On the Forecast of the Voltage Sags Using the Measurements in Real Power Systems

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

“…This feature was first evidenced in [16]. This constitutes a crucial aspect that influences the statistical modeling of measured voltage sags and their practical application in forecasting sags at every site within an entire electrical system, as demonstrated in [17][18][19][20][21]. Rare voltage sags can be modeled effectively as a Poisson process with a constant and positive rate of occurrence owing to their features of timeinvariance, memory-independence, and independence from each other.…”

“…For example, a strong correlation of sag clusters with significant lightning activity in some areas of the system may suggest an improvement in the performance of the lines related to lightning in those areas. In all the papers [16][17][18][19][20][21], the statistics of the measured sag were modeled by the random variable tim to th n xt v nt of th sit , ttn , rather than the numb r of sags, N. The random variable ttn , which measures the time between successive voltage sags at each site denoted by , provided a significantly larger database compared to N. This allowed forecasting rare sags with errors lower than 10% using the sags measured in only 3 years with any frequency.…”

“…Given that clusters continue to play a crucial role in representing the severity of voltage sags at various nodes within the power system, in successive papers [20,21], the forecast of all the sags (named total sags), that is rare sags and clusters of sags, was first approached. Considering the set of all the measured sags as total sags, they were modeled as a stochastic process in which the rate of sag changes over time.…”

“…In [20], the prediction of total voltage sags was limited to a few specific locations, whereas in [21], this forecasting was expanded to encompass an entire regional system. The forecast errors obtained in [20,21] were not completely satisfactory.…”

“…In [20], the prediction of total voltage sags was limited to a few specific locations, whereas in [21], this forecasting was expanded to encompass an entire regional system. The forecast errors obtained in [20,21] were not completely satisfactory. The main reason for these errors is related to having assumed that all measured sags were total sags, which were all together modelled as a stochastic process without any distinction between the two types of sags.…”