Failure Probability Estimation of Wind Turbines by Enhanced Monte Carlo Method

Sichani, Mahdi Teimouri; Nielsen, Søren R.K.; Næss, Arvid

doi:10.1061/(asce)em.1943-7889.0000334

Cited by 11 publications

(14 citation statements)

References 20 publications

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“…At the mean wind speed of 17 m/s, the kurtosis of Gaussian wind load is larger than that of hardening and softening wind loads due to the influence of the control system, etc., so the short-term It is noted that extreme values estimated by the ACER method are generally not sensitive to the selection of the initial threshold, but for strongly non-Gaussian cases, trail data have a great influence on extreme evaluation. Sichani [35] suggested that the initial threshold should be as small as possible for obtaining more sample data.…”

Section: The Average Conditional Exceedance Rate Methodsmentioning

confidence: 99%

“…It is noted that extreme values estimated by the ACER method are generally not sensitive to the selection of the initial threshold, but for strongly non‐Gaussian cases, trail data have a great influence on extreme evaluation. Sichani suggested that the initial threshold should be as small as possible for obtaining more sample data.…”

Section: Extreme Reliability Analysismentioning

confidence: 99%

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Reliability analysis of wind turbines under non‐Gaussian wind load

Shuang

Song

2017

Structural Design Tall Build

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Summary Based on translation models, both Gaussian and non‐Gaussian wind fields are generated using the harmony superposition method for examining the reliability of a typical wind turbine at operational and parked conditions. Using the blade aerodynamic model and multibody dynamics, wind turbine responses are calculated and then probability characteristics are analyzed in details. The short‐term extreme response distribution is estimated by the average conditional exceedance rate method at each mean wind speed bin, and the long‐term extreme response distribution is then determined by further integrating the short‐term extreme response distribution conditional on wind speed with the distribution of mean wind speed. Additionally, crack initiation life and crack propagation life are evaluated using the linear cumulative damage theory and linear crack propagation theory, respectively. The results indicate that non‐Gaussian characteristics of wind inflows have a noticeably greater influence on both extreme response and fatigue damage, and the Gaussian assumption cannot suit wind turbine in complex terrain.

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Section: The Average Conditional Exceedance Rate Methodsmentioning

confidence: 99%

Section: Extreme Reliability Analysismentioning

confidence: 99%

Reliability analysis of wind turbines under non‐Gaussian wind load

Shuang

Song

2017

Structural Design Tall Build

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“…Cheng (2002) proposed a reliability-based design extreme response method of offshore wind turbine tower. The stochastic simulation method employed must be very costly for practical applications and therefore improvements on Monte Carlo simulation were studied extensively (Sichani et al, 2012). Tarp-Johansen et al (2002) proposed a security coefficient determination method for extreme load of wind turbine tower.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reliability of Offshore Wind Turbine Tower Systems

Chen¹,

Liu²,

Li³

2014

Vulnerability, Uncertainty, and Risk

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Due to the aerodynamic effects on rotating blades and wave action on the tower, the offshore wind turbine tower system is actually a high-dimensional time-variant nonlinear system. With the increase of their size, the safety and serviceability of offshore wind turbines becomes increasingly important. Generally, to evaluate the global reliability of such systems involves high-dimensional nonlinear stochastic dynamics which is still a great challenge. In the present paper, a simplified integrated multi-degree-of-freedom model which characterizes the rotation of blades and the aerodynamics of blades is adopted. The probability density evolution method (PDEM) is then employed to evaluate the global reliability when the randomness of wind is involved. The example shows that it is feasible to design a complex structure exhibiting nonlinearity subjected to concurrent external dynamic excitations based on the global reliability.

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“…Reliability assessment of rotor blades with defect was investigated in Toft et al where the blades were modeled as series–parallel systems. Enhanced Monte Carlo method and Markov chain Monte Carlo method were also applied to estimate reliability of wind turbines, respectively. Reliability analysis of wind turbines subjected to electrical faults was carried out to evaluate the performance of an active tendon controller in Staino and Basu …”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of active tendon-controlled wind turbines by a computationally efficient wavelet-based probability density evolution method

Tao

Basu

2017

Struct Control Health Monit

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Summary We propose a computationally efficient stochastic framework and an associated mathematical formulation to assess the improvement in the vibration response of wind turbines with active structural controllers. Uncertainties in the turbulent wind speed as well as in the structural parameters are considered in the formulation for analyzing the 2 important (rated and cut‐out) wind speed conditions. A multimodal wind turbine model based on a NREL 5‐MW wind turbine model is adopted for analysis, and an active tendon controller is used to develop the stochastic framework. Dynamic interaction between the rotor blades and the supporting tower, as well as the coupling induced by the pretwist of the blades, and a collective pitch controller are considered. A time‐evolving phase spectrum method together with a phase delay spectrum model is used to simulate the stochastic wind field, which is computationally less expensive. The probability density contours and the extreme value distributions of the blade tip displacements with and without the active controller are obtained by the wavelet‐based probability density evolution method, followed by the computation of the corresponding failure probabilities. It is observed that the improvement in reliability and performance of the rotor blades with the controller becomes statistically more effective as the wind speed increases (e.g., significant beneficial effects are noticed from the probability distributions in terms of the spread at the cut‐out wind speed).

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Failure Probability Estimation of Wind Turbines by Enhanced Monte Carlo Method

Cited by 11 publications

References 20 publications

Reliability analysis of wind turbines under non‐Gaussian wind load

Reliability analysis of wind turbines under non‐Gaussian wind load

Dynamic Reliability of Offshore Wind Turbine Tower Systems

Reliability analysis of active tendon-controlled wind turbines by a computationally efficient wavelet-based probability density evolution method

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