Computational sensor nodes optimization for smart anomaly detection applied to wind energy

Abstract

The increasing use of wind energy generators in mountainous areas is an important necessity in establishing and enhancing renewable energy. This procedure has created a need for reliable and efficient methods for detecting anomalies remotely basing on computational sensor nodes optimization.These methods are useful for detecting deviations in the generator's behavior that may indicate a potential failure, allowing for prompt maintenance, minimizing the intervention costs, and ultimately reducing downtime and improving the generator's overall reliability compared to previous techniques. In this context, this paper presents the principal smart anomaly detection methods applied to wind energy generators. It covers different techniques, including machine learning algorithms, statistical approaches, and deep learning models. Furthermore, this paper aims to perform the optimization of computational sensor nodes (CSN) as new strategy, instead of basic wireless sensor network, in smart fault detection. This paradigm is growing very rapidly in varied fields. One of the promising application fields of CSN is wind energy generators, where this technology offers important support. This technology will allow for precise resource management. The experimental results show that the proposed approach can effectively detect anomalies in wind turbine data, and it outperforms traditional statistical methods with 99 % recognition rate against 93 % in the best way..