Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS
The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/7/898 |
| _version_ | 1798002588415688704 |
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| author | Quan Zhou Taotao Xiong Mubin Wang Chenmeng Xiang Qingpeng Xu |
| author_facet | Quan Zhou Taotao Xiong Mubin Wang Chenmeng Xiang Qingpeng Xu |
| author_sort | Quan Zhou |
| collection | DOAJ |
| description | The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method. |
| first_indexed | 2024-04-11T11:54:41Z |
| format | Article |
| id | doaj.art-d6895070cea54d9e8294177d06653194 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:54:41Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d6895070cea54d9e8294177d066531942022-12-22T04:25:12ZengMDPI AGEnergies1996-10732017-07-0110789810.3390/en10070898en10070898Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFISQuan Zhou0Taotao Xiong1Mubin Wang2Chenmeng Xiang3Qingpeng Xu4State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaState Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaState Grid Lishui Electric Power Supply Company, Lishui 323000, ChinaState Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaState Grid Chengdu Power Supply Company, Chengdu 610041, ChinaThe construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.https://www.mdpi.com/1996-1073/10/7/898wind turbinecluster analysisimproved Adaptive Neuro-fuzzy Inference System (ANFIS)fault early warning |
| spellingShingle | Quan Zhou Taotao Xiong Mubin Wang Chenmeng Xiang Qingpeng Xu Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS Energies wind turbine cluster analysis improved Adaptive Neuro-fuzzy Inference System (ANFIS) fault early warning |
| title | Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS |
| title_full | Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS |
| title_fullStr | Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS |
| title_full_unstemmed | Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS |
| title_short | Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS |
| title_sort | diagnosis and early warning of wind turbine faults based on cluster analysis theory and modified anfis |
| topic | wind turbine cluster analysis improved Adaptive Neuro-fuzzy Inference System (ANFIS) fault early warning |
| url | https://www.mdpi.com/1996-1073/10/7/898 |
| work_keys_str_mv | AT quanzhou diagnosisandearlywarningofwindturbinefaultsbasedonclusteranalysistheoryandmodifiedanfis AT taotaoxiong diagnosisandearlywarningofwindturbinefaultsbasedonclusteranalysistheoryandmodifiedanfis AT mubinwang diagnosisandearlywarningofwindturbinefaultsbasedonclusteranalysistheoryandmodifiedanfis AT chenmengxiang diagnosisandearlywarningofwindturbinefaultsbasedonclusteranalysistheoryandmodifiedanfis AT qingpengxu diagnosisandearlywarningofwindturbinefaultsbasedonclusteranalysistheoryandmodifiedanfis |