A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation
The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-leve...
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MDPI AG
2024-11-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/11/234 |
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| author | Xueting Cheng Jie Hao Yuxiang Li Juan Wei Weiru Wang Yaohui Lu |
| author_facet | Xueting Cheng Jie Hao Yuxiang Li Juan Wei Weiru Wang Yaohui Lu |
| author_sort | Xueting Cheng |
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| description | The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of “key component–wind turbine–wind farm” based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the “maximum membership degree”, which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms. |
| format | Article |
| id | doaj-art-a551a34ce2d240acb3a656a589fb5239 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-a551a34ce2d240acb3a656a589fb52392024-11-26T18:23:36ZengMDPI AGTechnologies2227-70802024-11-01121123410.3390/technologies12110234A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive EvaluationXueting Cheng0Jie Hao1Yuxiang Li2Juan Wei3Weiru Wang4Yaohui Lu5School of Electrical Engineering, Zhejiang University, Hangzhou 310058, ChinaState Grid Shanxi Electric Power Research Institute, Taiyuan 030001, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaState Grid Shanxi Electric Power Research Institute, Taiyuan 030001, ChinaState Grid Shanxi Electric Power Research Institute, Taiyuan 030001, ChinaThe maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of “key component–wind turbine–wind farm” based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the “maximum membership degree”, which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms.https://www.mdpi.com/2227-7080/12/11/234wind turbine groupservice qualityfuzzy comprehensive evaluationentropy weight methodstate assessment |
| spellingShingle | Xueting Cheng Jie Hao Yuxiang Li Juan Wei Weiru Wang Yaohui Lu A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation Technologies wind turbine group service quality fuzzy comprehensive evaluation entropy weight method state assessment |
| title | A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation |
| title_full | A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation |
| title_fullStr | A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation |
| title_full_unstemmed | A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation |
| title_short | A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation |
| title_sort | three level service quality index system for wind turbine groups based on fuzzy comprehensive evaluation |
| topic | wind turbine group service quality fuzzy comprehensive evaluation entropy weight method state assessment |
| url | https://www.mdpi.com/2227-7080/12/11/234 |
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