<![CDATA[Wind Power Plants (WPP) have great potential as an environmentally friendly renewable energy source. However, in its operations, WPP faces a number of challenges, especially related to monitoring, detection, and optimizing system performance. This research aims to implement an intelligent system that combines internet of things (IoT), machine learning (ML), and artificial intelligence (AI) technology to improve the performance of WPP. The methodology used includes real-time monitoring with the help of sensors, as well as the application of machine learning to detect faults early and monitor energy production transmission. Through a systematic literature review approach, this study examines various relevant literature in the development of smart systems for WPP. The results show that the use of IoT and machine learning can increase monitoring efficiency by up to 95%, with damage prediction accuracy reaching 99.24%, and energy efficiency improvement by 87%. Maximum Power Point Tracking (MPPT) and Fuzzy Logic Controller (FLC) technology also play a role in optimizing energy conservation. In conclusion, the application of smart systems in WPP not only improves operational efficiency and accelerates damage detection, but also supports the transition to more sustainable renewable energy. This study recommends further development of this technology, in order to strengthen the stability and dryness of energy supply.]]>
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