کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
387036 | 660895 | 2013 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS](/preview/png/387036.png)
• Fast growing wind industry needs sophisticated fault prognosis approach.
• A-priori knowledge-based ANFIS fault prognosis procedure was proposed.
• Domain knowledge incorporation to improve fault diagnosis.
• Test in a new wind farm and result was compared to an alarm approach.
• Confusion Matrix analysis was made to show the accuracy rate.
The fast growing wind industry has shown a need for more sophisticated fault prognosis analysis in the critical and high value components of a wind turbine (WT). Current WT studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarms and signals that could provide an early indication of component fault and allow the operator to plan system repair prior to complete failure. Several research programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. A new fault prognosis procedure is proposed in this paper using a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (ANFIS). This has the aim to achieve automated detection of significant pitch faults, which are known to be significant failure modes. With the advantage of a-priori knowledge incorporation, the proposed system has improved ability to interpret the previously unseen conditions and thus fault diagnoses are improved. In order to construct the proposed system, the data of the 6 known WT pitch faults were used to train the system with a-priori knowledge incorporated. The effectiveness of the approach was demonstrated using three metrics: (1) the trained system was tested in a new wind farm containing 26 WTs to show its prognosis ability; (2) the first test result was compared to a general alarm approach; (3) a Confusion Matrix analysis was made to demonstrate the accuracy of the proposed approach. The result of this research has demonstrated that the proposed a-priori knowledge-based ANFIS (APK-ANFIS) approach has strong potential for WT pitch fault prognosis.
Journal: Expert Systems with Applications - Volume 40, Issue 17, 1 December 2013, Pages 6863–6876