Stochastic Characterization of Faults in Electrical Transmission Networks: Case Study of the Electrical Community of Benin

Abstract Electrical energy is a key factor in the development of any nation. Demand has been rising in recent years. This is putting existing power networks in difficulty, as they have not anticipated this meteoric rise. Blackouts occur daily and hurt the socio-economic development of nations, especially the most vulnerable. A predictive network outage solution would contribute effectively to better transmission network planning. Unfortunately, outage data for conventional networks are based solely on dispatcher reports and are difficult to exploit. This is the case for the Electrical Community of Benin transmission network. Understanding the predictive nature of this data would help implement fault prediction algorithms in this network. This paper aims to model outages and production in the Electrical Community of Benin power grid using probability laws. The objective is to contribute to the security of the electricity transmission network. Predicting the number of outages, their duration and the overall power lost will allow dispatchers to adjust electrical energy sources to avoid blackouts and save on electrical energy to impact the cost of producing electrical energy. The Kolmogorov Smirnov test, the error estimation using Akaike's information criterion and Bayesian information criterion on the one hand, and the Chi-2 test and the error estimation using the Root Mean Square Error on the other, were used to fit Benin Electrical Community network outages and accumulated sources using Weibull's law, outage duration using Erlang's law and energy lost using the Exponential law.