•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.
Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A supervisory control and data acquisition (SCADA) ...system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT's health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purposes; 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. This study proposes a new method for analysing WT SCADA data by using an a priori knowledge-based adaptive neuro-fuzzy inference system with the aim to achieve automated detection of significant pitch faults. The proposed approach has been applied to the pitch data of two different designs of 26 variable pitch, variable speed and 22 variable pitch, fixed speed WTs, with two different types of SCADA system, demonstrating the adaptability of the approach for application to a variety of techniques. Results are evaluated using confusion matrix analysis and a comparison study of the two tests is addressed to draw conclusions.
Porous and polycrystalline In2O3 nanotubes are prepared by layer‐by‐layer assembly on CNT templates and subsequent calcination (see figure). The as‐prepared In2O3 nanotubes exhibit superior ...sensitivity to NH3 at room temperature, as well as a good reproducibility and short response/recovery time. This novel approach presented herein can be extended to synthesize other metal oxide nanotubes such as NiO, SnO2, Fe2O3, and CuO.
Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) ...system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several 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. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective.
Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) ...system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several 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. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective.
A simple reduction/oxidation chemical solution approach at room temperature has been developed to synthesize ultrafine SnO{sub 2} nanocrystals, in which NaBH{sub 4} is used as a reducing agent ...instead of mineralizers such as sodium hydroxide, ammonia, and alcohol. The morphology, structure, and optical property of the ultrafine SnO{sub 2} nanocrystals have been characterized by high-resolution transmission electron microscopy (HRTEM), X-ray powder diffraction (XRD), differential scanning calorimetry and thermogravimetric analysis (DSC-TGA), X-ray photoelectron spectroscopy (XPS) and UV-vis absorption spectroscopy. It is indicated that the uniform tetragonal ultrafine SnO{sub 2} nanocrystals with the size below 2 nm have been fabricated at room temperature. The band gap of the ultrafine SnO{sub 2} nanocrystals is about 4.1 eV, exhibiting 0.5 eV blue shift from that of the bulk SnO{sub 2} (3.6 eV). Furthermore, the mechanism for the reduction/oxidation chemical reaction synthesis of the ultrafine SnO{sub 2} nanocrystals has been preliminary presented.
A simple reduction/oxidation chemical solution approach at room temperature has been developed to synthesize ultrafine SnO
2 nanocrystals, in which NaBH
4 is used as a reducing agent instead of ...mineralizers such as sodium hydroxide, ammonia, and alcohol. The morphology, structure, and optical property of the ultrafine SnO
2 nanocrystals have been characterized by high-resolution transmission electron microscopy (HRTEM), X-ray powder diffraction (XRD), differential scanning calorimetry and thermogravimetric analysis (DSC–TGA), X-ray photoelectron spectroscopy (XPS) and UV–vis absorption spectroscopy. It is indicated that the uniform tetragonal ultrafine SnO
2 nanocrystals with the size below 2
nm have been fabricated at room temperature. The band gap of the ultrafine SnO
2 nanocrystals is about 4.1
eV, exhibiting 0.5
eV blue shift from that of the bulk SnO
2 (3.6
eV). Furthermore, the mechanism for the reduction/oxidation chemical reaction synthesis of the ultrafine SnO
2 nanocrystals has been preliminary presented.
Polycrystalline tin oxide nanotubes have been prepared by a layer-by-layer technique on carbon nanotubes template. Firstly, the surface of carbon nanotubes was modified by polyelectrolyte. Then, a ...uniform layer of tin oxide nanoparticles was formed on the positive charged surface of carbon nanotubes via a redox process. At last, the polycrystalline tin oxide nanotubes were synthesized after calcination at 650 deg. C in air for 3 h. The as-synthesized polycrystalline nanotubes with large surface area exhibit finer lithium storage capacity and cycling performance, which shows the potentially interesting application in lithium-ion battery.
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