Power quality event dynamics characterization via 2D trajectories using deviations of higher-order statistics

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

doi:10.1016/j.measurement.2018.04.098

Cited by 16 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this sense, the proposed method is a time compression method conceived of to provide flexibility to the instruments that introduce PQ functionalities. Their main difference related to previous works [14,28] resides in the possibility to inform about the normal operating conditions by measuring the voltage waveform both in the long term and short term. A scalable and global PQ index is introduced and related to the bi-dimensional graphs that store the voltage information of the network status.…”

Section: Pq Global Indicesmentioning

confidence: 95%

“…More work involving the trajectories of real HOS estimators and their deviations from the ideal 50-Hz sinusoidal waveform has been developed in [28], which proposes a method that computes deviations of the supplied energy in more intuitive graphs. That contribution was focused on specific PQ event detection such as sags and swells in short monitoring periods.…”

Section: Pq Global Indicesmentioning

confidence: 99%

See 1 more Smart Citation

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

et al. 2018

Self Cite

View full text Add to dashboard Cite

The increasing development of the smart grid demands reliable monitoring of the power quality at different levels, introducing more and more measurement points. In this framework, the advanced metering infrastructure must deal with this large amount of data, storage capabilities, improving visualization, and introducing customer-oriented interfaces. This work proposes a method that optimizes the smart grid data, monitoring the real voltage supplied based on higher order statistics. The method proposes monitoring the network from a scalable point of view and offers a two-fold perspective based on the duality utility-prosumer as a function of the measurement time. A global PQ index and 2D graphs are introduced in order to compress the time domain information and quantify the deviations of the waveform shape by means of three parameters. Time-scalability allows two extra features: long-term supply reliability and power quality in the short term. As a case study, the work illustrates a real-life monitoring in a building connection point, offering 2D diagrams, which show time and space compression capabilities, as well.Keywords: signal waveform compression; higher-order statistics (HOS); power quality (PQ); computational solutions for advanced metering infrastructure (AMI); smart grid (SG) applications

show abstract

Section: Pq Global Indicesmentioning

confidence: 95%

Section: Pq Global Indicesmentioning

confidence: 99%

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…1. In addition, the ideal values for a sinusoidal waveform are indicated as reference values [3], [5]. Skewness of a half cycle (sinusoidal waveform) tends to be negative.…”

Section: Hos Indicesmentioning

confidence: 99%

“…In this sense, higher-order statistics (HOS) can help in the classification, as they are tools capable to depict the shape of a waveform. HOS have been used before in power quality studies in order to characterise symmetry and shape of the voltage waveform distributions (bimodal distributions) via the 3 rd and 4 th order cumulates [3]. HOS also account for the nonlinear assessment of the signal [4] and are less sensitive to noise, which is good for nonlinear system characterization [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of circuit topologies of appliances based on higher order statistic

Florencias-Oliveros¹,

Blanco²,

Meyer³

et al. 2019

REPQJ

View full text Add to dashboard Cite

“…The institute of electrical and electronics engineers (IEEE) definition for voltage sag is as follows: the RMS voltage drops to 10%-90% of the rated value, and the duration is 10 ms-60 s [1,2]. Significant negative impacts on society and tremendous economic losses in industrial production have been caused by voltage sags [3][4][5]. In 2010, a production process was interrupted as a result of the abnormal operation of the frequency converter because of voltage sag in a cigarette factory in Xuzhou, Jiangsu Province, China.…”

Section: Introductionmentioning

confidence: 99%

Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Mei

Ren

et al. 2018

Energies

View full text Add to dashboard Cite

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

show abstract

Power quality event dynamics characterization via 2D trajectories using deviations of higher-order statistics

Cited by 16 publications

References 20 publications

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

Automatic classification of circuit topologies of appliances based on higher order statistic

Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Contact Info

Product

Resources

About