Optimal number of DFIG wind turbines in farm using Pareto genetic algorithm to minimize cost and turbines fault effect

Khattara, A.; Becherif, Mohamed; Ayad, M. Y.; Bahri, M.; Aboubou, A.

doi:10.1109/iecon.2013.6699997

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…With the use of control algorithms [63], generating units can be simplified in order to reduce maintenance costs. Khattara et al [64] developed a mathematical approach and a program of a Pareto genetic algorithm to calculate the cost of installation and minimal maintenance of a wind farm with minimal power loss during a fault.…”

Section: Condition-monitoring System (Cm)mentioning

confidence: 99%

New Tendencies in Wind Energy Operation and Maintenance

et al. 2021

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Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.

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Section: Condition-monitoring System (Cm)mentioning

confidence: 99%

New Tendencies in Wind Energy Operation and Maintenance

et al. 2021

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A Comprehensive Review of Artificial Intelligence and Wind Energy

Márquez

Gonzalo

2021

Arch Computat Methods Eng

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Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

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Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers

Soued

Ebrahim

Ramadan

et al. 2017

IET Electric Power Applications

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Wind energy (WE) as an environment‐friendly resource is green, clean, and innovative solution for the globe energy dilemma. For the blade pitch control (BPC) of the WE, the gain scheduling of the BPC becomes important to cope with the wind intermittency. The innovative idea of this study is to employ the novel advanced MOTs for enhancing the dynamic behaviour of the wind power plant. For this purpose, the genetic algorithm (GA), artificial bee colony (ABC) algorithm and grey Wolf optimiser (GWO) are used for the optimal tuning of the BPC system parameters. A comprehensive comparative study is presented to verify the effectiveness of the proposed MOTs over different conventional methods such as the conventional Zeigler–Nichols, and the simplex algorithm). This comparative analysis is based on the time response specifications such as maximum overshoot, settling time and steady‐state error. The proportional‐integral‐differential (PID) controller is applied to the multivariable BPC for a wind turbine generating system connected to a large power system. The simulation results show that the GWO‐PID is more effective than the ABC, GA and the conventional methods. Moreover,the GWO‐PID controller robustness is verified in the presence of system parameter variations, an abrupt change in the mechanical torque and the wind speed variation.

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Optimal number of DFIG wind turbines in farm using Pareto genetic algorithm to minimize cost and turbines fault effect

Cited by 3 publications

References 16 publications

New Tendencies in Wind Energy Operation and Maintenance

New Tendencies in Wind Energy Operation and Maintenance

A Comprehensive Review of Artificial Intelligence and Wind Energy

Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers

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