CN105573302A - Coal-fired power plant unit diagnostic device, system and method - Google Patents
Coal-fired power plant unit diagnostic device, system and method Download PDFInfo
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- CN105573302A CN105573302A CN201610076320.4A CN201610076320A CN105573302A CN 105573302 A CN105573302 A CN 105573302A CN 201610076320 A CN201610076320 A CN 201610076320A CN 105573302 A CN105573302 A CN 105573302A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention belongs to the energy conservation optimization field and discloses a coal-fired power plant unit diagnosis device. The diagnostic device includes a receiving module (1) used for receiving power plant unit data and expert experience model data and a reasoning module (2) which is connected with the receiving module (1) and is used for performing judgment according to the power plant unit data and expert experience model data so as to obtain diagnosis results and providing troubleshooting measures according to the diagnosis results. With the device of the invention adopted, the faults of a power plant unit can be accurately and rapidly diagnosed, and a powerful guarantee can be provided for production, maintenance and decision making.
Description
Technical field
The present invention relates to energy saving optimizing, particularly, relate to a kind of coal-burning power plant unit diagnostic device, system and method.
Background technology
At present, the annual generated energy of China accounts for the ratio of gross generation all more than 80%, and the electricity consumption of the whole society mainly provides by Thermal Power Enterprises.Electricity power enterprise faces the power system reform set up modern electric market, separate the factory and network and surf the Net at a competitive price, and is badly in need of the operational efficiency improving genset, improves the competitive power of enterprise.
, there is following problem in the equipment fault diagnosis forecast system of generation current factory: can only realize off-limit alarm usually, can not fully utilize test data, characteristic parameter analyses in depth running trend of the equipment, equipment failure position, reason, degree and expert advice; The knowledge of domain expert and experience can not be made full use of, there is the problems such as " bottleneck " and " flexibility " difference of knowledge representation and utilization; Can not effectively assess, perfect, expand expert knowledge library; Inference function is not strong, the nonlinear system of amount complicated and changeable is often occurred to the problem of " wrong report " or " failing to report "; Lack the explanation function to test result; The characteristic parameter obtained by data acquisition and signal analysis and processing can not utilize computing machine automatically to complete analysis interpretation to test result.
Because the complicacy of power generation plant technology is more and more higher, also more and more higher to the requirement of reliability and security, once system jam will cause huge economic loss and casualties.So carry out the research of fault diagnosis technology and apply, for the economy and security improving electric power factory equipment, there is great effect.
Summary of the invention
The object of this invention is to provide a kind of coal-burning power plant unit diagnostic device, system and method, coal-burning power plant's unit diagnostic device, system and method accurately can promptly diagnose Power Plant fault, for production, maintenance and decision-making provide strong guarantee.
To achieve these goals, the invention provides a kind of coal-burning power plant unit diagnostic device, this diagnostic device comprises: receiver module, for receiving Power Plant data and expertise model data; Reasoning module, is connected with described receiver module, for carrying out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provides failture evacuation measure according to diagnostic result.
The present invention also provides a kind of coal-burning power plant unit diagnostic system, and this diagnostic system comprises: data collector, for gathering the data of Power Plant; Data storage device, for storing expertise model data and result data; Coal-burning power plant's unit diagnostic device mentioned above; Operation display device, is connected with described data storage device, for obtaining described result data and the described diagnostic result of display.
The present invention also provides a kind of coal-burning power plant unit diagnostic method, and this diagnostic method comprises: receive Power Plant data and expertise model data; Carry out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provide failture evacuation measure according to diagnostic result.
Pass through technique scheme, adopt coal-burning power plant provided by the invention unit diagnostic device, system and method, Power Plant data and expertise model data is received by receiver module, carrying out judging to draw diagnostic result according to described Power Plant data and described expertise model data by reasoning module, and provide failture evacuation measure according to diagnostic result, wherein reasoning module is expert system module, neural network module and fuzzy logic module, carry out process by expert system module and draw the first diagnostic result, carry out process by neural network module and draw second opinion result, carry out process by fuzzy logic module and draw the 3rd diagnostic result, then integration module is utilized to select the diagnostic result of the highest confidence level.The present invention accurately can promptly diagnose Power Plant fault, for production, maintenance and decision-making provide strong guarantee.
Other features and advantages of the present invention are described in detail in embodiment part subsequently.
Accompanying drawing explanation
Accompanying drawing is used to provide a further understanding of the present invention, and forms a part for instructions, is used from explanation the present invention, but is not construed as limiting the invention with embodiment one below.In the accompanying drawings:
Fig. 1 is the structural representation of coal-burning power plant provided by the invention unit diagnostic device;
Fig. 2 is the structural representation of coal-burning power plant provided by the invention unit diagnostic system;
Fig. 3 is the process flow diagram of coal-burning power plant provided by the invention unit diagnostic method.
Description of reference numerals
1 receiver module 2 reasoning module
3 expert system module 4 neural network modules
5 fuzzy logic module 6 integration module
11 data collector 12 data storage devices
13 coal-burning power plant's unit diagnostic device 14 operation display devices.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.Should be understood that, embodiment described herein, only for instruction and explanation of the present invention, is not limited to the present invention.
Fig. 1 is the structural representation of coal-burning power plant provided by the invention unit diagnostic device.As shown in Figure 1, the invention provides a kind of coal-burning power plant unit diagnostic device, this diagnostic device comprises: receiver module 1, for receiving Power Plant data and expertise model data; Reasoning module 2, is connected with described receiver module 1, for carrying out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provides failture evacuation measure according to diagnostic result.
Expertise model data can be understood as accumulative experience inference for a long time, such as: boiler-steam dome full water problem is embodied in main steam temperature, the gentle axial translation of thrust bearing shoe valve; Oil cooler chilled water disruption is embodied on the gentle lubricating oil temperature of thrust bearing shoe valve; Load big ups and downs thrust bearing shoe valve overload is embodied on whether thrust bearing shoe valve temperature, axial translation and generated power load fluctuate.Separately there are other inferences, do not repeat them here.
Coal-burning power plant's unit diagnostic device can also comprise pretreatment module, is connected with receiver module 1, may be used for removing unwanted data.
Described reasoning module 2 comprises: expert system module 3, is connected with described receiver module 1, for judging the confidence level of the fault that described Power Plant data represent in conjunction with described expertise model data, to draw the first diagnostic result.
Described reasoning module 2 also comprises: neural network module 4, be connected with described receiver module 1, for utilizing neural network mathematical model, described Power Plant data and described expertise model data are processed, judge the confidence level of the fault that described Power Plant data represent, to draw second opinion result; Fuzzy logic module 5, is connected with described receiver module 1, for utilizing fuzzy logic to process described Power Plant data and described expertise model data, judges the confidence level of the fault that described Power Plant data represent, to draw the 3rd diagnostic result.
Expertise model data described in combination mentioned above judges the confidence level of the fault that described Power Plant data represent, the reasoning used in the process utilizing neural network mathematical model to process described Power Plant data and described expertise model data and to utilize fuzzy logic to process described Power Plant data and described expertise model data according to for " if ... so ... " form, such as: " if generator unit stator shell amplitude of radial vibration increases with exciting current and increases, characteristic frequency is for turning frequently, vibration phase is identical with shaft displacement phase place, so the confidence level of stator circularity deficiency is 0.9 ".
Expert system, neural network and fuzzy logic are respectively preponderated in information analysis application, but also respectively there is not enough aspect, the present invention has the ability of the weakness overcoming them, this can understand from the ability aspect analyzing fuzzy logic and neural network: because the difference of two kinds of methods, they respectively have relative merits, specifically, fuzzy system attempts the fuzzy conception existed in the language and thought of description and handler, thus apish intelligence, neural network is then physiological structure according to human brain and information process, create artificial neural network, its object is also apish intelligence.Because neural network and fuzzy logic are commonly used in the prior art, therefore no longer too much repeat at this.
This diagnostic device also comprises: integration module 6, be connected with described expert system module 3, described neural network module 4 and described fuzzy logic module 5, for according to described first diagnostic result, described second opinion result and described 3rd diagnostic result, judge that the diagnostic result of the highest confidence level is to draw optimal diagnosis result, and provide failture evacuation measure.If have wherein a kind of diagnostic result and other two kinds of gaps excessive in last three kinds of diagnostic results, then ignore this diagnostic result.
Fig. 2 is the structural representation of coal-burning power plant provided by the invention unit diagnostic system.As shown in Figure 2, the present invention also provides a kind of coal-burning power plant unit diagnostic system, and this diagnostic system comprises: data collector 11, for gathering the data of Power Plant; Data storage device 12, for storing expertise model data and result data; Coal-burning power plant's unit diagnostic device 13 mentioned above; Operation display device 14, is connected with described data storage device 12, for obtaining described result data and the described diagnostic result of display.Described operation display device 14 comprises boiler and major pant item operation display device 14 and steam turbine operation display device 14.
Data collector 11 gathers the data of Power Plant, and the data of Power Plant are sent to coal-burning power plant's unit diagnostic device 13, data storage device 12 stores expertise model data, expertise model data is sent to equally in coal-burning power plant's unit diagnostic device 13, coal-burning power plant's unit diagnostic device 13 processes, and generating process result data, result data are sent to data storage device 12 store, then operation display device 14 obtains result data and shows diagnostic result from data storage device 12.
Fig. 3 is the process flow diagram of coal-burning power plant provided by the invention unit diagnostic method.As shown in Figure 3, the present invention also provides a kind of coal-burning power plant unit diagnostic method, and this diagnostic method comprises: receive Power Plant data and expertise model data; Carry out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provide failture evacuation measure according to diagnostic result.
Carry out judging to show that diagnostic result also comprises according to described Power Plant data and described expertise model data: the confidence level judging the fault that described Power Plant data represent in conjunction with described expertise model data, to draw the first diagnostic result.
Carry out judging to show that diagnostic result also comprises according to described Power Plant data and described expertise model data: utilize neural network mathematical model to process described Power Plant data and described expertise model data, judge the confidence level of the fault that described Power Plant data represent, to draw second opinion result; Utilize fuzzy logic to process described Power Plant data and described expertise model data, judge the confidence level of the fault that described Power Plant data represent, to draw the 3rd diagnostic result.
This diagnostic method also comprises: according to described first diagnostic result, described second opinion result and described 3rd diagnostic result, judges that the diagnostic result of the highest confidence level is to draw optimal diagnosis result, and provides failture evacuation measure.
By object lesson, coal-burning power plant's unit diagnostic device will be described below:
First determine that relevant phenomenon revealed by main pump, and the relation that correlated phenomena and main pump reveal is set.
The characteristic phenomenon that main pump is revealed is that system pressure reduces, flow system flow declines and distance rod movement velocity is slow.Therefore the data needed are pump discharge pressure data, pump discharge data on flows, distance rod movement velocity and distance rod load.
Be below expertise model data, all can be understood as " if ... so ... " form:
When system pressure is between 175pa-225pa, system is in pressure normal condition, and when being greater than 300pa, system enters pressure high state, and when being more than or equal to 375pa, system is in pressure high state certainly.When system pressure is less than 175pa, system starts to enter the low state of pressure, and when reaching 100pa, system is in the low state of pressure certainly;
When flow system flow is in 3.5m
3/ s-4.5m
3time between/s, system is in flow normal condition, when being greater than 7m
3during/s, system is in flow high state certainly.When flow system flow is less than 3.5m
3during/s, system starts to enter the low state of flow, when reaching 1m
3during/s, system is in the low state of flow certainly;
When distance rod movement velocity is between 0.43mm/s-0.63mm/s, distance rod movement velocity is normal, and when being greater than 0.8mm/s, movement velocity is fast.When distance rod movement velocity is in, during 0.13mm/s-0.433mm/s, distance rod movement velocity is low, and when being less than 0.17mm/s, distance rod movement velocity is extremely slow;
When distance rod load is between 8500nm-12500nm, distance rod load is in normal condition, and when being greater than 16000nm, distance rod load is in high state.When distance rod load is in 2500nm-8500nm, distance rod load is in low state, and when being less than 1500nm, distance rod load is in extremely low state certainly;
When any one is in low state when distance rod movement velocity and distance rod load, can affirm that distance rod movement velocity is slow;
When pump discharge pressure is in low state, can affirm that system pressure is low;
When pump discharge flow is in low state, can affirm that flow system flow is low;
When appearance: system pressure is low, flow system flow is low, distance rod movement velocity slow phenomenon time, substantially can certainly there is main pump leakage failure.
In expert system module, distance rod movement velocity, distance rod load, the equal correspondence one of different numerical ranges of pump discharge pressure and pump discharge flow affects the Credibility probability value of the reasoning results, and each item number according to gradually in the process of critical value Credibility probability produce and increase constantly, such as distance rod movement velocity is positioned at 0.17mm/s-0.19mm/s, when other data are normal, the Credibility probability that main pump is revealed is 22%, but when pump discharge pressure is down to 170pa-175pa, it is 24% that the Credibility probability that main pump is revealed increases, 100pa-110pa is down to once pump discharge pressure, the Credibility probability that main pump is revealed will be reached for 45%.
For pump discharge pressure data, pump discharge data on flows, distance rod movement velocity and distance rod load, after pretreatment module process, 7 groups of inputs of Power Plant data expert system module 3, neural network module 4 and fuzzy logic module 5, as shown in the table, wherein S1_1 representative pressure sensor values, S1_2 represents flow sensor values, and S6_1 represents distance rod movement velocity, and S6_2 represents distance rod load:
The Power Plant data that table 1 inputs
As shown in the 7th batch of parameter in table, can find out that system pressure is low, flow system flow is low, distance rod movement velocity slow and distance rod load is low, substantially can conclude and occur main pump leakage failure.After input expert system module 3, export the first diagnostic result Credibility probability 82%.After parameters input neural network module 4 and fuzzy logic module 5, export second opinion credible result degree probability 85% and the 3rd diagnostic result Credibility probability 89% respectively, therefore the result of fuzzy logic module 5 is got, substantially conclude and occur that fault revealed by main pump, and provide the solution that fault revealed by main pump.Can find out, for this example, expert system module 3, neural network module 4 and fuzzy logic module 5 can determine that fault revealed by main pump.
Pass through technique scheme, adopt coal-burning power plant provided by the invention unit diagnostic device, system and method, Power Plant data and expertise model data is received by receiver module, carrying out judging to draw diagnostic result according to described Power Plant data and described expertise model data by reasoning module, and provide failture evacuation measure according to diagnostic result, wherein reasoning module is expert system module, neural network module and fuzzy logic module, carry out process by expert system module and draw the first diagnostic result, carry out process by neural network module and draw second opinion result, carry out process by fuzzy logic module and draw the 3rd diagnostic result, then integration module is utilized to select the diagnostic result of the highest confidence level.The present invention accurately can promptly diagnose Power Plant fault, for production, maintenance and decision-making provide strong guarantee.
Below the preferred embodiment of the present invention is described in detail by reference to the accompanying drawings; but; the present invention is not limited to the detail in above-mentioned embodiment; within the scope of technical conceive of the present invention; can carry out multiple simple variant to technical scheme of the present invention, these simple variant all belong to protection scope of the present invention.
It should be noted that in addition, each concrete technical characteristic described in above-mentioned embodiment, in reconcilable situation, can be combined by any suitable mode, in order to avoid unnecessary repetition, the present invention illustrates no longer separately to various possible array mode.
In addition, also can carry out combination in any between various different embodiment of the present invention, as long as it is without prejudice to thought of the present invention, it should be considered as content disclosed in this invention equally.
Claims (10)
1. coal-burning power plant's unit diagnostic device, is characterized in that, this diagnostic device comprises:
Receiver module (1), for receiving Power Plant data and expertise model data;
Reasoning module (2), is connected with described receiver module (1), for carrying out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provides failture evacuation measure according to diagnostic result.
2. coal-burning power plant according to claim 1 unit diagnostic device, is characterized in that, described reasoning module (2) comprising:
Expert system module (3), is connected with described receiver module (1), for judging the confidence level of the fault that described Power Plant data represent in conjunction with described expertise model data, to draw the first diagnostic result.
3. coal-burning power plant according to claim 2 unit diagnostic device, is characterized in that, described reasoning module (2) also comprises:
Neural network module (4), be connected with described receiver module (1), for utilizing neural network mathematical model, described Power Plant data and described expertise model data are processed, judge the confidence level of the fault that described Power Plant data represent, to draw second opinion result;
Fuzzy logic module (5), be connected with described receiver module (1), for utilizing fuzzy logic, described Power Plant data and described expertise model data are processed, judge the confidence level of the fault that described Power Plant data represent, to draw the 3rd diagnostic result.
4. coal-burning power plant according to claim 3 unit diagnostic device, it is characterized in that, this diagnostic device also comprises:
Integration module (6), be connected with described expert system module (3), described neural network module (4) and described fuzzy logic module (5), for according to described first diagnostic result, described second opinion result and described 3rd diagnostic result, judge that the diagnostic result of the highest confidence level is to draw optimal diagnosis result, and provide failture evacuation measure.
5. coal-burning power plant's unit diagnostic system, is characterized in that, this diagnostic system comprises:
Data collector (11), for gathering the data of Power Plant;
Data storage device (12), for storing expertise model data and result data;
Coal-burning power plant's unit diagnostic device (13) in claim 1-4 described in any claim;
Operation display device (14), is connected with described data storage device (12), for obtaining described result data and the described diagnostic result of display.
6. coal-burning power plant according to claim 5 unit diagnostic system, it is characterized in that, described operation display device (14) comprises boiler and major pant item operation display device (14) and steam turbine operation display device (14).
7. coal-burning power plant's unit diagnostic method, is characterized in that, this diagnostic method comprises:
Receive Power Plant data and expertise model data;
Carry out judging to draw diagnostic result according to described Power Plant data and described expertise model data, and provide failture evacuation measure according to diagnostic result.
8. coal-burning power plant according to claim 7 unit diagnostic method, is characterized in that, carries out judging to show that diagnostic result also comprises according to described Power Plant data and described expertise model data:
The confidence level of the fault that described Power Plant data represent is judged, to draw the first diagnostic result in conjunction with described expertise model data.
9. coal-burning power plant according to claim 8 unit diagnostic method, is characterized in that, carries out judging to show that diagnostic result also comprises according to described Power Plant data and described expertise model data:
Utilize neural network mathematical model to process described Power Plant data and described expertise model data, judge the confidence level of the fault that described Power Plant data represent, to draw second opinion result;
Utilize fuzzy logic to process described Power Plant data and described expertise model data, judge the confidence level of the fault that described Power Plant data represent, to draw the 3rd diagnostic result.
10. coal-burning power plant according to claim 9 unit diagnostic method, it is characterized in that, this diagnostic method also comprises:
According to described first diagnostic result, described second opinion result and described 3rd diagnostic result, judge that the diagnostic result of the highest confidence level is to draw optimal diagnosis result, and provide failture evacuation measure.
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Cited By (1)
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| CN108708773A (en) * | 2018-05-11 | 2018-10-26 | 湖北华电襄阳发电有限公司 | A kind of Steam Turbine Fault Diagnosis Methods, system and device |
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Application publication date: 20160511 |