The forecast of the occurrence of voltage sags at the sites of a system is nowadays feasible thanks to the availability of huge quantity of recorded data. To forecast future performance from the statistical analysis of recorded sags, the stochastic modelling of the voltage sags is required since the events are not statistically time independent. The presence of groups of sags, named clusters, brings the phenomenon far from the conditions of Poisson model. This paper proposes the Gamma distribution to model the sags, which also include the clusters. Different techniques for assessing the parameters of the Gamma distribution are presented and applied to forecast the number of sags expected at selected sites in the year 2018, i.e., the year successive to those when the sags were measured. The outcomes of the forecast are compared with the sags effectively occurred in those sites in the year 2018, using different criteria for evaluating the forecast error. The results showed the viability of the approach and encourage further studies to improve the accuracy and extend the forecast to entire systems.
Stochastic Model to Forecast the Voltage Sags in Real Power Systems
Di Stasio L.;Varilone P.;Verde P.
2021-01-01
Abstract
The forecast of the occurrence of voltage sags at the sites of a system is nowadays feasible thanks to the availability of huge quantity of recorded data. To forecast future performance from the statistical analysis of recorded sags, the stochastic modelling of the voltage sags is required since the events are not statistically time independent. The presence of groups of sags, named clusters, brings the phenomenon far from the conditions of Poisson model. This paper proposes the Gamma distribution to model the sags, which also include the clusters. Different techniques for assessing the parameters of the Gamma distribution are presented and applied to forecast the number of sags expected at selected sites in the year 2018, i.e., the year successive to those when the sags were measured. The outcomes of the forecast are compared with the sags effectively occurred in those sites in the year 2018, using different criteria for evaluating the forecast error. The results showed the viability of the approach and encourage further studies to improve the accuracy and extend the forecast to entire systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
