz²,

Jaen-Cuéllar³

et al. 2020

IET Generation, Transmission & Distribution

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Micro-genetic algorithms for detecting and classifying electric power disturbances

Cited by 14 publications

References 41 publications

Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization

Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances

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