Health monitoring of wind turbine: data-based approaches

Health monitoring of wind turbine: data-based approaches

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This chapter presented a robust data-driven fault detection scheme with the application to a wind turbine benchmark. The proposed scheme is based on robust residual generators constructed directly from available process measurements. For this purpose, a parity space is first identified from the measured data, and optimal parity vectors are selected from the parity space according to a given performance index and an optimization criterion to generate a robust residual vector. A proper evaluation approach as well as a suitable decision logic is further given to make a correct final decision. The effectiveness of the proposed scheme is finally demonstrated by the results obtained from the simulation of a wind turbine benchmark model.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 Benchmark system and faults description
  • 7.2.1 Benchmark model
  • 7.2.2 Fault scenarios
  • 7.3 Robust data-driven fault detection design
  • 7.3.1 Identify parity space directly from measured data
  • 7.3.2 Select optimal parity vector from parity space
  • 7.3.3 Construct robust residual generators
  • 7.3.4 A designed robust fault detection scheme
  • 7.4 Benchmark simulation
  • 7.5 Conclusions
  • References

Inspec keywords: fault diagnosis; wind turbines; health and safety; optimisation

Other keywords: suitable decision logic; health monitoring; robust residual vector; data-based approaches; robust residual generators; robust data-driven fault detection scheme; correct final decision; parity space; wind turbine benchmark model

Subjects: Optimisation techniques; Plant engineering, maintenance and safety; Wind power plants

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