A PCA-based approach for substation clustering for voltage sag studies in the Brazilian new energy context

Filho, Jacques Miranda; Filho, Jose Maria de Carvalho; Paiva, Anderson Paulo de; Souza, Paulo Vitor Grillo de; Tomasin, Samuel

doi:10.1016/j.epsr.2016.02.012

Cited by 20 publications

(8 citation statements)

References 6 publications

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“…Ward's method was used to form clusters of substations with a similarity level to voltage sags, three distinct groups were found with small, medium and large amount of voltage sags. The work [21] is an evolution of that presented in [20]. The method showed results that are more precise, stable and reliable.…”

Section: State Of Artmentioning

confidence: 98%

“…The results showed that Ward's method was the most appropriate to the considered database. In the paper [20] it is proposed to apply principal component analysis (PCA) to reduce 32 variable input data (with some level of redundancy) by seven principal components (PCs) which account for 97.9% of the information from the original variables, and from these PCs form clusters of substations, using the Ward's method, considering the Euclidean distance between the elements. The formed clusters allowed to classify the distribution systems in three categories regarding the number of occurrence of voltage sags (high, medium and low levels).…”

Section: State Of Artmentioning

confidence: 99%

“…Articles [19][20][21] test several methods of clustering, with the objective of evaluating which one is best suited to form groupings of distribution systems regarding the frequency of voltage sags. These works are the ones that are most related to this paper, but they are focused only on forming the groups, while this paper besides forming the groups, uses this information to establish a base value for the voltage sag index, distinct for each distribution system according to the performance of similar systems.…”

Section: State Of Artmentioning

confidence: 99%

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Cluster-Based Method to Determine Base Values for Short-Term Voltage Variation Indices

et al. 2020

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This paper proposes a methodology for establishing base values for short-term voltage variation indices. The work is focused on determining which variables best describe the disturbance and based on that, establish clusters that allow a more adequate definition of base values for the indices. To test the proposed methodology, real data from 19 distribution systems belonging to a Brazilian electricity utility were used and consequently the index presented in the country standard was considered. This study presents a general methodology that can be applied to all distribution systems in Brazil and could serve as a guide for the regulatory agencies in other countries, to establish base values for their indices. Furthermore, the objective is to show through the results that, with the database used is possible to establish clusters of distribution systems related to the voltage sag and with these establish a base impact factor, distinct for each distribution system.

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Section: State Of Artmentioning

confidence: 98%

Section: State Of Artmentioning

confidence: 99%

Section: State Of Artmentioning

confidence: 99%

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Cluster-Based Method to Determine Base Values for Short-Term Voltage Variation Indices

et al. 2020

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“…Many researchers have particularly worked on voltage sag and/or swell disturbances as these disturbances are the most frequent and dangerous to the health of the entire network. Principle component analysis (PCA) was proposed by Filho et al (2016) for predicting the number of voltage sags, depending on which clustering of different substations has been done for regulatory point of view. Nagata et al (2018) designed detection and segmentation filters for sag and swell using independent component analysis and proved the proposed technique better than WT.…”

Section: Introductionmentioning

confidence: 99%

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Saini

Kapoor

Beniwal

et al. 2019

Transactions of the Institute of Measurement and Control

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In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.

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“…Among these causal sources, short circuit, also called system fault, is the most common cause of this event. However, localization is not a trivial task, since voltage sags occur in a short time interval and propagate throughout the system [1], [5].…”

mentioning

confidence: 99%

Optimized Method for Locating the Source of Voltage Sags

Filho

Borges

Rabelo

et al. 2021

JCOMSS

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Short-Duration Voltage Variations (SDVVs) are the power quality disturbances (PQD) that mainly affect industrial systems, and are originated for various reasons, in particular short circuits over large areas, even those originating in remote points of the electrical system. The location problem aims to indicate the area or region or distance from the substation that is connected to the source causing the voltage sags, and is a fundamental task to ensure good power quality. One of the strategies used to determine the location of sources causing SDVVs and for an implementation of machine learning algorithms in modern distribution networks, called Smart Grids. Monitoring a Smart Grid plays a key role, however mostly it generates a large volume of data (Big Data) and as a result, multiple challenges arise due to the properties of this data such as volume, variety and velocity. This work presents an optimization through genetic algorithm to select meters which already exist in the Smart Grid, using a voltage sag location method in order to reduce the data obtained and analyzed throughout the localization process. Optimization was evaluated through a comparison with a non-optimized localization method, this comparison showed a difference between the hit rates of less than 1%.

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A PCA-based approach for substation clustering for voltage sag studies in the Brazilian new energy context

Cited by 20 publications

References 6 publications

Cluster-Based Method to Determine Base Values for Short-Term Voltage Variation Indices

Cluster-Based Method to Determine Base Values for Short-Term Voltage Variation Indices

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Optimized Method for Locating the Source of Voltage Sags

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