Abstract
Based on the characteristic that pitch system faults of wind turbines will lead to the change of system parameters, the observer-based multi-innovation stochastic gradient algorithm as a fault diagnosis method is proposed in this paper. The multi-innovation identification algorithm can improve the parameter estimation accuracy by extending the innovation length. According to the observer canonical state space system model, the algorithm that combines the multi-innovation stochastic gradient algorithm with the state observer can obtain the interactive estimation between system states and system parameters. Firstly, the pitch system model is transformed into an identification model by converting into a canonical state space model. Then, the algorithm proposed is adopted to estimate system states and system parameters. The fault diagnosis problem is transformed into a parameter estimation issue. At the end, pitch system faults could be diagnosed through the variation of system parameters. The simulation results show that the proposed method is able to diagnose the pitch system faults effectively.
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Wu, D., Liu, W., Zhai, Y., Shen, Y. (2016). Fault Diagnosis for the Pitch System of Wind Turbines Using the Observer-Based Multi-innovation Stochastic Gradient Algorithm. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_54
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DOI: https://doi.org/10.1007/978-981-10-2663-8_54
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