scholarly journals Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dongmei Zhang ◽  
Jun Yuan ◽  
Jiang Zhu ◽  
Qingchang Ji ◽  
Xintong Zhang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Wind Turbine ◽  
Industrial Development ◽  
Wind Farm ◽  
Wind Farms ◽  
Maintenance Cost ◽  
Key Factors ◽  
Turbine Generator ◽  
System Availability ◽  
Wind Turbine Generator

To facilitate continuous development of the wind power industry, maintaining technological innovation and reducing cost per kilowatt hour of the electricity generated by the wind turbine generator system (WTGS) are effective measures to facilitate the industrial development. Therefore, the improvement of the system availability for wind farms becomes an important issue which can significantly reduce the operational cost. To improve the system availability, it is necessary to diagnose the system fault for the wind turbine generator so as to find the key factors that influence the system performance and further reduce the maintenance cost. In this paper, a wind farm with 200 MW installed capacity in eastern coastal plain in China is chosen as the research object. A prediction model of wind farm’s faults is constructed based on the Gaussian process metamodel. By comparing with actual observation results, the constructed model is proved able to predict failure events of the wind turbine generator accurately. The developed model is further used to analyze the key factors that influence the system failure. These are conducive to increase the running and maintenance efficiency in wind farms, shorten downtime caused by failure, and increase earnings of wind farms.

scholarly journals A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid

2019 ◽  
Vol 9 (4) ◽  
pp. 769 ◽  
Author(s):  
Fang Liu ◽  
Junjie Ma ◽  
Wendan Zhang ◽  
Min Wu
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Smart Grids ◽  
Wind Farm ◽  
Wind Farms ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
High Penetration ◽  
Comprehensive Survey

As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used in wind farm modeling and simulation. To contribute to the development of wind power generation, a comprehensive survey of the aggregation modeling of wind farms is given in this article. A wind farm aggregation model consists of three parts, respectively, the wind speed model, the wind turbine generator (WTG) model, and the WTG transmission system model. Different modeling and aggregation methods, principles, and formulas for the above three parts are introduced. First, the features and emphasis of different wind speed models are discussed. Then, the aggregated wind turbine generator (WTG) models are divided into single WTG and multi-WTG aggregation models, considering the aggregation of wind turbines and generators, respectively. The calculation methods for the wind conditions and parameters of different aggregation models are discussed. Finally, the WTG transmission model of the wind farm from the aggregation bus is introduced. Some research directions are highlighted in the end according to the issues related to the aggregation modeling of wind farms in smart grids.

Grid Tripping and Re-connection: Full-scale Experimental Validation of a Dynamic Wind Turbine Model

2003 ◽  
Vol 27 (3) ◽  
pp. 205-213 ◽  
Author(s):  
Niels Raben ◽  
Martin Heyman Donovan ◽  
Erik Jørgensen ◽  
Jan Thisted ◽  
Vladislav Akhmatov
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
System Stability ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Shaft System ◽  
The Impact ◽  
Turbine Model

An experiment with tripping and re-connecting a MW wind turbine generator was carried out at the Nøjsomheds Odde wind farm in Denmark. The experimental results are used primarily to validate the shaft system representation of a dynamic wind turbine model. The dynamic wind turbine model is applied in investigations of power system stability with relation to incorporation of large amounts of wind power into the Danish power grid. The simulations and the measurements are found to agree. The experiment was part of a large R&D program started in Denmark to investigate the impact of the increasing capacity of wind power fed into the Danish power grid.

scholarly journals Optimal scheduling of the next preventive maintenance activity for a wind farm

2021 ◽  
Vol 6 (3) ◽  
pp. 949-959
Author(s):  
Quanjiang Yu ◽  
Michael Patriksson ◽  
Serik Sagitov
Keyword(s):  
Wind Turbine ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Wind Farm ◽  
Linear Optimization ◽  
Wind Farms ◽  
Offshore Wind ◽  
Computational Time ◽  
Maintenance Cost ◽  
Component System

Abstract. A large part of the operational cost for a wind farm is due to the cost of equipment maintenance, especially for offshore wind farms. How to reduce the maintenance cost, and hence increase profitability, is this article's focus. It presents a binary linear optimization model whose solution may inform the wind turbine owners about which components, and when, should undergo the next preventive maintenance (PM) replacements. The suggested short-term scheduling strategy takes into account eventual failure events of the multi-component system – in that after the failed system is repaired, the previously scheduled PM plan should be updated, assuming that the restored components are as good as new. The optimization algorithm of this paper, NextPM, is tested through numerical case studies applied to a four-component model of a wind turbine. The first study addresses the important case of a single component system, used for parameter calibration purposes. The second study analyses the case of seasonal variations of mobilization costs, as compared to the constant mobilization cost setting. Among other things, this analysis reveals a 35 % cost reduction achieved by the NextPM model, as compared to the pure corrective maintenance (CM) strategy. The third case study compares the NextPM model with another optimization model – the preventive maintenance scheduling problem with interval costs (PMSPIC), which was the major source of inspiration for this article. This comparison demonstrates that the NextPM model is accurate and much faster in terms of computational time.

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