Comparing the Monte Carlo and analytical methods to assess the risks of voltage sags disruptions

Customers are undoubtedly demanding a better power quality to be provided by electric power distribution companies. Many aspects have contributed to that, such as customer protection laws and regulatory issues. Furthermore there is relatively poor knowledge concerning load sensitivity in customer equipment, which used to be predominantly electromechanical and have become mostly electronically controlled. Load characteristics have been changing in all types of customers. Industrial customers seek for higher efficiency and productivity; commercial customers seek for more sophistication and services; whereas residential customers are increasingly using more technology for their own comfort. Electronic devices, used in these customers, are generally more sensitive to disturbances in the power system and, at the same time, are the ones that also generate disturbances, such as harmonic distorted currents. Claiming for damages in customer equipment due to disturbances in the electrical network is an issue that have been gaining greater attention. In particular, short duration voltage variations (SDVV), namely voltage sags and swells, are phenomena that have been taking into account in such claims. In such a context, this paper aims at developing models and tools to study SDVV in electrical power distribution systems, since a number of publications have dealt with vulnerability areas in transmission networks [1], [6]. This paper considers two different methodologies to address the computation of SDVV indices as well as long duration interruptions based on short circuit analysis [5], [2], [3] and system failure rates. The first methodology is based on the Monte Carlo method, in which a statistical analysis of SDVV occurrences that originate in the distributed network is carried out. The second methodology is analytical, since it computes a combination of all short-circuit types, in all network busses, for a range of fault impedances and natural extinction times in order to obtain a representative profile of the network occurrences.