Voltage Sags (Dips) Measured in Real Interconnected Systems: Methods and Tools to Detect their Origin, and to Forecast Future Performance

The availability of data measured in the field is one of the greatest advances in the last decades for electric power systems planning, management and control. The technological innovations of systems and devices for the measurement of a wide range of quantities allowed their installation at almost all levels of voltages, from distribution to transmission. Increasing number of proposals of new techniques are coming from researchers and system operators for facing the various problems of system operation relying on the availability of measured data in real systems. The data from the field enable data-driven approaches, which can be integrated into traditional model-based methods constructing the so-called digital twin of a system. It is a system digital model whose parameters and linkages are continuously changed and tuned in accordance with the measurements from the actual system in service. New and attractive possibilities for addressing the problems of planning, managing, and controlling the power systems are opening for researchers and system operators. The studies presented in this thesis used the voltage sags measured in the medium voltage (MV) regional systems of E-Distribuzione for four years. Two main problems were faced by data-driven approaches: the ascertaining the origin of the measured voltage sags, and the forecasting the voltage sags which will occur in the site of the system. The correct ascertaining of the origin of voltage sags is crucial in view of future economic regulation by the national energy Authorities. It gives to the system operator the correct indication if the measured sags were or not due to faults in his own network interconnected with other systems. The possibility of forecasting the future performance in terms of voltage sags per year was a challenge never dealt with in the literature for the absence for field data. The literature, till now, proposed methods, models, and tools to estimate the average performance of a system, derived from model-based approaches. Regarding the origin of the voltage sags measured at the MV busbars of the High Voltage/Medium Voltage (HV/MV) stations, this thesis analyses and compares two methods which use only the residual voltage, the time the voltage sags occurred, and their duration. The analysis was conducted also studying the effect of the presence of DG (Distributed Generation). The data from field, moreover, revealed that in some specific cases the sags caused by faults in the MV systems propagated to HV networks. This problem was also studied simulating a portion of a real system, which presents the interconnections between HV and MV network. Regarding the forecast of voltage sags, the thesis proposed two main methods which use at least three years of measurement. The common choice of these two methods is the selection of the random variable, different from the statistical variable used by all the methods in literature. The random variable used in this study is the time to next event, that is the time intercurred between each couple of sags, instead of the variable number of voltage sags. This choice allows a huge increase of the data sets with the positive consequence of reasonable measurements time to obtain a forecast with acceptable accuracy. The first method, based on Poisson model, is suitable for rare sags, that are the sags occurred with a time to next event of the dimension of hours. The second method, based on Gamma model, is suitable for all the voltage sags, comprehensive of sags occurred close each other as groups. The latter, named clusters, are typically due to exogenous causes from the power systems, like adverse climatic conditions or fires. Intermittent indices are also proposed introduced for an initial screening of the measured sags to focus if, and how many, clusters are present in the data base of the measured sags. Such analysis drives the successive steps of the statistical analyses for discriminating the adequacy of Poisson and Gamma models. The studies presented in this thesis are the subjects of the scientific papers listed below. [1] C. Noce, M. D. Santis, L. D. Stasio, P. Varilone and P. Verde, "Detecting the Origin of the Voltage Sags Measured in the Smart Grids," 2019 International Conference on Clean Electrical Power (ICCEP), July 2nd-4th 2019, pp. 129-135, doi: 10.1109/ICCEP.2019.8890121. [2] C. Noce, L. Di Stasio, P. Varilone, P. Verde and M. De Santis, "On the Forecast of the Voltage Sags: First Stages of Analysis on Real Systems," 2020 55th International Universities Power Engineering Conference (UPEC), 1st – 4th September 2020, pp. 1-6, doi: 10.1109/UPEC49904.2020.9209816. [3] De Santis, M.; Di Stasio, L.; Noce, C.; Verde, P.; Varilone, P. Initial Results of an Extensive, Long-Term Study of the Forecasting of Voltage Sags. Energies 2021, 14, 1264. https://doi.org/10.3390/en14051264. [4] Paola Verde, Pietro Varilone, Leonardo Di Stasio, Michele De Santis, Christian Noce, Previsione dei buchi di tensione: sfide aperte dalla regolazione. AEIT - Volume 107 - Numero 1/2 gennaio/febbraio 2021 - ISSN 1825-828X , pp. 46-53. [5] M. De Santis, L. Di Stasio, C. Noce, P. Verde and P. Varilone, "Indices of Intermittence to Improve the Forecasting of the Voltage Sags Measured in Real Systems," in IEEE Transactions on Power Delivery, doi: 10.1109/TPWRD.2021.3082280. [6] Leonardo Di Stasio, Paola Verde, Pietro Varilone, Michele De Santis, Christian Noce, “Stochastic Model to Forecast the Voltage Sags in Real Power Systems”, AEIT International Conference, October 4th – 8th 2021. [7] G. M. Casolino, L. Di Stasio, P. Varilone, P. Verde, C. Noce, M. De Santis, “On the Forecast of the Voltage Sags Using the Measurements in Real Power Systems”, Accepted for the Conference ICHQP2022, Naples (IT), May 29th- June 1st 2022.