On the Forecast of the Voltage Sags: First Stages of Analysis on Real Systems

Noce, Christian; Stasio, Leonardo Di; Varilone, P.; Verde, P.; Santis, Michele De

doi:10.1109/upec49904.2020.9209816

Cited by 10 publications

(20 citation statements)

References 11 publications

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“…In this paper, the same regional system presented in [25] was considered. The system has 83 HV/MV substations with 152 MV busbars that represent the points of measurement of the voltage sags (we discarded three sites of the network since they registered in all the considered years a number of voltage sags less than 50; therefore, we considered 149 sites).…”

Section: Main Characteristics Of the Measured Data And Time Aggregation For Multiple Sagsmentioning

confidence: 99%

“…In this paper, which is an extended version of an earlier paper [25], we presented the preliminary results of the study, indicating, with more details than were presented in [25], the main critical aspects encountered and some solutions to overcome them.…”

Section: Introductionmentioning

confidence: 95%

“…This study was conducted without performing any simulations, and only the available data were used to conduct the statistical analyses. This paper is an extended version of our earlier paper [25], in which the results of the preliminary studies were presented. These preliminary studies were focused mainly on the evaluation of the statistical techniques that were used to assess the input data that came from the field.…”

Section: Introductionmentioning

confidence: 99%

“…The overriding goal of this study is to give to the network operators proper algorithms that will allow them to forecast how many voltage sags will occur and the sites at which they are likely to occur. Also, this extended version of the paper [25] allows identifying the details of the main critical aspects that are encountered, and it proposes adequate solutions, which are in progress. In this paper, we addressed the subject forecasting the performance of a real system by using the measurements obtained by the MSs installed at the HV/MV substations within an Italian Region due to the regulation of ARERA.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Section: Main Characteristics Of the Measured Data And Time Aggregation For Multiple Sagsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations