Nowadays, the utilization of renewable energy sources like wind energy is considered one of the most effective means of generating massive amounts of electricity. This is evident in the rapid increase of wind farms all over the world which comprise a huge number of wind turbines. However, the drawback of utilizing wind turbines is that it requires maintenance, which could be a costly operation. To keep the wind turbines in pristine condition so as to reduce downtime, the implementation of CMS (Condition Monitoring System) and FDS (Fault Detection System) is mandatory. The efficiency and accuracy of these systems are crucial in deciding when to carry out a maintenance process. In this paper, a fault detection system based on intelligent clustering method is proposed. Using SCADA data, the clustering model was trained and evaluated for its accuracy through rigorous simulations. Results show that the proposed approach is able to accurately detect the deteriorating condition of a wind turbine as it nears a downtime period.