CN116110203A - Natural gas power generation early warning management method and system based on intelligent monitoring technology - Google Patents
Natural gas power generation early warning management method and system based on intelligent monitoring technology Download PDFInfo
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Abstract
The invention provides a natural gas power generation early warning management method and system based on an intelligent monitoring technology, which relate to the technical field of intelligent monitoring, and are used for acquiring historical data information of a natural gas power generation unit, acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information, acquiring fault sign information, analyzing the fault sign information to obtain a power generation unit accident tree, monitoring a target power generation unit in real time through a temperature sensor and a vibration sensor to acquire operation parameters, inputting the operation parameters into the power generation unit accident tree, acquiring operation state information of the target power generation unit, and carrying out early warning management on the target power generation unit based on the operation state information. The invention solves the technical problem that the natural gas power generation management in the prior art only can rely on high-strength manual inspection, so that the management effect is poor, and the running state of the generator set is early warned in advance by remotely monitoring the state parameters of the generator set in real time, so that the management effect is improved.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to a natural gas power generation early warning management method and system based on an intelligent monitoring technology.
Background
In recent years, the rapid development of national economy provides continuous power for the stable and rapid development of the power industry in China, the power demand in China is rapidly increased, the power supply capability is steadily enhanced, the work of Western gas east transportation, offshore natural gas development, foreign liquefied natural gas introduction and the like is comprehensively developed along with the large-scale development and utilization of natural gas resources in China, and the rapid development of natural gas markets mainly benefits from the strong support of clean energy sources in the global scope at present, and market demands are promoted. Along with the release of related national policies, the generation of coal by gas is a new trend, so that the method is particularly important for the power generation management of natural gas, but the common natural gas power generation management method still has certain drawbacks, and certain liftable space exists for the power generation management of natural gas.
In the prior art, natural gas power generation management can only rely on high-strength manual inspection, so that the management has certain sporadic performance, and further the management effect is poor.
Disclosure of Invention
The embodiment of the application provides a natural gas power generation early warning management method and system based on an intelligent monitoring technology, which are used for solving the technical problems that in the prior art, natural gas power generation management can only rely on high-strength manual inspection, so that the management effect is poor.
In view of the above problems, embodiments of the present application provide a natural gas power generation early warning management method and system based on an intelligent monitoring technology.
In a first aspect, an embodiment of the present application provides a natural gas power generation early warning management method based on an intelligent monitoring technology, where the method includes: acquiring historical data information of a natural gas generator set; acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information, and acquiring fault symptom information; analyzing the fault symptom information to obtain a generator set fault tree; the temperature sensor and the vibration sensor are used for monitoring the target generator set in real time to obtain operation parameters; inputting the operation parameters into the generator set fault tree to acquire the operation state information of the target generator set; and carrying out early warning management on the target generator set based on the running state information.
In a second aspect, an embodiment of the present application provides a natural gas power generation early warning management system based on an intelligent monitoring technology, where the system includes: the historical data information acquisition module is used for acquiring historical data information of the natural gas generator set; the fault symptom information acquisition module is used for acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information and acquiring fault symptom information; the fault symptom information analysis module is used for analyzing the fault symptom information to obtain a generator set fault tree; the operation parameter acquisition module is used for monitoring the target generator set in real time through the temperature sensor and the vibration sensor to acquire operation parameters; the running state information acquisition module is used for inputting the running parameters into the generator set fault tree to acquire the running state information of the target generator set; and the early warning management module is used for carrying out early warning management on the target generator set based on the running state information.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the embodiment of the application provides a natural gas power generation early warning management method based on intelligent monitoring technology, which relates to the technical field of intelligent monitoring, and is used for acquiring historical data information of a natural gas generator set, acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information, acquiring fault sign information, analyzing the fault sign information to obtain a generator set accident tree, monitoring a target generator set in real time through a temperature sensor and a vibration sensor, acquiring operation parameters, inputting the operation parameters into the generator set accident tree, acquiring operation state information of the target generator set, and carrying out early warning management on the target generator set based on the operation state information. The technical problem that in the prior art, natural gas power generation management can only rely on high-strength manual inspection to ensure poor management effect is solved, and the running state of a generator set is early warned in advance through remote real-time monitoring of the state parameters of the generator set, so that the management effect is improved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a natural gas power generation early warning management method based on an intelligent monitoring technology according to an embodiment of the application;
fig. 2 is a schematic flow chart of extracting time domain characteristic indexes in a natural gas power generation early warning management method based on an intelligent monitoring technology according to an embodiment of the application;
fig. 3 is a schematic flow chart of acquiring the frequency domain index in a natural gas power generation early warning management method based on an intelligent monitoring technology according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a natural gas power generation early warning management system based on an intelligent monitoring technology.
Reference numerals illustrate: the system comprises a historical data information acquisition module 10, a fault symptom information acquisition module 20, a fault symptom information analysis module 30, an operation parameter acquisition module 40, an operation state information acquisition module 50 and an early warning management module 60.
Detailed Description
The embodiment of the application provides a natural gas power generation early warning management method based on an intelligent monitoring technology, which is used for solving the technical problem that in the prior art, natural gas power generation management can only rely on high-strength manual inspection, so that the management effect is poor.
Example 1
As shown in fig. 1, an embodiment of the present application provides a natural gas power generation early warning management method based on an intelligent monitoring technology, where the method is applied to an intelligent monitoring system, and the intelligent monitoring system is communicatively connected with a temperature sensor and a vibration sensor, and the method includes:
step S100: acquiring historical data information of a natural gas generator set;
specifically, the natural gas power generation early warning management method based on the intelligent monitoring technology is applied to an intelligent monitoring system, the intelligent monitoring system is in communication connection with a temperature sensor and a vibration sensor, and the temperature sensor and the vibration sensor are respectively used for monitoring temperature and vibration data of a natural gas generator set in real time during operation.
Firstly, historical working condition data of a generator set is obtained through a natural gas generator set management system, wherein the historical working condition data comprise faults generated in a historical mode and working condition parameters corresponding to various faults, the faults of the natural gas generator set are usually from motor bearing faults of a generator cylinder body and an air cooler fan, and various manifestations of the faults from slight to severe are formed, such as bearing scrapping and machine set shutdown, and each fault type comprises one or more groups of working condition parameters. And through the acquisition of the historical working condition data, data support is provided for subsequent research and judgment of relevant state parameters.
Step S200: acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information, and acquiring fault symptom information;
specifically, fault symptoms such as abnormal functions, overhigh temperature, excessive consumption of lubricating oil cooling water and the like are extracted from massive historical data information, and the most common abnormal functions are taken as examples, and refer to abnormal working conditions of equipment, wherein the abnormal phenomena comprise difficult starting, slow starting, automatic stopping, insufficient equipment operation power, emergency braking failure and the like in the equipment operation process, and the fault symptoms are obvious and easy to detect. The fault symptoms are divided into time domain indexes and frequency domain indexes, for the time domain indexes, as the working condition of the natural gas generator set is not constant, the natural gas generator set deviates from the design working condition according to the actual condition in the actual operation process, the characteristics of time domain parameters are extracted through deviation degree analysis, the change characteristics of temperature and vibration characteristic parameters along with power are analyzed, and the time domain indexes closely connected with the generator set are extracted. For the frequency domain index, the difficulties brought by the variable speed operation and the larger background noise of the natural gas generator set are overcome, so that a noise reduction method is used for extracting the fault characteristic frequency, and the signal is decomposed into a plurality of basic modal components and a remainder through gradual screening, so that the noise is removed, and the noise reduction effect is remarkable.
Step S300: analyzing the fault symptom information to obtain a generator set fault tree;
specifically, during the operation of the generator set, some faults have no influence on the whole operation, that is, even if the faults occur, the system can normally operate, and once one or more links in some faults occur, the possibility of the whole failure can be reduced through the improvement of the system design, the fault symptom information is disassembled according to the logic relationship, the logic diagram of the whole system is constructed, and the relationship among the characteristic parameters, the fault types and the redundant safety design elements is found, so that the generator set fault tree is constructed. Placing the unwanted results at the root of the failure tree, i.e. the uppermost event, such as the machine shutdown as a result of unwanted occurrence during the operation of the natural gas power generating machine, after analyzing the uppermost event, it can be confirmed that the event may occur in two ways: too high a temperature, too much consumption of lubricating oil and cooling water, and there are multiple failure rates in each mode, which becomes a source of failure that can be analyzed. The actual rate value for each failure is identified on the failure tree, and the failure probability of the failure tree can be calculated by a computer program. Through the construction of the generator set fault tree, a basis is provided for judging possible fault modes, and early warning is carried out on the safety weak links of the generator set, so that the management effect on the natural gas generator is improved.
Step S400: the temperature sensor and the vibration sensor are used for monitoring the target generator set in real time to obtain operation parameters;
specifically, the current faults mainly concentrate on motor bearing faults of a generator cylinder body and an air cooler fan, so that the conditions of overhigh temperature, alarm, shutdown and the like are caused, and the relevant states of the air cooler fan and the like cannot be accurately acquired and are researched and judged through manual inspection when the generator set is started and operated, so that the state parameters of two key parts of the generator set are remotely monitored in real time through the temperature sensor and the vibration sensor, the operating state of the generator set is researched and judged through the temperature rise and the vibration, and the obtained data are processed through the means of principal component analysis, noise reduction and the like due to the fact that the data acquired in real time are too huge, the operating parameters of the generator set are obtained through data processing, and the real-time operating condition of the generator set can be obtained through the analysis of the operating parameters.
Step S500: inputting the operation parameters into the generator set fault tree to acquire the operation state information of the target generator set;
specifically, the method searches a cause event of the top time and a combination of the cause event through a fault tree of the generator set, namely a minimum cut set, wherein the cut set is a set of bottom events in the fault tree, the top events are necessarily generated when the bottom events occur, the minimum cut set is a cut set which is not changed into the cut set if any one of the bottom events contained in the cut set is removed, the cut set is the minimum cut set, and potential faults are found through the minimum cut set. Comparing the operation parameters with data in the minimum cut set, judging the operation state of the generator set under the current operation parameters according to the degree of fit, and finding out the cut set of the fault tree as { x "according to the properties of AND/OR gates 1 ，x 2 }，{x 2 ，x 3 }，{x 2 ，x 1 And the operation parameter is { x } 2 ，x 1 And the cut set fitness with the fault tree is 100%, so that the fault is necessarily generated when the fault tree runs under the parameter, management is needed in time, the preset cut set fitness is set, and the fault can be judged as long as the preset cut set fitness is met.
Step S600: performing early warning management on the target generator set based on the running state information;
specifically, the influence of the historical operation data on the faults is analyzed, preset early warning sign information is set, the condition that the influence on the unit is not large is judged to be not met, the condition is placed, the condition that the influence on the unit is not big is judged to be met is judged to be abnormal through subsequent periodical maintenance, the condition that the influence on the unit is met is judged to be abnormal, the condition includes environment temperature abnormality, equipment temperature abnormality, vibration abnormality and the like, the abnormal information and the historical data are compared and analyzed in a summarizing mode, a data analysis result is obtained in the form of a bar graph and a pie graph, an alarm prompt is generated, and the alarm prompt is uploaded to a mobile terminal such as a mobile phone. The real-time remote monitoring of the data is realized, the fault is early warned in advance, and the occurrence of shutdown events caused by serious faults is avoided.
Further, as shown in fig. 2, step S200 of the present application further includes:
step S210: acquiring historical operation condition data of the generator set based on the historical data information;
step S220: acquiring the change characteristics of the temperature characteristic parameter and the vibration characteristic parameter along with the power based on the historical operation condition data;
step S230: and extracting a time domain characteristic index based on the change characteristics.
Specifically, by combining the characteristic that the wind turbine generator system bearing is in variable working condition operation and through a deviation calculation formula
Wherein sigma s The standard error estimated value is represented, and is equivalent to the standard error, whether the measured value is a bad value or not is judged, the coincidence degree of one sample data among a plurality of parallel measurement results is judged by using the precision, the deviation is represented by the deviation, the smaller the deviation is, the higher the precision of the measurement results is, and the characteristics of the time domain parameters are extracted. It should be noted that, when items are compared, if the expected value and standard deviation of one item are larger than those of other items, the standard deviation is not simpleItem risks with large standard deviation are considered to be large, and relative indexes of the item risks and the item risks are further analyzed and compared, wherein the relative indexes are deviation coefficients. And analyzing the change characteristics of the temperature and vibration characteristic parameters along with the power, and extracting time domain characteristic indexes closely related to the running working conditions of the unit.
Further, as shown in fig. 3, step S200 of the present application further includes:
step S240: acquiring fault characteristic information based on the historical operating condition data;
step S250: noise reduction is carried out on the fault characteristic information, and fault characteristic frequency is obtained;
step S260: and acquiring the frequency domain index based on the fault characteristic frequency.
Specifically, all vibration modes contained in the signal are identified through the characteristic time scale, components of each scale forming the original signal are continuously extracted from high frequency to low frequency, so that characteristic mode functions obtained through decomposition are sequentially arranged from high frequency to low frequency, namely, the component with the highest frequency is obtained firstly, then the component with the next highest frequency is obtained, and finally, a residual component with the frequency close to 0 is obtained. For the signals which are continuously decomposed, the high-frequency component with high energy always represents the main characteristic of the original signal and is the most main component, so that the noise reduction processing of fault characteristic information is realized, and the noise frequency is decomposed into the components of each frequency band, and the fault characteristic frequency is obtained.
Further, step S400 of the present application further includes:
step S410: acquiring temperature data through the temperature sensor;
step S420: obtaining vibration data through the vibration sensor;
step S430: performing dimension reduction processing on the temperature data and the vibration data to obtain temperature characteristics and vibration characteristics;
step S440: and acquiring an operation parameter based on the temperature characteristic and the vibration characteristic.
Specifically, the natural gas generating set fault symptoms comprise temperature rise, large vibration, noise increase and the like of a mechanical bearing, temperature and vibration data are acquired through a temperature sensor and a vibration sensor, and a database is established so as to accurately analyze and judge the running state of the bearing.
The feature data in the database is subjected to dimension reduction processing through a principal component analysis method, firstly, the extracted data is subjected to numerical processing, a feature data set matrix is constructed, a first feature data set is obtained, each feature data in the first feature data set is subjected to centering processing, a second feature data set is formed, and the second feature data set is a data matrix.
And calculating the second characteristic data set through a covariance formula to obtain a first covariance matrix of the second characteristic data set, and then calculating the characteristic value and the characteristic vector of the first covariance matrix through matrix calculation, wherein each characteristic value corresponds to one characteristic vector. And selecting the first K maximum eigenvalues and the eigenvectors corresponding to the first eigenvalues from the first eigenvector of the obtained faults, and projecting the original features in the first eigenvalue set onto the selected eigenvector to obtain a first eigenvalue set after dimension reduction, so as to obtain the operation parameters.
And performing dimension reduction processing on the data in the database by using a principal component analysis method, and eliminating redundant data on the premise of guaranteeing the information quantity, so that the sample quantity of the characteristic data in the database is reduced, and the operation speed of the data is increased.
Further, step S600 of the present application further includes:
step S610: acquiring preset early warning sign information;
step S620: judging whether the running state information meets the early warning sign information or not;
step S630: and when the information is satisfied, generating abnormal alarm information and sending the abnormal alarm information to the mobile terminal.
Specifically, the past rules or observed possibility precursors are summarized according to historical operation data to obtain preset early warning sign information, if the temperature reaches a certain value or is free, the machine set is stopped after a period of time, the value is used as the early warning sign information, the operation state information is monitored, when the operation state information does not meet the early warning sign information, the machine set is in a normal operation state, when the operation state information meets the early warning sign information, the machine set is in a fault imminent state, abnormal alarm information is generated, emergency signals are sent to management staff to report dangerous conditions, and therefore the occurrence of faults under the conditions of unknowing or insufficient preparation is avoided, and loss caused by the faults is reduced to the greatest extent.
Further, the present application further includes:
step S710: acquiring an abnormality symptom based on the abnormality alarm information;
step S720: inputting the abnormal symptoms into the generator set fault tree to reversely push, and obtaining a fault mode;
step S730: a repair measure is determined based on the failure mode.
Specifically, the running state meeting the abnormal alarm information is taken as an abnormal sign, the fault tree analysis is generally a deduction type failure analysis method from top to bottom to analyze the undesired state in the system, at the moment, the abnormal sign is taken as a starting point of the traversing of the fault tree of the generator set, a reverse reasoning method is used from bottom to top, more fault signs are fused, a fault mode is identified, the underlying cause of fault development is found out through the fault tree analysis, and then maintenance measures are determined.
Example two
Based on the same inventive concept as the natural gas power generation early warning management method based on the intelligent monitoring technology in the foregoing embodiments, as shown in fig. 4, the present application provides a natural gas power generation early warning management system based on the intelligent monitoring technology, where the system includes:
the system comprises a historical data information acquisition module 10, wherein the historical data information acquisition module 10 is used for acquiring historical data information of a natural gas generator set;
the fault symptom information acquisition module 20 is used for acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information and acquiring fault symptom information;
the fault symptom information analysis module 30 is used for analyzing the fault symptom information to obtain a generator set fault tree;
the operation parameter acquisition module 40 is used for monitoring the target generator set in real time through the temperature sensor and the vibration sensor to acquire operation parameters;
the operation state information acquisition module 50 is used for inputting the operation parameters into the generator set fault tree to acquire the operation state information of the target generator set;
the early warning management module 60 is used for carrying out early warning management on the target generator set based on the running state information by the early warning management module 60.
Further, the system further comprises:
the historical operation condition data acquisition module is used for acquiring the historical operation condition data of the generator set based on the historical data information;
the change characteristic acquisition module is used for acquiring the change characteristics of the temperature characteristic parameters and the vibration characteristic parameters along with the power based on the historical operation condition data;
and the time domain feature index acquisition module is used for extracting the time domain feature index based on the change characteristics.
Further, the system further comprises:
the fault characteristic information acquisition module is used for acquiring fault characteristic information based on the historical operating condition data;
the noise reduction module is used for reducing noise of the fault characteristic information and obtaining fault characteristic frequency;
and the frequency domain index acquisition module is used for acquiring the frequency domain index based on the fault characteristic frequency.
Further, the system further comprises:
the temperature data acquisition module is used for acquiring temperature data through the temperature sensor;
the vibration data acquisition module is used for acquiring vibration data through the vibration sensor;
the dimension reduction processing module is used for carrying out dimension reduction processing on the temperature data and the vibration data to obtain temperature characteristics and vibration characteristics;
and the parameter acquisition module is used for acquiring the operation parameters based on the temperature characteristics and the vibration characteristics.
Further, the system further comprises:
the early warning sign information acquisition module is used for acquiring preset early warning sign information;
the running state information judging module is used for judging whether the running state information meets the early warning sign information or not;
and the abnormal alarm information generation module is used for generating abnormal alarm information when the abnormal alarm information is satisfied and sending the abnormal alarm information to the mobile terminal.
Further, the system further comprises:
the abnormal symptom acquisition module is used for acquiring abnormal symptoms based on the abnormal alarm information;
the reverse-pushing module is used for inputting the abnormal symptoms into the generator set fault tree to carry out reverse-pushing, and obtaining a fault mode;
and the maintenance measure acquisition module is used for determining maintenance measures based on the fault mode.
Through the foregoing detailed description of a natural gas power generation early warning management method based on an intelligent monitoring technology, those skilled in the art can clearly know a natural gas power generation early warning management method and a natural gas power generation early warning management system based on an intelligent monitoring technology in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because the device corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The natural gas power generation early warning management method based on the intelligent monitoring technology is characterized by being applied to an intelligent monitoring system, wherein the intelligent monitoring system is in communication connection with a temperature sensor and a vibration sensor, and the method comprises the following steps:
acquiring historical data information of a natural gas generator set;
acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information, and acquiring fault symptom information;
analyzing the fault symptom information to obtain a generator set fault tree;
the temperature sensor and the vibration sensor are used for monitoring the target generator set in real time to obtain operation parameters;
inputting the operation parameters into the generator set fault tree to acquire the operation state information of the target generator set;
and carrying out early warning management on the target generator set based on the running state information.
2. The method of claim 1, wherein the obtaining the time domain indicator and the frequency domain indicator of the fault signal based on the historical data information comprises:
acquiring historical operation condition data of the generator set based on the historical data information;
acquiring the change characteristics of the temperature characteristic parameter and the vibration characteristic parameter along with the power based on the historical operation condition data;
and extracting a time domain characteristic index based on the change characteristics.
3. The method of claim 2, wherein the obtaining the time domain indicator and the frequency domain indicator of the fault signal based on the historical data information further comprises:
acquiring fault characteristic information based on the historical operating condition data;
noise reduction is carried out on the fault characteristic information, and fault characteristic frequency is obtained;
and acquiring the frequency domain index based on the fault characteristic frequency.
4. The method of claim 1, wherein the real-time monitoring of the target generator set by the temperature sensor and the vibration sensor to obtain the operating parameters comprises:
acquiring temperature data through the temperature sensor;
obtaining vibration data through the vibration sensor;
performing dimension reduction processing on the temperature data and the vibration data to obtain temperature characteristics and vibration characteristics;
and acquiring an operation parameter based on the temperature characteristic and the vibration characteristic.
5. The method of claim 1, wherein the pre-warning management of the target genset based on the operating state comprises:
acquiring preset early warning sign information;
judging whether the running state information meets the early warning sign information or not;
and when the information is satisfied, generating abnormal alarm information and sending the abnormal alarm information to the mobile terminal.
6. The method of claim 5, wherein the method further comprises:
acquiring an abnormality symptom based on the abnormality alarm information;
inputting the abnormal symptoms into the generator set fault tree to reversely push, and obtaining a fault mode;
a repair measure is determined based on the failure mode.
7. The utility model provides a natural gas electricity generation early warning management system based on intelligent monitoring technique, its characterized in that, system and temperature sensor, vibration sensor communication connection, the system includes:
the historical data information acquisition module is used for acquiring historical data information of the natural gas generator set;
the fault symptom information acquisition module is used for acquiring time domain indexes and frequency domain indexes of fault signals based on the historical data information and acquiring fault symptom information;
the fault symptom information analysis module is used for analyzing the fault symptom information to obtain a generator set fault tree;
the operation parameter acquisition module is used for monitoring the target generator set in real time through the temperature sensor and the vibration sensor to acquire operation parameters;
the running state information acquisition module is used for inputting the running parameters into the generator set fault tree to acquire the running state information of the target generator set;
and the early warning management module is used for carrying out early warning management on the target generator set based on the running state information.
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| CN117010191A (en) * | 2023-08-04 | 2023-11-07 | 贵州北盘江电力股份有限公司光照分公司 | Abnormal state identification method and system for hydroelectric generating set |
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| CN117010191A (en) * | 2023-08-04 | 2023-11-07 | 贵州北盘江电力股份有限公司光照分公司 | Abnormal state identification method and system for hydroelectric generating set |
| CN117010191B (en) * | 2023-08-04 | 2024-03-19 | 贵州北盘江电力股份有限公司光照分公司 | Abnormal state identification method and system for hydroelectric generating set |
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