A Unified Index for Power Quality Assessment in Distributed Generation Systems Using Grey Clustering and Entropy Weight

Sacasqui, Marcos; Luyo, Jaime E.; Delgado, Alexi

doi:10.1109/andescon.2018.8564631

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…Objective weighting method relies on specific measurement data only and thus cannot reflect the attention paid to different indexes by decision makers. The commonly used objective weighting methods include variation coefficient method , maximization deviation method , entropy weight method, etc. The entropy weighting method relies on the concept of information entropy and determines the weight of the index according to the variability between the indicators.…”

Section: Fuzzy Comprehensive Evaluation Algorithmmentioning

confidence: 99%

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Synthetic evaluation of DC power quality based on combination weighting

Qingxian³

et al. 2020

IEEJ Transactions Elec Engng

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With the development of global renewable energy, DC transmission and DC power distribution technology have been applied on a large scale, and the requirements for DC power quality (PQ) are more stringent. Evaluating DC PQ reasonably and effectively has become a research hotspot at this stage. In this paper, a comprehensive analysis for various indexes of DC PQ is conducted to obtain a unified parameter for describing the characteristics of PQ from an overall perspective. First, weight values of PQ indexes are calculated by combining fuzzy analytic hierarchy process (FAHP) and the improved entropy weight method. FAHP can satisfy the consistency without the consistency test, and the improved entropy weight method is an objective weighting method that can effectively avoid the preference of subjective weighting method. Then, based on a triangular fuzzy function, the membership degree is obtained. Finally, a fuzzy comprehensive evaluation is made. An example analysis is carried out with the data provided by the Gree photovoltaic direct‐driven inverter multi‐variable refrigerant flow system to show the validity of the proposed method for evaluating DC PQ. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Fuzzy Comprehensive Evaluation Algorithmmentioning

confidence: 99%

“…At the same time, we compared the improved entropy weight method with the variation coefficient method , maximization deviation method , and traditional entropy weight method . The real data of Gree photovoltaic cottage are used as experimental data.…”

Section: Algorithm Examples Analysismentioning

confidence: 99%

Synthetic evaluation of DC power quality based on combination weighting

Qingxian³

et al. 2020

IEEJ Transactions Elec Engng

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“…The DC distribution systems become more attractive in industrial plants [5], which usually include various DC loads and AC loads, and use power electronic converters to realize AC and DC power conversion. These power converters provide fast response capability and effective filtering against power disturbances [6][7][8]. However, compared to the study on power quality (PQ) factors of AC power systems, many PQ issues have not been explicitly resolved or studied [9][10][11], including harmonics, interharmonics, sag, swell, interruptions, transients, and notch, which are mainly caused by load changes, switching phenomena, power electronic equipment, transformer charging, non-linear loads and environmental factors [12].…”

Section: Introductionmentioning

confidence: 99%

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Luo

et al. 2020

Energies

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Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

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“…The present study will be accomplished through the collection of data from the city of Upper Hunter, Australia, since it is recognized as one of the cities with the greatest negative impact in terms of air quality; therefore, a prediction of the behavior of air particles is one of its most urgent needs to address this problem. Given that air pollution, in Australia, has been estimated to be responsible for more deaths than road accidents [16], likewise Australia has been considered one of the countries with the highest levels of asthma in the world due to this air quality [17], [18]. As a result, there have been public complaints recently where residents of Upper Hunter, Muswellbrook claim that air pollution in their city has become part of their daily lives, getting worse every day and affecting the public health of citizens [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado¹,

Ricardo²,

Carbajal³

2020

IJACSA

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Air quality is crucial for the environment and the life quality of citizens. Therefore, in the present study a software application is developed to predict air quality on the basis of 2.5 particulate matter (.) and 10particulate matter (), in the city of Upper Hunter, Australia, as it is considered to be one of the cities with the lowest air quality levels worldwide. For this purpose, it has been decided to use the methodology of long-short term memory (LSTM) from data collected by NSW department of planning industry and environment during the period of 30 September 2012 to 30 September 2019, to predict the behavior of the mentioned particulate matter during the month of October 2019. A comparison between the average and maximum values suggested by the software and the actual values has been made and it is shown that the predicted results of the study are quite close to reality. Finally, the results obtained in this study may serve as a basis for local authorities to proceed with the necessary protocols and measures in case an alarming prediction occurs.

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A Unified Index for Power Quality Assessment in Distributed Generation Systems Using Grey Clustering and Entropy Weight

Cited by 15 publications

References 8 publications

Synthetic evaluation of DC power quality based on combination weighting

Synthetic evaluation of DC power quality based on combination weighting

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

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