Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in s...Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.展开更多
Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require fu...Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.展开更多
Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the ...Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.展开更多
Safety maintenance of power equipment is of great importance in power grids,in which image-processing-based defect recognition is supposed to classify abnormal conditions during daily inspection.However,owing to the b...Safety maintenance of power equipment is of great importance in power grids,in which image-processing-based defect recognition is supposed to classify abnormal conditions during daily inspection.However,owing to the blurred features of defect images,the current defect recognition algorithm has poor fine-grained recognition ability.Visual attention can achieve fine-grained recognition with its abil-ity to model long-range dependencies while introducing extra computational complexity,especially for multi-head attention in vision transformer structures.Under these circumstances,this paper proposes a self-reduction multi-head attention module that can reduce computational complexity and be easily combined with a Convolutional Neural Network(CNN).In this manner,local and global fea-tures can be calculated simultaneously in our proposed structure,aiming to improve the defect recognition performance.Specifically,the proposed self-reduction multi-head attention can reduce redundant parameters,thereby solving the problem of limited computational resources.Experimental results were obtained based on the defect dataset collected from the substation.The results demonstrated the efficiency and superiority of the proposed method over other advanced algorithms.展开更多
Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods su...Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method.However,due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference,the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge(PRPD)map might occasionally presents incorrect results,thus affecting the power equipment operation and maintenance strategy.The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean(FFT-CLEAN)sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm,improves the spatial resolution of the acoustic array sensor device,and proposes an acoustic array sensor device based on the acoustic spectrogram.The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal,and reduce the false alarm rate of the acoustic array sensor device.The proposed method is tested by selecting power cables as the object,and its effectiveness is proved by laboratory verification and field verification.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFB2604600).
文摘Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.
基金the National Natural Science Foundation of China(U1966210).
文摘Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.
文摘Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.
基金supported in part by Major Program of the National Natural Science Foundation of China under Grant 62127803.
文摘Safety maintenance of power equipment is of great importance in power grids,in which image-processing-based defect recognition is supposed to classify abnormal conditions during daily inspection.However,owing to the blurred features of defect images,the current defect recognition algorithm has poor fine-grained recognition ability.Visual attention can achieve fine-grained recognition with its abil-ity to model long-range dependencies while introducing extra computational complexity,especially for multi-head attention in vision transformer structures.Under these circumstances,this paper proposes a self-reduction multi-head attention module that can reduce computational complexity and be easily combined with a Convolutional Neural Network(CNN).In this manner,local and global fea-tures can be calculated simultaneously in our proposed structure,aiming to improve the defect recognition performance.Specifically,the proposed self-reduction multi-head attention can reduce redundant parameters,thereby solving the problem of limited computational resources.Experimental results were obtained based on the defect dataset collected from the substation.The results demonstrated the efficiency and superiority of the proposed method over other advanced algorithms.
基金This work was supported by the science and technology project of State Grid Shanghai Municipal Electric Power Company(No.52090020007F)National Key R&D Program of China(2017YFB0902800).
文摘Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method.However,due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference,the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge(PRPD)map might occasionally presents incorrect results,thus affecting the power equipment operation and maintenance strategy.The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean(FFT-CLEAN)sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm,improves the spatial resolution of the acoustic array sensor device,and proposes an acoustic array sensor device based on the acoustic spectrogram.The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal,and reduce the false alarm rate of the acoustic array sensor device.The proposed method is tested by selecting power cables as the object,and its effectiveness is proved by laboratory verification and field verification.
