In view of characteristics of solar photovoltaic (PV) power station such as the decentralized layout and massive monitoring and control information, a solar PV power generation monitoring and control system has been d...In view of characteristics of solar photovoltaic (PV) power station such as the decentralized layout and massive monitoring and control information, a solar PV power generation monitoring and control system has been designed. The system is designed into three layers namely the sensor and actuator layer, the PLC field monitoring and control layer and the remote network monitoring and control layer. Through ZigBee wireless network, PROFIBUS and GPRS wireless network, the system makes the three layers exchange information rapidly, and the system supervises not only various operational parameters of the power generating system but also weather changes as a way to change the solar tracking strategy of the PV power generating system and reduce the operating energy consumption of the system. Through the hardware redundant design of PLC central controller and the upper computer, the solar PV power station can be more secure and reliable when running.展开更多
To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Differen...To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.展开更多
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t...A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.展开更多
This paper has an objective to show a developed quantitative criterion,based in two mathematical variables that explicit the deviation degree of a normal situation,applying simultaneously data from terminal impedances...This paper has an objective to show a developed quantitative criterion,based in two mathematical variables that explicit the deviation degree of a normal situation,applying simultaneously data from terminal impedances and frequency response.Based in more than 100-measured equipment,of different applications(step-up transformer,transmission transformer,etc.,),for a period of 10 years,the work presents some examples of practical application of this methodology in Brazilian Electrical System.展开更多
The open-circuit fault of the power switches in shunt active power filter(SAPF) would exacerbate the harmonic pollution of power grid, and degrade the reliability of the devices and system. A fault diagnosis method is...The open-circuit fault of the power switches in shunt active power filter(SAPF) would exacerbate the harmonic pollution of power grid, and degrade the reliability of the devices and system. A fault diagnosis method is proposed based on reference model and an over-modulation strategy under hardware fault tolerance for SAPF. First, a mathematic model is established for SAPF. Second, the residuals are generated by comparing the outputs of reference model and those of actual model, and open-switch fault is detected and diagnosed by residual evaluation. After that, hardware fault tolerance is performed with the three-phase four-switch(TPFS) topology to isolate the faulty phase. Finally, the over-modulation strategy is proposed to increase the voltage transfer ratio of the TPFS topology. Simulation and experimental results verified the feasibility and effectiveness of the proposed method.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm bas...In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.展开更多
在机械设备监测中因气泡与明暗场的干扰,传统的在线磨粒图像分析方法无法实现磨粒的准确分割。为有效排除在线磨粒图像中气泡和明暗场的干扰,提出一种基于U-Net网络的磨粒图像前景分割算法;在磨粒图像前景分割的基础上,计算了磨粒覆盖率...在机械设备监测中因气泡与明暗场的干扰,传统的在线磨粒图像分析方法无法实现磨粒的准确分割。为有效排除在线磨粒图像中气泡和明暗场的干扰,提出一种基于U-Net网络的磨粒图像前景分割算法;在磨粒图像前景分割的基础上,计算了磨粒覆盖率,并基于长短期记忆神经网络(Long Short Term Memory,LSTM)对磨粒图像的磨粒覆盖率进行时序预测。开展长周期摩擦磨损试验,采集大量磨粒图像,对提出的磨粒图像前景分割算法和磨粒覆盖率预测方法进行了验证。结果表明:训练后的U-Net网络能够实现磨粒图像的精确分割,基于LSTM预测的磨粒覆盖率和真实磨粒覆盖率趋势基本吻合,可为机械设备故障诊断提供参考。展开更多
It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quit...It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.展开更多
A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively an...A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions was employed to determine the nature and the grade of the faults in a transformer system. And the proposed method was verified with the experimental data. The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults can be detected at the same time.展开更多
Power transformer is one of the most important equipment in the power system.Its operating condition affects the reliability of power supply directly.Therefore,in order to guarantee transformer operation safely and re...Power transformer is one of the most important equipment in the power system.Its operating condition affects the reliability of power supply directly.Therefore,in order to guarantee transformer operation safely and reliably,it is necessary to assess condition of power transformer accurately.Return voltage method based on voltage response measurements is still a new non-intrusive diagnosis method for internal insulation aging of transformer.In this paper the technique and application of return voltage measurement and some results of voltage response measurements of several transformers was introduced.Voltage response measurements were performed on various transformers with different voltage grades,various operating periods,different moisture contents and aging degrees on site.Derived moisture contents from return voltage measurement were compared with the corresponding moisture contents obtained from the analysis of oil samples.Based on on-site experiments and theoretical analysis,the criteria for insulation state of transformer are proposed.Moisture condition of transformer insulation can be determined by using return dominant time constant,and a good correlation between aging degree and the return voltage initial slopes of the aged transformers.Field test performed on several transformers,its interpretation of results are also presented,which proves that return voltage measurements can be used as a reliable tool for evaluating moisture content in transformer insulation.展开更多
基金sponsored by National Natural Science Foundation of China(50975020)National Major Program of Science and Tech-nique(2009ZX04014-101)Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipalipality(PHR20090518)
文摘In view of characteristics of solar photovoltaic (PV) power station such as the decentralized layout and massive monitoring and control information, a solar PV power generation monitoring and control system has been designed. The system is designed into three layers namely the sensor and actuator layer, the PLC field monitoring and control layer and the remote network monitoring and control layer. Through ZigBee wireless network, PROFIBUS and GPRS wireless network, the system makes the three layers exchange information rapidly, and the system supervises not only various operational parameters of the power generating system but also weather changes as a way to change the solar tracking strategy of the PV power generating system and reduce the operating energy consumption of the system. Through the hardware redundant design of PLC central controller and the upper computer, the solar PV power station can be more secure and reliable when running.
基金This work was supported by the National Key Research and Development Program Topics(2020YFC2200902)the National Natural Science Foundation of China(11872110).
文摘To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.
基金Project(217/s/458)supported by Azarbaijan Shahid Madani University,Iran
文摘A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.
文摘This paper has an objective to show a developed quantitative criterion,based in two mathematical variables that explicit the deviation degree of a normal situation,applying simultaneously data from terminal impedances and frequency response.Based in more than 100-measured equipment,of different applications(step-up transformer,transmission transformer,etc.,),for a period of 10 years,the work presents some examples of practical application of this methodology in Brazilian Electrical System.
基金Project(2012AA051601)supported by the High-Tech Research and Development Program of China
文摘The open-circuit fault of the power switches in shunt active power filter(SAPF) would exacerbate the harmonic pollution of power grid, and degrade the reliability of the devices and system. A fault diagnosis method is proposed based on reference model and an over-modulation strategy under hardware fault tolerance for SAPF. First, a mathematic model is established for SAPF. Second, the residuals are generated by comparing the outputs of reference model and those of actual model, and open-switch fault is detected and diagnosed by residual evaluation. After that, hardware fault tolerance is performed with the three-phase four-switch(TPFS) topology to isolate the faulty phase. Finally, the over-modulation strategy is proposed to increase the voltage transfer ratio of the TPFS topology. Simulation and experimental results verified the feasibility and effectiveness of the proposed method.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
文摘In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.
文摘在机械设备监测中因气泡与明暗场的干扰,传统的在线磨粒图像分析方法无法实现磨粒的准确分割。为有效排除在线磨粒图像中气泡和明暗场的干扰,提出一种基于U-Net网络的磨粒图像前景分割算法;在磨粒图像前景分割的基础上,计算了磨粒覆盖率,并基于长短期记忆神经网络(Long Short Term Memory,LSTM)对磨粒图像的磨粒覆盖率进行时序预测。开展长周期摩擦磨损试验,采集大量磨粒图像,对提出的磨粒图像前景分割算法和磨粒覆盖率预测方法进行了验证。结果表明:训练后的U-Net网络能够实现磨粒图像的精确分割,基于LSTM预测的磨粒覆盖率和真实磨粒覆盖率趋势基本吻合,可为机械设备故障诊断提供参考。
基金This project was supported by the Hebei Provincial Nature Science Foundation (E20070011048).
文摘It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.
文摘A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions was employed to determine the nature and the grade of the faults in a transformer system. And the proposed method was verified with the experimental data. The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults can be detected at the same time.
基金Project Supported by Science and Technology Fund of Fujian E-lectric Power Limited Company(NC2006044)Scientific Research Fund of Fujian Education Depart ment(JB06045)
文摘Power transformer is one of the most important equipment in the power system.Its operating condition affects the reliability of power supply directly.Therefore,in order to guarantee transformer operation safely and reliably,it is necessary to assess condition of power transformer accurately.Return voltage method based on voltage response measurements is still a new non-intrusive diagnosis method for internal insulation aging of transformer.In this paper the technique and application of return voltage measurement and some results of voltage response measurements of several transformers was introduced.Voltage response measurements were performed on various transformers with different voltage grades,various operating periods,different moisture contents and aging degrees on site.Derived moisture contents from return voltage measurement were compared with the corresponding moisture contents obtained from the analysis of oil samples.Based on on-site experiments and theoretical analysis,the criteria for insulation state of transformer are proposed.Moisture condition of transformer insulation can be determined by using return dominant time constant,and a good correlation between aging degree and the return voltage initial slopes of the aged transformers.Field test performed on several transformers,its interpretation of results are also presented,which proves that return voltage measurements can be used as a reliable tool for evaluating moisture content in transformer insulation.
