Early detection of faults and stall effects associated to wind farms
Introduction
In the last decade, the wind turbine generator technology has been spreading around the world, therefore, it has the cheapest energy cost, the new wind farms are growing in a rate of 6.3% [8] although, the energy companies has still difficulties in the maintenance of the wind technology [2]. Other important aspect is the repowering in large power plants, it is required in countries as Brazil, therefore, it has constraints as time and energy restrictions [3]. In large scale, the WTGs are designed with reliability criterion in wind farms, equipment similar near to 3 MW per WTG and 50 to 150 WTGs, in each wind farms. In this context, the maintenance team should evaluate three challenges: Logistics and the supply change associated to the transportation through countries [4], energy conversion systems associated to the technology and modern control systems [5] and finally, the condition monitoring and fault detection, associated to large power plants, in order to provide the energy required in the power purchase agreement with the local government [6].
Since 2020, several authors have investigated about the reliability of the wind farms and failures modes, however, experimental studies should incorporate to validate the new perspectives, in order to solve real problems with solutions associated to maximize the production.
In 2021, associated to the winter season, several wind farms in USA has been affected with ice condition, and faults associated to yaw misalignment caused by ice with and without heating system [18]. Therefore, the early detection of faults is a priority, in order to normalized the production of the WTGs and reduce the lost production caused by ice [19]. The research article question is the following: Is possible to detect in a early condition faults by yaw misalignment and ice influence in the wind turbine generator with data from production? The research article is organized as follows: In Section “Description and model developmentDescription and model development” description of the blade and yaw alignment is done with a brief systematic review and mathematical modeling of the system is presented; therefore, in Section “Case study” a case study in a real wind farm with thirteen WTGs, associated to active power vs wind turbine generators and wind direction vs yaw angle analysis. Later, the Section “Conclusions” is a discussion about the main results and contribution to the failure analysis theory; finally, the last section has conclusions.
Section snippets
Systematic review
This research article has been analyzed with the PICO methodology, the algorithm implemented for the reference evaluation has detected 79 research articles, in December 12th, 2020, as follows: ((Wind farm) OR (Wind turbine)) AND ((Yaw) OR(pitch)) AND(failure) NOT((storage) OR(structural) OR(atmospheric) OR(horizontal)). The main contribution is the following.
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The component investigated in the last year is the bearing, however “the root causes of premature failure of bearings are still much less
Distribution of the wind turbine generator
The case the study is for thirteen WTGs, with a location in the north of Perú, in the Fig. 9. About the WTG a good indicator, as a first approach, it is the between neighborhood with WTGs. In this case study, The worst found between V11 and V12 (in the Fig. 10), it could be an failure in the anemometer and yaw systems, it is due to lowest R2 and correlation with wind velocity average each ten minutes in Fig. 12; usually the average is among 0.55 and 0.89. Furthermore, an interesting case
Conclusions
A novel evaluation of the failure in wind farms with lower efficiency than manufacturer design. The mathematical model for the analysis of the power and wind curve is considered with a complete evaluation in a wind farm of thirteen wind turbine generators. The main root cause failure is the blade problems, mainly associated to contamination and Yaw angle defects. The analysis allows to compare online the information in all the wind farm. It has been experimentally validated with a wind farm.
List of variables
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V is the wind speed average.
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X is the mechanical state of the turbine.
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= p probability of availability of WTG.
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P(wt) is the power produced in the WTG according the wind velocity, through the time and performance of the WTG.
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F(v) is the wind velocity function.
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v is the wind velocity in a specific time.
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T is the time, with T > 0.
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b turbine constant.
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is a limit wind velocity, for an specific analysis.
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vr is the rated wind speed.
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wind velocity range though a stage and position, with wTG
CRediT authorship contribution statement
Ricardo Manuel Arias Velásquez: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Writing - review & editing, Supervision, Project administration. Freddy Antonio Ochoa Tataje: Validation, Formal analysis, Investigation, Data curation, Visualization, Writing - review & editing. María del Carmen Emilia Ancaya-Martínez: Validation, Formal analysis, Investigation, Data curation, Visualization, Writing - review &
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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