Voltage sag estimation of real transmission systems for faults along the lines

Mambro, Enrica Di; Caramia, P.; Varilone, P.; Verde, P.

doi:10.1109/ichqp.2018.8378833

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…As shown in Table 1, also with a high frequency of sags (from 1 per week to 1 per month) an accuracy of 10% would require registering sags for 7 or 30 years, respectively. Consequently, most of the relevant literature has been mainly focused on the estimation of the average performance of a system in terms of the yearly frequency of sags [8][9][10][11], rather than on forecasting future performance. However, forecasting one year in advance of the expected number of sags at each site can guide the decisions of the Distribution System Operator (DSO) for network enhancement or reinforcement.…”

Section:  mentioning

confidence: 99%

Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices

Casolino,

de Santis,

Di Stasio

et al. 2024

IEEE Open J. Power Energy

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The field measurement campaigns have revealed that voltage sags also occur as clusters and not only as rare phenomena. The clusters of sags represent a stochastic process due to their time dependence; the rare satisfy the requirements for a Poisson distribution process. To forecast both kinds of sags using the statistics of the measurements, different approaches are required. In this study, a general method for predicting both types of sags is proposed with a procedure that can be implemented automatically. The method uses intermittent indices to distinguish between the sites that have a prevalent number of rare sags and the sites where rare sags and clusters occurred. Based on this means of identification, the technique offers two distinct models for predicting each kind of sag. The final goal is to implement the procedure in a measurement system that can automatically pre-analyze the recorded sags and choose the best technique for prediction depending on the type of sag. The first results were satisfying with forecast errors reduced in comparison with those obtained without the proposed procedure.

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Section:  mentioning

confidence: 99%

Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices

Casolino,

de Santis,

Di Stasio

et al. 2024

IEEE Open J. Power Energy

Self Cite

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“…In the relevant literature, the only way to characterize the performance of the system in terms of the expected number of voltage sags, usually expressed as yearly frequency, was to resort to simulations based on the system modelling in short circuit conditions [3][4][5][6][7][15][16][17][18][19]27,33]. It was considered unfeasible to characterize the expected number of sags derived from measurements because decades of years were needed to obtain the statistical description of the number of voltage sags suitable for forecasting the future performance with adequate confidence [34].…”

Section: The Use Of Ttne For the Statistical Analysis Of Measured Voltage Sags: Clusters And Rare Sagsmentioning

confidence: 99%

Initial Results of an Extensive, Long-Term Study of the Forecasting of Voltage Sags

Santis

Stasio

Noce³

et al. 2021

Energies

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This paper presents the preliminary results of our research activity aimed at forecasting the number of voltage sags in distribution networks. The final goal of the research is to develop proper algorithms that the network operators could use to forecast how many voltage sags will occur at a given site. The availability of four years of measurements at Italian Medium Voltage (MV) networks allowed the statistical analyses of the sample voltage sags without performing model-based simulations of the electric systems in short-circuit conditions. The challenge we faced was to overcome the barrier of the extremely long measurement times that are considered mandatory to obtain a forecast with adequate confidence. The method we have presented uses the random variable time to next event to characterize the statistics of the voltage sags instead of the variable number of sags, which usually is expressed on an annual basis. The choice of this variable allows the use of a large data set, even if only a few years of measurements are available. The statistical characterization of the measured voltage sags by the variable time to next event requires preliminary data-conditioning steps, since the voltage sags that are measured can be divided in two main categories, i.e., rare voltage sags and clusters of voltage sags. Only the rare voltage sags meet the conditions of a Poisson process, and they can be used to forecast the performance that can be expected in the future. However, the clusters do not have the characteristics of memoryless events because they are sequential, time-dependent phenomena the occurrences of which are due to exogenic factors, such as rain, lightning strikes, wind, and other adverse weather conditions. In this paper, we show that filtering the clusters out from all the measured sags is crucial for making successful forecast. In addition, we show that a filter, equal for all of the nodes of the system, represents the origin of the most important critical aspects in the successive steps of the forecasting method. In the paper, we also provide a means of tracking the main problems that are encountered. The initial results encouraged the future development of new efficient techniques of filtering on a site-by-site basis to eliminate the clusters.

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Voltage sag frequency kernel density estimation method considering protection characteristics and fault distribution

Wang

Luo

Xiao

2019

Electric Power Systems Research

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Voltage sag estimation of real transmission systems for faults along the lines

Cited by 9 publications

References 10 publications

Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices

Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices

Initial Results of an Extensive, Long-Term Study of the Forecasting of Voltage Sags

Voltage sag frequency kernel density estimation method considering protection characteristics and fault distribution

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