Maximization of the Power Production of an Offshore Wind Farm

Balakrishnan, Raj Kiran; Hur, Sung-ho

doi:10.3390/app12084013

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…[11] ILP Obtains the optimal solution of the problem. [13] PSO Does not guarantee finding the optimal solution. [23,24] MILP Gives the optimal solution to the problem.…”

Section: Aocspmentioning

confidence: 99%

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Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

et al. 2023

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The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

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“…[11] ILP Obtains the optimal solution of the problem. [13] PSO Does not guarantee finding the optimal solution. [23,24] MILP Gives the optimal solution to the problem.…”

Section: Aocspmentioning

confidence: 99%

“…Balakrishnan et al [13] stated that wind energy production depends on local conditions and the interference caused by nearby WTs. They used active wake control methods such as wake redirection control (WRC) and axial induction control (AIC) to reduce the wake effect and maximise power production in offshore wind farms.…”

Section: Introductionmentioning

confidence: 99%

Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

et al. 2023

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“…Consequently, compared to individual structures, the transmission chains of wind turbines inherently require higher reliability assurance [8]. Wind turbine assembly facilities are typically located in remote mountainous and offshore regions abundant in wind energy resources to ensure stable wind farm operations [9]. However, challenges arise in the daily monitoring and maintenance of wind turbines due to factors like installation, transportation, and environmental conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

Zhang,

Ruan,

Huang

et al. 2023

Meas. Sci. Technol.

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With the rapid development of wind power generation, ensuring the reliability and fault diagnosis of wind turbine transmission chains has become a focal point. Traditional clustering methods that address the fault diagnosis problem in wind turbine transmission chains suffer from low fault discrimination and accuracy, as they rely on a single indicator. This paper proposes a two-dimensional clustering model that utilizes multiple indicators to address the issue of distinguishing similar faults within the wind turbine transmission chain from various perspectives and dimensions. The proposed approach involves collecting vibration signals from the transmission chain of wind turbines using sensors and using the root mean square and kurtosis of the fault signal as clustering features. A multidimensional polar coordinate clustering model is established, with the signal kurtosis representing the polar angle and the root mean square representing the polar radius. The clustering of fault signals within wind turbine transmission chains is achieved by optimizing the clustering boundaries by establishing a clustering boundary optimization equation. The results of the developed clustering model are evaluated using external and internal evaluation methods. Experimental results demonstrate that this method exhibits high accuracy and low computational complexity in diagnosing faults within wind turbine transmission chains. In comparison with other clustering methods, the proposed method outperforms them according to the experimental results. Hence, this study presents a novel approach for health monitoring and fault diagnosis of the transmission chain of wind turbines, which has significant implications for improving the reliability of wind turbine operation and reducing maintenance costs.

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