| 基于随机组合赋权模糊评价的风电机组健康状态评估 |
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| 引用本文： | 李进友,李媛,冯冰,邢作霞,许增金.基于随机组合赋权模糊评价的风电机组健康状态评估[J].太阳能学报,2022,43(8):340-351. |
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| 作者姓名： | 李进友 李媛 冯冰 邢作霞 许增金 |
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| 作者单位： | 1.沈阳工业大学理学院,沈阳 110870; 2.国网天津市电力公司检修公司,天津 300000; 3.沈阳工业大学电气工程学院,沈阳 110870; 4.沈阳工业大学化工装备学院,辽阳 111003 |
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| 基金项目： | 辽宁省自然科学基金（2019-ZD-0202） |
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| 摘 要： | 由于健康指标权重随机性会导致风电机组状态评估灵敏度降低,提出一种评估风电机组健康状态的随机组合赋权模糊评价方法。首先,通过相关性、方差、偏度等多角度分析风电场采集与监视控制系统（SCADA）数据,结合IEC61400-1标准建立机组健康状态评估指标架构,并基于随机因子优化组合权重得到赋权公式,提高评估指标层权重的准确性。其次,为充分覆盖评估指标数据劣化度,基于岭型分布函数建立健康指标劣化隶属度计算函数。结合随机组合权重和隶属度函数,构建风电机组健康状态模糊综合评价数学模型。通过分层评估风电机组健康状态指标架构,得到机组健康等级并实现故障预警。最后,对大连驼山风电场多台机组进行评估试验,结果表明：该文方法能准确评估出风电机组健康状态等级,相比组合赋权云模型方法,灵敏度提高了1.85%。
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| 关 键 词： | 风电机组 SCADA系统 模糊综合评价法 状态评估 随机因子 随机组合赋权 |
| 收稿时间： | 2020-12-28 |
WIND TURBINE HEALTH STATE ASSESSMENT BASED ON STOCHASTIC COMBINATION WEIGHTING FUZZY EVALUATION |
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| Li Jinyou,Li Yuan,Feng Bing,Xing Zuoxia,Xu Zengjin.WIND TURBINE HEALTH STATE ASSESSMENT BASED ON STOCHASTIC COMBINATION WEIGHTING FUZZY EVALUATION[J].Acta Energiae Solaris Sinica,2022,43(8):340-351. |
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| Authors: | Li Jinyou Li Yuan Feng Bing Xing Zuoxia Xu Zengjin |
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| Affiliation: | 1. School of Science, Shenyang University of Technology, Shenyang 110870, China; 2. State Grid Tianjin Electric Power Company Maintenance Company, Tianjin 300000, China; 3. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China; 4. School of Chemical Equipment, Shenyang University of Technology, Liaoyang 111003, China |
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| Abstract: | Since the randomness of health indicator weights will reduce the sensitivity of wind turbine states evaluation, this paper proposes a fuzzy evaluation method based on a random combination weight to evaluate the health state of wind turbines. Firstly, the supervisory control and data acquisition （SCADA） data of wind farms are analyzed through correlation, variance, skewness aspects. Combined with the IEC61400-1 standard, a health states evaluation indicator framework of wind turbines is established, and the weighting formula is obtained based on the optimal combination of random factors, by which to improve the weight accuracy of the evaluation indicator layer. Then, to fully cover the data deterioration of evaluation indicators, the membership calculation function of health indicator deterioration is established based on the ridge distribution function. Combined with the random combination weight and membership function, a fuzzy comprehensive evaluation mathematical model for wind turbine health state is established. Through hierarchical evaluation of wind turbine health state indicator framework, the health grade of the wind turbine is obtained, and fault warning is realized. Finally, multiple units in the Dalian Tuoshan wind farm are evaluated. The results show that, compared with the combined weighted cloud model, the sensitivity of the proposed method is enhanced by 1.85%, which can accurately evaluate the health state of wind turbines. |
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| Keywords: | wind turbines SCADA system fuzzy comprehensive evaluation method condition assessment random factor random combination weighting |
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