Towards analysing risks to public safety from wind turbines

Brouwer, Sander R.; Al-Jibouri, Saad H.S.; Cárdenas, Ibsen Chivatá; Halman, Johannes I.M.

doi:10.1016/j.ress.2018.07.010

Cited by 21 publications

(12 citation statements)

References 25 publications

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“…as power system quality and reliability, system protection, and power flow control [3]. Moreover, compared to other electricity generation systems, wind turbines (WTs) have relatively higher failure rates because of the harsher operation conditions and higher maintenance costs due to their relative inaccessibility [5].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

et al. 2020

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A wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW onshore wind turbine. INDEX TERMS Reliability engineering, failure analysis, risk analysis, fault detection, wind energy.

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Section: Introductionmentioning

confidence: 99%

Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

et al. 2020

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“…Second, safety of technicians on site as well as of individuals is increased. Damage types such as breaking of components of the blade, partial blade breaks, and even loss of a blade can be prevented if detected at an early stage [ 7 ]. Third, monitoring in operation can be used to optimise turbine settings and to examine blade behaviour.…”

Section: Introductionmentioning

confidence: 99%

Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes

Loss

Bergmann

2020

Sensors

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Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in operation is needed to optimise turbine settings and, consequently, to reduce alternate bending. In our approach, an acceleration model was used to analyse periodically occurring deviations from uniform bending. By using hierarchical clustering, significant bending patterns could be extracted and patterns were analysed with regard to reference data. In a simulation of alternate bending effects, various effects were successfully represented by different bending patterns. A real data experiment with accelerometers mounted at the blade tip of turbine blades demonstrated a clear relation between the rotation frequency and the resulting bending patterns. Additionally, the markedness of bending shapes could be used to assess the amount of alternate bending of the blade in both simulations and experiment.s The results demonstrate that model-based bending shapes provide a strong indication for alternate bending and, consequently, can be used to optimise turbine settings.

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“…In this field, multiple data driven techniques and methods have been developed. These methods are based on risk analysis [18], correlation of faults and weather conditions [19], the analysis of the statistical uncertainty of component reliability [20] or dirt and mud detection [21]. Other studies aim at developing maintenance strategies through opportunistic condition based maintenance [22], or considering production and deterioration uncertainties [23].…”

Section: Introductionmentioning

confidence: 99%

“…It has been employed in this paper the Sugeno-type, where the Sugeno has an output membership function linear or constant.Defuzzification is the process of producing a quantifiable result given fuzzy sets and the corresponding membership degrees. There are many types of defuzzification methods, where in this paper has been choose the centroid technique[18][87].…”

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confidence: 99%

Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

Marugán

Chacón

Márquez

2019

Reliability Engineering & System Safety

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Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks.

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Towards analysing risks to public safety from wind turbines

Cited by 21 publications

References 25 publications

Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes

Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

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