Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices
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 sag...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10521533/ |
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| author | G. M. Casolino M. de Santis L. Di Stasio C. Noce P. Varilone P. Verde |
| author_facet | G. M. Casolino M. de Santis L. Di Stasio C. Noce P. Varilone P. Verde |
| author_sort | G. M. Casolino |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4a40f499b0bd458f93150fc8256a4e7c |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-4a40f499b0bd458f93150fc8256a4e7c2025-01-21T00:03:01ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011123124010.1109/OAJPE.2024.339736510521533Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence IndicesG. M. Casolino0https://orcid.org/0000-0003-2309-8584M. de Santis1https://orcid.org/0000-0002-4058-1722L. Di Stasio2C. Noce3P. Varilone4https://orcid.org/0000-0002-0938-9257P. Verde5https://orcid.org/0000-0001-5932-9319Dipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano,” Università degli Studi di Cassino e del Lazio Meridionale, Cassino, ItalyDipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Aversa, ItalyENEL Group, Rome, ItalyENEL Group, Rome, ItalyDipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano,” Università degli Studi di Cassino e del Lazio Meridionale, Cassino, ItalyDipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano,” Università degli Studi di Cassino e del Lazio Meridionale, Cassino, ItalyThe 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.https://ieeexplore.ieee.org/document/10521533/Voltage sagforecastingPoisson processGamma distribution |
| spellingShingle | G. M. Casolino M. de Santis L. Di Stasio C. Noce P. Varilone P. Verde Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices IEEE Open Access Journal of Power and Energy Voltage sag forecasting Poisson process Gamma distribution |
| title | Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices |
| title_full | Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices |
| title_fullStr | Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices |
| title_full_unstemmed | Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices |
| title_short | Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices |
| title_sort | measured rare voltage sags and clusters of sags prediction models driven by the intermittence indices |
| topic | Voltage sag forecasting Poisson process Gamma distribution |
| url | https://ieeexplore.ieee.org/document/10521533/ |
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