An Integrated Production/Maintenance Optimization Planning of a Stand-alone Wind Energy System for Rural Electrification

The renewable energy industry has gained so much attention due to the global importance attached to it. However, these sources are volatile in nature, hence, it is important to properly plan the production system to ensure continuity. This work focused on production and maintenance of wind energy system as a stand-alone system for rural electrification. The methodology for power production forecast in this work is optimization using machine learning technique; support vector regression (SVR) and estimation from theoretical technique. The production optimization is aimed to determine the optimal number of panels and batteries required to satisfy the random demand at minimal cost. In order to improve the system functionality and minimize failure, an integrated preventive maintenance model was developed to determine the optimal number of maintenances to be performed. Thus, scheduling optimal time to perform the preventive maintenance. The maintenance model is integrated with the power production rate to determine the maintenance cost. A numerical simulation was presented in order to test the developed algorithm using a case study in Katsina, Nigeria