Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

Zhang, Yuwei; Zhang, Yin; Zhou, Xuelian

doi:10.1016/j.measurement.2021.110390

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…Besides, for PQD detection in noisy situations, a two-dimensional Riesz Transform is applied to convert one-dimensional signals into two-dimensional signals to obtain efficient features [50]. Feature sparse, and fusion selection was performed on the raw voltage transformed GIP [51] and later combined with ANN to improve the classification accuracy. In addition, GASF was presented to convert one-dimensional PQD signals into two-dimensional image files [52] and subsequently applied to prototype photovoltaic systems.…”

Section: Grayscale Image Processing Technologymentioning

confidence: 99%

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A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Section: Grayscale Image Processing Technologymentioning

confidence: 99%

“…Unlike traditional clasS-transformed feature extraction methods, the dimensional transformation was used in [51] to convert PQS to grayscale images. An FNN classifier selected the features to distinguish single PQ events from combined PQ events.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…Se divide la parte imaginaria en la parte real de (1). Se selecciona la parte imaginaria de (1) para determinar 𝐶 𝑟 ; conforme a la división de la parte imaginaria en la parte real se extrae 𝑅 𝑟 , 𝑋 𝐶𝑟 y 𝑋 𝐶𝑟 𝑅 𝑟 reemplazando en la parte imaginaria de (1) obteniendo (2).…”

Section: A Análisis Circuito Eléctrico Equivalente Transformador De T...unclassified

“…Estas perturbaciones de calidad de la energía afectan la óptima operación de cargas sensibles como computadoras de precisión y microprocesadores en la red, y pueden causar accidentes de seguridad y pérdidas económicas [2] [5]. Para hacer frente a estas perturbaciones surge una rama de la ingeniería eléctrica conocida como calidad de la energía, de la cual, existen varios estándares desarrollados especialmente por IEEE (Institute of Electrical and Electronics Engineers), ANSI (American National Standards Institute) y NEMA (National Electrical Manufactures Association).…”

Section: Introductionunclassified

Design of a ferroresonant power conditioner

Fernández¹,

Peñaranda²,

Rodriguez³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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Resumen-Este trabajo trata del diseño de un acondicionador de potencia basado en transformador de tensión ferroresonante, enfatizándose el análisis de circuito eléctrico y circuito magnético equivalente para proponer el diseño de un estudio de caso. Se presentan pruebas experimentales del diseño propuesto teniendo resultados satisfactorios para cambios en la tensión de entrada por encima del valor nominal.

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“…Downsampling, based on empirical mode decomposition, can be used to calculate the characteristics of the data to better detect PQ-distorted signals 17 . A visual attention technique can select data feature indices, in the fusion of PQ detection and scale analysis, using processed images of the original signals 18 . Adaptive resolution based on the S transform can be used to analyze the PQ data in the frequency-time domain.…”

Section: Introductionmentioning

confidence: 99%

Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

Zjavka

2022

Sci Rep

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Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance.

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Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

Cited by 30 publications

References 26 publications

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Design of a ferroresonant power conditioner

Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

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