Applying Swarm Intelligence and Bayesian Inference for Wind Turbine SCADA-Based Condition Monitoring and Prognostics



Xiang Ye Lisa Osadciw


Diagnosis and prognosis of potential faults is crucial to maintain and improve the efficiency of the wind energy system. In this paper, we propose a SCADA-based condition monitoring and prognostics system. We apply particle swarm optimization to recognize different patterns of turbine health condition by fusing performance test results. As monitoring daily turbine health condition, we design a data-driven Bayesian inference approach to predict turbine potential failures by tracking the abnormal variations.

How to Cite

Ye, X. ., & Osadciw, L. . (2014). Applying Swarm Intelligence and Bayesian Inference for Wind Turbine SCADA-Based Condition Monitoring and Prognostics. Annual Conference of the PHM Society, 6(1).
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wind energy, Bayesian inference, Fault Diagnosis and Prognosis, data-driven method, PSO

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