ANALYSE STATISTIQUE DE LIMPACT DES PANNES DU RESEAU ELECTRIQUE SUR LES SOURCES ET LA CHARGE: ETUDE DE CAS DU RESEAU ELECTRIQUE DE LA COMMUNAUTE ELECTRIQUE DU BENIN

Breakdowns in the electricity network contribute to economic losses and upset populations who are increasingly dependent on this source of energy in their daily lives. The Benin Electric Community (CEB) is in charge of the electricity transmission network for Togo and Benin. It is responsible for dispatching the national sources of electrical energy production in the two countries and the import sources to the distribution networks of the two nations. Minimizing breakdowns and blackouts of this network will make it possible to contribute effectively to the economic and technical development of the company on the one hand and the socio-political development of the two countries on the other. This study aims to analyze the impact of breakdowns of the CEB network on the socio-economic level of the two countries on the one hand and on the electrical quantities of the operation of the network (sources or loads) with the aim of proposing a digital failure prediction solution. To achieve this objective, an analysis based on descriptive statistics and statistical linear regression was used. The results showed that more than 55.68% of breakdowns are caused by distribution networks. Earth faults are the most represented among the failures inherent in network control and management systems. There is an impact of blackouts on the social level. The populations of Parakou in Benin are the most affected with cumulative service unavailability of 75 days 19 hours 52 minutes. The economic impacts on the CEB were also recorded. For the correlative study, it appears that there is a weak negative correlation between the number of triggers and the sum of the sources of the CEB. This indicates a possibility of predictive detection of network failures using data from CEB sources. In the rest of this work, a stochastic statistical analysis of the data would be made to determine a mathematical model in order to design the CEB network failure prediction tool.