Power System Condition Monitoring and Failure Analysis
First, the significance of power system status monitoring First, the establishment of historical records of equipment operations, so that the equipment in the operation of the situation occurred in the data and data have been accumulated for future use.
Second, determine whether the equipment is operating normally or not, and determine the nature and degree of equipment failure. The main basis for the judgment is the historical archives that have been established before, including the level of equipment operation status and the features displayed in the course of such failures.
Third, in order to provide the necessary basis for maintenance during the implementation of state maintenance, the operating status of the equipment must be evaluated. At the same time, these conditions should be analyzed and classified and evaluated to form a certain evaluation standard. The main content of the assessment of the state detection includes: assessing the running status of the equipment, estimating the abnormal state of the cup, and predicting future changes of the equipment failure status. Including these contents into the evaluation system is mainly to provide certain conditions for assessment, so as to continuously improve and improve the assessment and monitoring.
In summary, the operational data of the equipment can be continuously accumulated, improved, and improved in the process of condition monitoring, breaking the shackles of the past management system, and improving the management system. Therefore, the author believes that in the modern power system equipment management, the condition monitoring system can not be ignored.
Second, the state of the key technology research monitoring First, the so-called power equipment in the signal acquisition aspect of the online monitoring system, its function is to continuously check the status of the equipment and judgments, and to predict the development trend of the equipment state; system operation The time is the period of use of the equipment, that is, it must be monitored as long as the equipment is still in use.
The acquisition of the status information of the diagnosis object is a task that the device operation status reflects the operation of the device. The content of the information includes not only the voltage, current, frequency, and partial discharge of the power device, but also the density of magnetic lines and the normal signal. And fault signal. In general, the signal acquisition method will vary with the characteristics of the signal that characterizes the state of the device. Signal sampling mainly has the following methods:
1. The length of the sample of each acquired signal is the length needed to process a sufficient amount of data. We refer to this sample as a one-time sample.
2. The sampling time is well defined in advance, and the sampling frequency is a set period, which is simply the timing sampling.
3, automatic sampling, sampling occurs at random time, the sampling signal to sudden changes in failure as a means.
4, special sampling, sampling method according to the different fault diagnosis requirements and different, such as speed tracking sampling, peak sampling.
Second, the data transmission signal processing system is usually far away from the monitoring equipment. Therefore, the data is susceptible to interference, easy to lose, and phase shift (subject to environmental factors) in the transmission process. It is necessary to perform analog-to-digital conversion on the data beforehand. Processing and compression packaging, and then transmitted to the processing control center via the communication path. Communication equipment is now widely used in the electric power field. Optical fiber transmission of digital signals can suppress interferences and ensure signal quality.
Third, after the data processing industrial control data processing center receives the state quantity data packet transmitted from the communication line, it uses various mathematical methods to unpack the data. For example, spectrum analysis converts time-domain continuous-time signals into frequency-domain signals of different frequencies for analysis; in the time-domain, correlation analysis between two signals uses correlation analysis to search for processed data of another signal; wavelet analysis; neural network; artificial intelligent. The application of digital information technology and intelligent technology to the data processing of power equipment monitoring systems enables online monitoring of power equipment to be more accurate and accurate.
Third, the fault diagnosis recommendations First, the use of multi-sensing technology and information fusion processing technology to diagnose a fault of different failures. Multi-sensor technology uses multiple sensors to observe the same object from multiple sides and multiple angles, that is, multiple fault characterizations for the same fault, multi-level and multi-domain (time domain, space domain, frequency domain) acquisition of different feature quantities, and selection of faults. Reflect the state information with high sensitivity, thus more comprehensive analysis and diagnosis of failures.
Information fusion technology is a data processing process that analyzes and integrates data from multiple sensors according to certain criteria. Due to the same equipment failure, there is an intrinsic relationship between different reflections in different feature spaces. Using fusion technology to “seek common differences†can improve the accuracy of power equipment status detection and fault diagnosis. However, the basic theory of information fusion is not yet complete, and the diagnostic method remains to be studied.
Second, based on the feature space vector fault diagnosis method, the fault feature amount can be corrected in real time by learning the fault error. This diagnostic method has a certain self-adaptability and is suitable for fault diagnosis of complex objects with uncertainties and slow time-varying. Its essence is to use each fault symptom vector as a new priori indication vector in the original symptomatic megavector set, and to modify the fault feature vector according to an adaptive algorithm. When the priori symptom vector is uncertain, it is necessary to manually determine the first failure.
Thirdly, considering the intrinsic characteristics of power equipment and the uncertainty caused by the lack of on-line monitoring of state information, the principle of maximum degree of membership in fuzzy theory can be considered to diagnose the cause of failure, determine the type of failure, and combine state signals with fuzzy mathematics methods. Analyze the randomness and ambiguity of the problem.
In addition to the above methods, faults can also be diagnosed by combining artificial intelligence, expert systems, and neural networks.
IV. Conclusion In the course of the development of the power system in the last decade, the condition monitoring technology and fault diagnosis technology of the equipment, as a new technology, have continued to develop by leaps and bounds. Regardless of whether it is from the perspective of development prospects or from the perspective of application prospects, it shows a good momentum of development. Although the time for the development of these two technologies in China has continued for quite some time, and various detection devices have been put into production and use, however, the use of condition monitoring and fault diagnosis techniques has not yet been popularized. There are some problems that cannot be ignored in the process of understanding and using technology. We should continue to vigorously explore and study this technology to improve the stability and efficiency of the power system.
Second, determine whether the equipment is operating normally or not, and determine the nature and degree of equipment failure. The main basis for the judgment is the historical archives that have been established before, including the level of equipment operation status and the features displayed in the course of such failures.
Third, in order to provide the necessary basis for maintenance during the implementation of state maintenance, the operating status of the equipment must be evaluated. At the same time, these conditions should be analyzed and classified and evaluated to form a certain evaluation standard. The main content of the assessment of the state detection includes: assessing the running status of the equipment, estimating the abnormal state of the cup, and predicting future changes of the equipment failure status. Including these contents into the evaluation system is mainly to provide certain conditions for assessment, so as to continuously improve and improve the assessment and monitoring.
In summary, the operational data of the equipment can be continuously accumulated, improved, and improved in the process of condition monitoring, breaking the shackles of the past management system, and improving the management system. Therefore, the author believes that in the modern power system equipment management, the condition monitoring system can not be ignored.
Second, the state of the key technology research monitoring First, the so-called power equipment in the signal acquisition aspect of the online monitoring system, its function is to continuously check the status of the equipment and judgments, and to predict the development trend of the equipment state; system operation The time is the period of use of the equipment, that is, it must be monitored as long as the equipment is still in use.
The acquisition of the status information of the diagnosis object is a task that the device operation status reflects the operation of the device. The content of the information includes not only the voltage, current, frequency, and partial discharge of the power device, but also the density of magnetic lines and the normal signal. And fault signal. In general, the signal acquisition method will vary with the characteristics of the signal that characterizes the state of the device. Signal sampling mainly has the following methods:
1. The length of the sample of each acquired signal is the length needed to process a sufficient amount of data. We refer to this sample as a one-time sample.
2. The sampling time is well defined in advance, and the sampling frequency is a set period, which is simply the timing sampling.
3, automatic sampling, sampling occurs at random time, the sampling signal to sudden changes in failure as a means.
4, special sampling, sampling method according to the different fault diagnosis requirements and different, such as speed tracking sampling, peak sampling.
Second, the data transmission signal processing system is usually far away from the monitoring equipment. Therefore, the data is susceptible to interference, easy to lose, and phase shift (subject to environmental factors) in the transmission process. It is necessary to perform analog-to-digital conversion on the data beforehand. Processing and compression packaging, and then transmitted to the processing control center via the communication path. Communication equipment is now widely used in the electric power field. Optical fiber transmission of digital signals can suppress interferences and ensure signal quality.
Third, after the data processing industrial control data processing center receives the state quantity data packet transmitted from the communication line, it uses various mathematical methods to unpack the data. For example, spectrum analysis converts time-domain continuous-time signals into frequency-domain signals of different frequencies for analysis; in the time-domain, correlation analysis between two signals uses correlation analysis to search for processed data of another signal; wavelet analysis; neural network; artificial intelligent. The application of digital information technology and intelligent technology to the data processing of power equipment monitoring systems enables online monitoring of power equipment to be more accurate and accurate.
Third, the fault diagnosis recommendations First, the use of multi-sensing technology and information fusion processing technology to diagnose a fault of different failures. Multi-sensor technology uses multiple sensors to observe the same object from multiple sides and multiple angles, that is, multiple fault characterizations for the same fault, multi-level and multi-domain (time domain, space domain, frequency domain) acquisition of different feature quantities, and selection of faults. Reflect the state information with high sensitivity, thus more comprehensive analysis and diagnosis of failures.
Information fusion technology is a data processing process that analyzes and integrates data from multiple sensors according to certain criteria. Due to the same equipment failure, there is an intrinsic relationship between different reflections in different feature spaces. Using fusion technology to “seek common differences†can improve the accuracy of power equipment status detection and fault diagnosis. However, the basic theory of information fusion is not yet complete, and the diagnostic method remains to be studied.
Second, based on the feature space vector fault diagnosis method, the fault feature amount can be corrected in real time by learning the fault error. This diagnostic method has a certain self-adaptability and is suitable for fault diagnosis of complex objects with uncertainties and slow time-varying. Its essence is to use each fault symptom vector as a new priori indication vector in the original symptomatic megavector set, and to modify the fault feature vector according to an adaptive algorithm. When the priori symptom vector is uncertain, it is necessary to manually determine the first failure.
Thirdly, considering the intrinsic characteristics of power equipment and the uncertainty caused by the lack of on-line monitoring of state information, the principle of maximum degree of membership in fuzzy theory can be considered to diagnose the cause of failure, determine the type of failure, and combine state signals with fuzzy mathematics methods. Analyze the randomness and ambiguity of the problem.
In addition to the above methods, faults can also be diagnosed by combining artificial intelligence, expert systems, and neural networks.
IV. Conclusion In the course of the development of the power system in the last decade, the condition monitoring technology and fault diagnosis technology of the equipment, as a new technology, have continued to develop by leaps and bounds. Regardless of whether it is from the perspective of development prospects or from the perspective of application prospects, it shows a good momentum of development. Although the time for the development of these two technologies in China has continued for quite some time, and various detection devices have been put into production and use, however, the use of condition monitoring and fault diagnosis techniques has not yet been popularized. There are some problems that cannot be ignored in the process of understanding and using technology. We should continue to vigorously explore and study this technology to improve the stability and efficiency of the power system.
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