Petri Net Model and Reliability Evaluation for Wind Turbine Hydraulic Variable Pitch Systems

Abstract

Abstract:Based on an analysis of the working principles of the hydraulic variable pitch system of a wind turbine, a novel Petri net model and reliability evaluation method are proposed. First, Petri net theory is adopted to build a model for each discrete state of the operation of the hydraulic pitch system of the wind turbine and at the same time a fault Petri net model is established. Then through qualitative analysis and quantitative calculations based on the fault Petri net, the system reliability indexes are obtained. During the qualitative analysis process, in order to more conveniently find the minimal cut sets of the fault Petri net, a Visual C++ 6.0-based algorithm is compiled and the minimal cut sets are tested correctly with another method. During the quantitative calculation process, the fault probability has been obtained from the equations according to the fault probability of libraries and transitions between different states. Not only does the proposed Petri net describe the structure, function and operation of the hydraulic pitch system with a graphic language, but the fault Petri net model can also clearly express the logical relations among faults. The novel Petri net model offers simple calculations and the prospect of broad applicability and the new reliability evaluation method provides an important reference for the performance evaluation of these systems.

Reliability based design of fluid power pitch systems for wind turbines

“…Yang et al . presented a method for determining reliability on the basis of a Stochastic Petri Net (SPN) of a 500 kW Vestas V39 turbine fluid power pitch system. The method mainly concerned the development of an algorithm for determining minimal cut sets of the SPN and not how the SPN is constructed itself.…”

Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms

“…In fact, the sensitivity analysis of power production in relation to the pitch angle and other parameters needs complex inference strategies, such as ANFIS (Adaptive Neuro-Fuzzy Inference System), as is shown in the recent literature [25][26][27][28]. There are also data-driven approaches to detect or somehow balance pitch angle faults alone [29,30] or in combination with other parameters [31,32].…”

Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review

“…Identifying the ways that a WT can fail is the first stage of the risk management process which will constitute the base of work on future life extension processes, making it a feasible practice. Premature brake activation [21] Unsteady performance [28] Increased torque pulsation [18,31] Inability of excessive operational load mitigation [35] Corrosion [28] Excessive heating in the winding [18] Operation instability due to hydraulic system failure [36] Gearbox (bearings and gears) Ref Increase in losses and efficiency reduction [31] Air contamination in the hydraulic system [37] Gear tooth damage [38] Rotor misalignment [39] Inability of aerodynamic braking [35] Pitting [38] Imbalances and harmonics in the air gap flux [18,40] Hydraulic fluid bulk modulus reduction [37] Cracking [38] Shorted winding coil (reduction in generator reactance) [29] Leakage in the hydraulic system [37] Gear eccentricity [29] Tower and Foundation Ref …”

“…Certain faults of hydraulic systems produce operational instability [36], however, premature brake activation [21] also compromises their intended operational mode. Other examples of pitch control system failures are: the reduction of the hydraulic fluid effective bulk modulus produced by air contamination of the hydraulic system, reduction of plant bandwidth and significant leakage in the hydraulic system [37].…”