Optimal Redundancy and Maintenance Strategy Decisions for Offshore Wind Power Converters

Shafiee, Mahmood; Patriksson, Michael; Strömberg, Ann-Brith; Tjernberg, Lina Bertling

doi:10.1142/s0218539315500151

Cited by 9 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the previously mentioned concerns with non-parametric models, the need for parametric models of WT shows its importance when a study about optimization or performance and maintenance evaluation is demanded. 32–35 For instance, when using a non-parametric model, optimization of performance of a WT (from power generation point of view) would require evolutionary algorithms where they add to the uncertainty and complexity of the work (let alone not having an optimal solution). 36 A parametric model which is able to show health condition, degradation or reliability of the WT with regard to its various sub-assemblies would answer significant questions on performance monitoring and planning objectives.…”

Section: Introductionmentioning

confidence: 99%

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

Mazidi

Tjernberg

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

show abstract

Section: Introductionmentioning

confidence: 99%

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

Mazidi

Tjernberg

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…At this point, it needs to be highlighted that redesigning of the system architecture apart from redesigning of the system components includes also the suggestion of redundant structure for critical components and the implementation of more and innovated condition monitoring systems. Especially designing parallel connected critical subsystems and introducing condition monitoring systems play an increasingly important role in the new WT models …”

Section: Proposed Maintenance Interventionsmentioning

confidence: 99%

“…Especially designing parallel connected critical subsystems and introducing condition monitoring systems play an increasingly important role in the new WT models. [45][46][47][48][49] The final clustered DSM (Figure 13) consists of clusters that already belong to one or even two main subsystems (except the last cluster that consists of systems that belong to four subsystems). The systems that belong to the first cluster (25,29,32,28,26,37,33,36)-which also belong to Electrical and Control Systems-interact with almost all the rest of the systems.…”

Section: Proposed Maintenance Interventionsmentioning

confidence: 99%

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

2019

View full text Add to dashboard Cite

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility.In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the "readiness to fail" of a subsystem in conjunction with the "failure riskiness" can determine the "failure criticality." The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design-out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system. KEYWORDS design structure matrix, failures, fault tree analysis, safety impact, wind turbines | INTRODUCTIONWind is a clean and inexhaustible energy source and has become an important factor in the field of sustainable development. The global wind energy potential was estimated to be 72 TW, sufficient to supply the entire world's energy demand for all purposes. 1 Wind turbines (WTs) are installed either onshore (on land) or offshore (beyond the coast). Offshore wind farms seem to be advantageous because of the enormous energy potential associated with the large continuous areas and the stronger winds that imply greater power generation, even though they have higher investment, operation, and maintenance costs. The size of WTs is of great significance as a bigger WT produces more power in a more efficient way-resulting in greener produced electricity. 2The issue of WTs' failures is of particular interest. Failure is the loss of the ability of an item to perform a required function. 4 In WT, the unusually tough ambient conditions and the extremely high dynamic loading of the components often result to failures. These two factors are carefully considered in the design phase and materials' selection of a WT. Common causes of WTs failure can be components failure, high wind, waves,

show abstract

“…Amongst the state of the art research, some authors focus on methods for the signal analysis, mathematical models or an ensemble of statistical methods sequentially connected. Authors such as Shafiee et al [13] develop methods to calculate the number of redundant converters and to determine the number of failures needed before reporting a maintenance task in the case of turbines offshore, located in hard-to-reach places. On the other hand, Hameed et al [14] apply transformations for spectral analysis with the aim of detecting deviations before failures.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Algorithms for Wind Turbine Failure Prediction

Martí-Puig

Blanco-M

Cárdenas³

et al. 2019

Energies

View full text Add to dashboard Cite

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.

show abstract

Optimal Redundancy and Maintenance Strategy Decisions for Offshore Wind Power Converters

Cited by 9 publications

References 33 publications

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

Feature Selection Algorithms for Wind Turbine Failure Prediction

Contact Info

Product

Resources

About