CN112001096A - Real-time simulation-based power production operation deduction method - Google Patents
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Abstract
The invention discloses an electric power production operation deduction method based on real-time simulation, which belongs to the field of electric power and comprises the steps of acquiring production data in real time, marking abnormal indexes, combining multiple adjustment schemes, deducting adjustment results in real time, screening feasible adjustment schemes, recording adjustment operations and storing the adjustment operations in a historical case library, so that the technical problems of real-time data acquisition and synchronization of simulation working conditions and field operation working conditions are solved. Problems can be found in time when equipment is changed, equipment is aged, equipment is abnormal or control logic is modified, the simulation model is updated, and the situation that simulation data lose reference value due to the difference of the simulation model is avoided.
Description
Technical Field
The invention belongs to the technical field of electric power, and relates to an electric power production operation deduction method based on real-time simulation.
Background
With the rapid development of electric power technology, automatic control technology and big data technology, simulation systems have been widely used in military affairs, national defense, colleges and universities, research institutions and the first production line to factories, mines, enterprises and the like. The primary purpose of power production import simulation is to train qualified operators and improve the safety and reliability of power production. The simulation system firstly divides the whole power system into component levels according to the medium properties and the process characteristics, compiles simulation modules for each component according to the thermodynamic characteristics, and associates the simulation modules to build an integral simulation system model according to the actual conditions of the unit. The operation characteristics of the simulation system are consistent with those of a real power system, the performance and parameter change rules of the simulation system are analyzed, and reference is provided for operation control and adjustment of the unit.
At present, the simulation deduction analysis depends on human experience, a digital instruction needs to be manually input, and the deduction result needs to be manually verified and is mainly performed off-line.
Disclosure of Invention
The invention aims to provide a real-time simulation-based electric power production operation deduction method, which solves the technical problems of real-time data acquisition and synchronization of simulation working conditions and field operation working conditions.
In order to achieve the purpose, the invention adopts the following technical scheme:
a real-time simulation-based power production operation deduction method comprises the following steps:
step 1: establishing an electric power production operation deduction system, wherein the electric power production operation deduction system comprises a data acquisition server, an identification server and a simulation server;
step 2: the data acquisition server monitors the production process of the power system by acquiring the operation parameters of the power unit in the power system in real time to acquire real-time power production data;
and step 3: the identification server identifies the real-time power production data and marks an abnormal index A in the data, and the method comprises the following steps:
step A1: classifying the real-time power production data into different indexes, and establishing a historical operation database of each index;
step A2: counting upper and lower threshold values of corresponding indexes according to a historical operation database of each index, and setting an abnormal index according to the upper and lower threshold values;
step A3: when the real-time power production data accord with the abnormal index, marking the abnormal data of the real-time power production data, marking the corresponding index as an abnormal index A, and extracting the name, the abnormal occurrence time Ta and the numerical value Va of the abnormal index;
and 4, step 4: judging the abnormal reason of the abnormal index A, and determining whether the equipment fault or the running state needs to be adjusted according to the comparison between the real-time data of the abnormal index and the simulation data;
introducing all real-time power production data corresponding to the abnormal occurrence time Ta into a real-time simulation system, performing short-time simulation calculation, and comparing the numerical value Sa of the abnormal index A in the simulation result with the numerical value Va of the abnormal index A in the real-time power production data after the simulation process is converged and stabilized:
when the deviation between the two is larger than a certain range, the deviation between the equipment part simulation model and the field equipment is considered, the aging or fault phenomenon of the current equipment is considered, the fault reason is obtained through a fault diagnosis library, and a shutdown or output reduction maintenance instruction is given; when the deviation between the simulation model and the field condition is smaller than or equal to a certain range, the simulation model is considered to be basically consistent with the field condition, the operation needs to be adjusted, a simulation deduction stage is entered through the simulation server, and the operation is adjusted;
and 5: and recording and adjusting operation, storing the operation into a historical case library, recording operation steps and results of operators, and keeping the operation steps and results in the historical case library.
Preferably, the upper and lower thresholds of the index are also set by manual customization.
Preferably, when the real-time power generation data is not within the upper and lower thresholds at the time of executing the step a3, the real-time power generation data is determined to be abnormal data.
Preferably, when step 4 is executed, the specific operation steps in the real-time simulation deduction stage are as follows:
step S1: acquiring a related controllable parameter group B {1,2,3, … n }, searching related parameters in an expert database and a historical case database according to the name and the numerical value Va of the abnormal index A, and acquiring the related controllable parameter group and the abnormal reason of the abnormal index;
step S2: combining a plurality of adjustment schemes based on the associated controllable parameter sets, randomly changing the numerical value and the number of the controllable parameter sets, and combining the numerical values and the number of the controllable parameter sets into a plurality of adjustment schemes of the abnormal indexes;
step S3: deducing the adjustment result in real time, introducing all real-time production data of abnormal occurrence time into a real-time simulation system, applying the adjustment scheme generated in the step S2, doubling the simulation, parallelly deducing various adjustment schemes, listing an adjustment deduction result group comprising a target working condition, a control mode and time required for reaching the target working condition;
step S4: screening a feasible adjustment scheme, wherein the adjustment scheme that the target working condition can be recovered to the normal working condition in the adjustment deduction result is a production operation guidance scheme; the first groups of schemes with short time for reaching the target working condition are preferred production operation guide schemes; if the deduction result can not be recovered to the normal working condition, the step S2 is repeated.
The invention relates to a real-time simulation-based electric power production operation deduction method, which solves the technical problems of real-time data acquisition and synchronization of simulation working conditions and field operation working conditions. Problems can be found in time when equipment is changed, equipment is aged, equipment is abnormal or control logic is modified, the simulation model is updated, and the situation that simulation data lose reference value due to the difference of the simulation model is avoided. And multiple concurrent accelerated deductions are performed, an optimal parameter adjustment scheme for deduction is given in a short time, operation guidance is provided in real time, and operators verify and execute the adjustment scheme, so that the negative influence of manual misoperation on the safety of the power system is reduced. By deducing the future working condition, the operation and management personnel master the fault or abnormal development trend of the power generation equipment in advance in the simulation process, the system provides operation guidance which is subjected to deduction and verification and prepares enough time for adjustment, so that the fault is avoided, and the accident loss is reduced.
Drawings
FIG. 1 is a main flow of the real-time simulation production operation deduction of the present invention;
fig. 2 is a real-time simulation deduction process of the present invention.
Detailed Description
1-2, a method for deriving an electric power production operation based on real-time simulation comprises the following steps:
step 1: establishing an electric power production operation deduction system, wherein the electric power production operation deduction system comprises a data acquisition server, an identification server and a simulation server;
step 2: the data acquisition server monitors the production process of the power system by acquiring the operation parameters of the power unit in the power system in real time to acquire real-time power production data;
and step 3: the identification server identifies the real-time power production data and marks an abnormal index A in the data, and the method comprises the following steps:
step A1: classifying the real-time power production data into different indexes, and establishing a historical operation database of each index;
step A2: counting upper and lower threshold values of corresponding indexes according to a historical operation database of each index, and setting an abnormal index according to the upper and lower threshold values;
step A3: when the real-time power production data accord with the abnormal index, marking the abnormal data of the real-time power production data, marking the corresponding index as an abnormal index A, and extracting the name, the abnormal occurrence time Ta and the numerical value Va of the abnormal index;
and 4, step 4: judging the abnormal reason of the abnormal index A, and determining whether the equipment fault or the running state needs to be adjusted according to the comparison between the real-time data of the abnormal index and the simulation data;
introducing all real-time power production data corresponding to the abnormal occurrence time Ta into a real-time simulation system, performing short-time simulation calculation, and comparing the numerical value Sa of the abnormal index A in the simulation result with the numerical value Va of the abnormal index A in the real-time power production data after the simulation process is converged and stabilized:
when the deviation between the two is larger than a certain range, the deviation between the equipment part simulation model and the field equipment is considered, the aging or fault phenomenon of the current equipment is considered, the fault reason is obtained through a fault diagnosis library, and a shutdown or output reduction maintenance instruction is given; when the deviation between the simulation model and the field condition is smaller than or equal to a certain range, the simulation model is considered to be basically consistent with the field condition, the operation needs to be adjusted, a simulation deduction stage is entered through the simulation server, and the operation is adjusted;
and the operator verifies and selects the adjustment scheme, and selects whether to adopt the production operation guidance scheme or which production operation guidance scheme to execute according to own experience.
And 5: and recording and adjusting operation, storing the operation into a historical case library, recording operation steps and results of operators, and keeping the operation steps and results in the historical case library.
Preferably, the upper and lower thresholds of the index are also set by manual customization.
Preferably, when the real-time power generation data is not within the upper and lower thresholds at the time of executing the step a3, the real-time power generation data is determined to be abnormal data.
Preferably, when step 4 is executed, the specific operation steps in the real-time simulation deduction stage are as follows:
step S1: acquiring a related controllable parameter group B {1,2,3, … n }, searching related parameters in an expert database and a historical case database according to the name and the numerical value Va of the abnormal index A, and acquiring the related controllable parameter group and the abnormal reason of the abnormal index;
the expert database records the phenomena, reasons and processing guide rules of typical abnormal accident cases of the same unit, and the historical case database records the abnormal accident cases and processing schemes of the current unit over the years. Searching related cases in an expert database and a historical case database, listing related parameters B influencing the index A, and taking B {1,2,3, … n } as A associated controllable parameter groups.
Step S2: combining a plurality of adjustment schemes based on the associated controllable parameter sets, randomly changing the numerical value and the number of the controllable parameter sets, and combining the numerical values and the number of the controllable parameter sets into a plurality of adjustment schemes of the abnormal indexes;
in this embodiment, associated controllable parameter values are randomly set, for example, B {1} normal adjustment range is 0 to 10, and 10 values [3.42, 3.35, 8.64, 5.09, 1.98, 2.03, 2.28, 7.66, 5.86, 5.14] are randomly generated. The associated controllable parameter sets are randomly combined, e.g., if there are three parameters, then B may be combined as B {1}, B {2}, B {3}, B {1,2}, B {2,3}, B {1,2,3 }.
Step S3: deducing the adjustment result in real time, introducing all real-time production data of abnormal occurrence time into a real-time simulation system, applying the adjustment scheme generated in the step S2, doubling the simulation, parallelly deducing various adjustment schemes, listing an adjustment deduction result group comprising a target working condition, a control mode and time required for reaching the target working condition;
step S4: screening a feasible adjustment scheme, wherein the adjustment scheme that the target working condition can be recovered to the normal working condition in the adjustment deduction result is a production operation guidance scheme; the first groups of schemes with short time for reaching the target working condition are preferred production operation guide schemes; if the deduction result can not be recovered to the normal working condition, the step S2 is repeated.
In this embodiment, if the repeated deduction results cannot be restored to the normal working condition, a "feasible solution is not deduced" is prompted.
The invention introduces the idea of simulation deduction through data real-time acquisition and synchronization of the simulation working condition and the field operation working condition, and provides operation guidance for operators in real time in the field production operation process; the method supports real-time simulation, acceleration of a simulation process and high concurrency deduction, allows a plurality of component control parameters to be adjusted simultaneously, randomly combines the parameter size number to generate an adjustment scheme, and simulates a plurality of schemes simultaneously. The simulation process is allowed to be accelerated by 10 times or even higher, target working conditions under various combinations, control modes and time required for reaching the target working conditions are given in a short time, comprehensive comparison is convenient for operators, an optimal parameter adjusting scheme is selected and applied, intelligent operation of a unit is realized, and the production and operation level of enterprises is improved.
The invention relates to a real-time simulation-based electric power production operation deduction method, which solves the technical problems of real-time data acquisition and synchronization of simulation working conditions and field operation working conditions. Problems can be found in time when equipment is changed, equipment is aged, equipment is abnormal or control logic is modified, the simulation model is updated, and the situation that simulation data lose reference value due to the difference of the simulation model is avoided. And multiple concurrent accelerated deductions are performed, an optimal parameter adjustment scheme for deduction is given in a short time, operation guidance is provided in real time, and operators verify and execute the adjustment scheme, so that the negative influence of manual misoperation on the safety of the power system is reduced. By deducing the future working condition, the operation and management personnel master the fault or abnormal development trend of the power generation equipment in advance in the simulation process, the system provides operation guidance which is subjected to deduction and verification and prepares enough time for adjustment, so that the fault is avoided, and the accident loss is reduced.
Claims (1)
1. A real-time simulation-based power production operation deduction method is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing an electric power production operation deduction system, wherein the electric power production operation deduction system comprises a data acquisition server, an identification server and a simulation server;
step 2: the data acquisition server monitors the production process of the power system by acquiring the operation parameters of the power unit in the power system in real time to acquire real-time power production data;
and step 3: the identification server identifies the real-time power production data and marks an abnormal index A in the data, and the method comprises the following steps:
step A1: classifying the real-time power production data into different indexes, and establishing a historical operation database of each index;
step A2: counting upper and lower threshold values of corresponding indexes according to a historical operation database of each index, and setting an abnormal index according to the upper and lower threshold values;
step A3: when the real-time power production data accord with the abnormal index, marking the abnormal data of the real-time power production data, marking the corresponding index as an abnormal index A, and extracting the name, the abnormal occurrence time Ta and the numerical value Va of the abnormal index;
when the real-time power production data is not within the upper and lower thresholds in the execution of the step a3, determining that the real-time power production data is abnormal data;
and 4, step 4: judging the abnormal reason of the abnormal index A, and determining whether the equipment fault or the running state needs to be adjusted according to the comparison between the real-time data of the abnormal index and the simulation data;
introducing all real-time power production data corresponding to the abnormal occurrence time Ta into a real-time simulation system, performing short-time simulation calculation, and comparing the numerical value Sa of the abnormal index A in the simulation result with the numerical value Va of the abnormal index A in the real-time power production data after the simulation process is converged and stabilized:
when the deviation between the two is larger than a certain range, the deviation between the equipment part simulation model and the field equipment is considered, the aging or fault phenomenon of the current equipment is considered, the fault reason is obtained through a fault diagnosis library, and a shutdown or output reduction maintenance instruction is given; when the deviation between the simulation model and the field condition is smaller than or equal to a certain range, the simulation model is considered to be basically consistent with the field condition, the operation needs to be adjusted, a simulation deduction stage is entered through the simulation server, and the operation is adjusted;
step S1: acquiring a related controllable parameter group B {1,2,3, … n }, searching related parameters in an expert database and a historical case database according to the name and the numerical value Va of the abnormal index A, and acquiring the related controllable parameter group and the abnormal reason of the abnormal index;
step S2: combining a plurality of adjustment schemes based on the associated controllable parameter sets, randomly changing the numerical value and the number of the controllable parameter sets, and combining the numerical values and the number of the controllable parameter sets into a plurality of adjustment schemes of the abnormal indexes;
step S3: deducing the adjustment result in real time, introducing all real-time production data of abnormal occurrence time into a real-time simulation system, applying the adjustment scheme generated in the step S2, doubling the simulation, parallelly deducing various adjustment schemes, listing an adjustment deduction result group comprising a target working condition, a control mode and time required for reaching the target working condition;
step S4: screening a feasible adjustment scheme, wherein the adjustment scheme that the target working condition can be recovered to the normal working condition in the adjustment deduction result is a production operation guidance scheme; the first groups of schemes with short time for reaching the target working condition are preferred production operation guide schemes; if the deduction result can not be recovered to the normal working condition, repeating the step S2;
and 5: recording and adjusting operation, storing the operation into a historical case library, recording operation steps and results of operators, and keeping the operation steps and results in the historical case library;
the upper and lower thresholds of the index are also set by manual customization.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283744A (en) * | 2021-05-24 | 2021-08-20 | 国网上海市电力公司 | Design and updating method for lightweight power consumption abnormal characteristic fingerprint database |
| CN113806967A (en) * | 2021-10-18 | 2021-12-17 | 广东英达思迅智能制造有限公司 | Missing equipment data simulation method and system based on Internet of things and storage medium |
| CN114626309A (en) * | 2022-05-12 | 2022-06-14 | 江苏未来智慧信息科技有限公司 | End difference coal consumption optimization and adjustment method for high-pressure heater system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101945009A (en) * | 2010-09-14 | 2011-01-12 | 国网电力科学研究院 | Positioning method and device of power communication network fault based on case and pattern matching |
| CN103941207A (en) * | 2014-03-03 | 2014-07-23 | 广州供电局有限公司 | Electric power measurement automation terminal detection method and system |
| CN104133850A (en) * | 2014-07-07 | 2014-11-05 | 国家电网公司 | Power distribution network fault simulation analysis method based on historical operating data screening technology |
| CN104766138A (en) * | 2015-03-27 | 2015-07-08 | 大唐淮南洛河发电厂 | Thermal power plant equipment property evaluation and early warning method and system based on industrial internet |
| CN111818183A (en) * | 2020-08-31 | 2020-10-23 | 江苏未来智慧信息科技有限公司 | Power production working condition monitoring method based on equipment characteristics |
-
2020
- 2020-10-29 CN CN202011175971.1A patent/CN112001096B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101945009A (en) * | 2010-09-14 | 2011-01-12 | 国网电力科学研究院 | Positioning method and device of power communication network fault based on case and pattern matching |
| CN103941207A (en) * | 2014-03-03 | 2014-07-23 | 广州供电局有限公司 | Electric power measurement automation terminal detection method and system |
| CN104133850A (en) * | 2014-07-07 | 2014-11-05 | 国家电网公司 | Power distribution network fault simulation analysis method based on historical operating data screening technology |
| CN104766138A (en) * | 2015-03-27 | 2015-07-08 | 大唐淮南洛河发电厂 | Thermal power plant equipment property evaluation and early warning method and system based on industrial internet |
| CN111818183A (en) * | 2020-08-31 | 2020-10-23 | 江苏未来智慧信息科技有限公司 | Power production working condition monitoring method based on equipment characteristics |
Non-Patent Citations (1)
| Title |
|---|
| 史兴领 等: "电厂设备在线监测与故障预警***的设计与实现", 《电力信息与通信技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283744A (en) * | 2021-05-24 | 2021-08-20 | 国网上海市电力公司 | Design and updating method for lightweight power consumption abnormal characteristic fingerprint database |
| CN113806967A (en) * | 2021-10-18 | 2021-12-17 | 广东英达思迅智能制造有限公司 | Missing equipment data simulation method and system based on Internet of things and storage medium |
| CN113806967B (en) * | 2021-10-18 | 2023-02-10 | 广东英达思迅智能制造有限公司 | Missing equipment data simulation method and system based on Internet of things and storage medium |
| CN114626309A (en) * | 2022-05-12 | 2022-06-14 | 江苏未来智慧信息科技有限公司 | End difference coal consumption optimization and adjustment method for high-pressure heater system |
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