CN113703410B - Wisdom operation platform of wisdom power plant - Google Patents

Wisdom operation platform of wisdom power plant Download PDF

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CN113703410B
CN113703410B CN202111011433.3A CN202111011433A CN113703410B CN 113703410 B CN113703410 B CN 113703410B CN 202111011433 A CN202111011433 A CN 202111011433A CN 113703410 B CN113703410 B CN 113703410B
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platform
module
power plant
parameter
intelligent
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CN113703410A (en
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杨海
陈宏文
李祥
谭祥麟
陈祥师
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Guangdong Huadian Shenzhen Energy Co ltd
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Huadian International Power Co Ltd Shenzhen Co
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/418Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41875Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/33Director till display
    • G05B2219/33273DCS distributed, decentralised controlsystem, multiprocessor
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

The invention relates to the technical field of power plant operation management, in particular to an intelligent operation platform of an intelligent power plant, which comprises a supervision assistant module, an alarm analysis module and an operation assistant module, wherein the supervision assistant module is used for acquiring power plant data in real time, determining abnormal parameters in the power plant data and generating parameter abnormal alarm information for alarming; the alarm analysis module is used for receiving the alarm information, analyzing the alarm information and generating an alarm information analysis result, and the operation assistant module is used for receiving the emergency information or the fault reason and generating a fault handling guidance scheme aiming at the fault reason or the emergency information. The intelligent operation platform provided by the invention can be used for monitoring the data of the power plant, finding out abnormal parameters in time, determining the fault reasons causing the abnormal parameters, giving a fault disposal guidance scheme according to the fault reasons, improving the operation and management level of the power plant and assisting operators in fault judgment and fault disposal.

Description

Wisdom operation platform of wisdom power plant
Technical Field
The invention relates to the field of intelligent power plants, in particular to an intelligent operation platform of an intelligent power plant.
Background
With the rapid development of technologies such as computer science, data processing, artificial intelligence and the like, the requirements of intelligent operation and management of power plants are more and more urgent. The intelligent power plant intelligent management system has the advantages that various intelligent instruments and equipment are used for collecting and analyzing data, the data are uploaded to the data platform for processing, and are reprocessed in corresponding application for further utilization, so that the personnel reduction and the efficiency improvement of a power plant are finally realized, the operation and management level of the power plant is improved, and the intelligent power plant intelligent development general trend is shown.
The existing power plant operation management platform only plays a role in monitoring power plant data, and operators mainly adjust operation parameters of a power plant or deal with power plant faults by combining the power plant data with experience, so that the requirement on the capability of the operators is high, and the loss of the power plant is easily caused by the fact that the operators judge errors to cause the adjustment errors of the operation parameters or fail to effectively deal with the power plant faults.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent operation platform of an intelligent power plant.
The intelligent operation platform provided by the invention comprises a monitoring assistant module, an alarm analysis module and an operation assistant module, wherein:
the monitoring assistant module is connected with the big data platform and the alarm analysis module and used for acquiring power plant data from the big data platform in real time, determining abnormal parameters in the power plant data according to preset abnormal parameters, generating parameter abnormal alarm information and giving an alarm, wherein the power plant data comprises main equipment operation parameter data and auxiliary equipment operation parameter data;
the alarm analysis module is connected with the DCS, the patrol platform, the digital twin platform, the fault diagnosis platform and the supervision assistant module, and is used for receiving alarm information from the DCS, the patrol platform, the digital twin platform, the fault diagnosis platform and the supervision assistant module, analyzing the alarm information according to a preset alarm analysis strategy and generating an alarm information analysis result for an operator to check and confirm, wherein the alarm information analysis result comprises one or more potential fault reasons and the probability of each potential fault reason;
and the operation assistant module is connected with the alarm analysis module and the supervision assistant module and is used for receiving the emergency information or the fault reason which is sent by the alarm analysis module and confirmed by the operator, and generating a fault handling guidance scheme according to a preset fault handling guidance strategy aiming at the fault reason or the emergency information so that the operator can operate according to the fault handling guidance scheme to eliminate the fault.
Furthermore, the assistant module is operated and is also connected with the inspection platform, the maintenance platform and the intrinsic safety platform; the operation assistant module is also used for acquiring standby auxiliary machine state data from the monitoring assistant module, acquiring power plant equipment abnormal data from the inspection platform, acquiring local measures from the maintenance platform and the intrinsic safety platform, performing executable judgment on executive equipment corresponding to the periodic test plans and the soot blowing plans according to the standby auxiliary machine state data, the power plant equipment abnormal data, the local measures and preset periodic test plans and the soot blowing plans, and generating periodic work prompts according to executable judgment results to remind operators to execute periodic work.
Furthermore, the intelligent operation platform also comprises an optimizer module, and the optimizer module is connected with the operation assistant module, the digital twin platform and the big data platform;
the optimization engineer module is used for acquiring a twin simulation result from the digital twin platform, acquiring a big data analysis result of a corresponding historical optimal operating condition from the big data platform, generating parameter online adjustment guidance information according to a preset parameter adjustment guidance strategy, the twin simulation result and the big data analysis result of the historical optimal operating condition of the big data platform, and sending the parameter online adjustment guidance information to the operation assistant module;
and the operation assistant module is also used for outputting the optimized adjustment guiding information of the parameters according to the parameter online adjustment guiding information and the executable judgment result of the execution equipment corresponding to the parameter online adjustment guiding information, so as to guide the operator to perform optimized adjustment on the parameters of the execution equipment when the operator performs regular work.
Furthermore, the intelligent operation platform also comprises an evaluator module, and the evaluator module is connected with the optimizer module;
the online parameter adjustment method comprises the steps of obtaining online parameter adjustment guide information, using the online parameter adjustment guide information as an optimal operation adjustment mode of a parameter to be adjusted, obtaining an actual operation adjustment mode of the parameter to be adjusted of an operator, comparing the actual operation adjustment mode with the optimal operation adjustment mode, tracking and evaluating performance difference between the actual operation adjustment mode and the optimal operation adjustment mode in real time, generating and recording a comparison deviation result, sending the comparison deviation result to an operation assistant module, prompting the operator to compare the deviation result through the operation assistant module, sending the comparison deviation result to an optimizer module, sending the comparison deviation result to a digital twin platform and a big data platform through the optimization twin module, and assisting in optimization iteration of a twin model of the digital twin platform and updating of an analysis sample of the big data platform.
Furthermore, the evaluator module is also connected with the big data platform; the method is used for obtaining the operation analysis results of each operator from the big data platform, establishing an operator operation habit library, and providing operation improvement and promotion suggestions by combining the actual operation adjustment mode of the current operator.
Furthermore, the intelligent operation platform further comprises an intelligent control module, the intelligent control module is connected with the operation assistant module and used for carrying out one or more of intelligent automatic start-stop control of the unit, variable working condition unattended process control, cold end intelligent frequency conversion control, primary frequency modulation homologous optimization control of the gas turbine, thermoelectric load intelligent scheduling control or intelligent water island control according to parameter online adjustment guidance information output by the operation assistant.
Further, the preset abnormal parameter determination strategy is as follows: the method comprises the steps of obtaining equipment habits summarized manually and equipment habits analyzed by a big data platform, establishing an equipment habit library based on the equipment habits, the current operating conditions of the power plant and the normal operating interval of preset power plant data, and judging whether abnormal parameters exist in the power plant data according to the equipment habit library.
Further, the supervision assistant module is further used for determining a safety boundary according to equipment habits, obtaining economic working condition distribution counted by the big data platform, and drawing real-time running envelope lines or contour maps of the key parameters and the working conditions of the key equipment so that operators can master running safety margins and performance distribution of the key equipment and the key parameters.
Further, the supervision assistant module is further configured to obtain the alarm information level of the abnormal parameter according to the abnormal level of the abnormal parameter and a preset corresponding relationship between the abnormal level and the alarm information level, so as to implement a graded alarm prompt on the abnormal parameter.
Further, supervision assistant module still is used for sending the power plant data who obtains in real time to supervision terminal and generates supervision picture and show, and generate supervision reminding information and send to supervision terminal to remind the operating personnel to regularly look over supervision picture.
The intelligent operation platform of the intelligent power plant provided by the invention at least comprises the following beneficial effects: prison dish assistant module in the wisdom operation platform can real-time supervision power plant data to judge whether there is abnormal parameter in the power plant data, when having abnormal parameter, prison dish assistant module can in time generate parameter abnormal alarm information and report to the police, handles with reminding the operating personnel, need not the operating personnel and judges whether there is abnormal parameter according to the experience. In addition, an alarm analysis module in the platform can analyze alarm information to obtain fault reasons, and analyzes the fault reasons confirmed by operators to generate a fault handling guidance scheme for the operators to refer to, so that the workload of the operators is reduced, and the loss of the power plant caused by the error adjustment of operation parameters or the failure of effective handling of the power plant fault due to the error judgment of the operators is avoided. And then can realize the reduction of personnel and increase of efficiency of the power plant, improve the operation and management level of the power plant.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an intelligent operation platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the connection relationship between the intelligent operation platform and the external device according to an embodiment of the present invention; 1-an intelligent operation platform, 101-a supervision assistant module, 102-an alarm analysis module, 103-an operation assistant module, 104-an optimizer module, 105-an evaluator module, 2-a big data platform, 3-a DCS system, 4-an inspection platform, 5-a digital twin platform, 6-a fault diagnosis platform, 7-a maintenance platform and 8-an intrinsic safety platform.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In an embodiment of the present invention, as shown in fig. 1 and fig. 2, an intelligent operation platform of an intelligent power plant is disclosed, the intelligent operation platform 1 includes a supervisory assistant module 101, an alarm analysis module 102, and an operation assistant module 103, wherein:
the monitoring assistant module 101 is connected with the big data platform 2 and the alarm analysis module 102, and is used for acquiring power plant data from the big data platform 2 in real time, determining abnormal parameters in the power plant data according to preset abnormal parameters, generating parameter abnormal alarm information and giving an alarm, wherein the power plant data comprises main equipment operation parameter data, auxiliary equipment operation parameter data and system operation parameter data;
in this embodiment, the big data platform 2 is used to collect, store and analyze the power plant data in real time, and send the obtained power plant data to the supervisory assistant module 101 in real time.
Specifically, the main equipment includes a combustion engine, a steam engine, a boiler and a generator, and the main equipment operation parameter data includes combustion engine operation parameter data (for example, vibration, pressure, temperature, flow rate, rotation speed and the like of the combustion engine), steam engine operation parameter data (for example, vibration, pressure, temperature, flow rate, rotation speed and the like of the steam engine), boiler operation parameter data (for example, vibration, pressure, temperature, flow rate, rotation speed and the like of the boiler) and generator operation parameter data (for example, generator exciting current, exciting voltage, rotor current, rotor voltage, generator active power, reactive power and the like).
The monitoring assistant module 101 monitors whether the values of the parameters of the main device are within a preset normal parameter value range, and if not, determines that the parameters belong to abnormal parameters, and timely generates parameter alarm information for parameter abnormal alarm for the abnormal parameters.
The operation auxiliary equipment comprises a water feeding pump, a circulating water pump, a condensation pump, a closed pump, a vacuum pump and the like, the operation parameter data of the operation auxiliary equipment comprises a current parameter, a rotating speed parameter, a vibration parameter, a bearing temperature parameter, a cooling water temperature parameter, an oil temperature parameter, a flow parameter of a working medium, a pressure parameter, a temperature parameter and the like, similarly, a normal parameter value range can be set for each operation parameter in advance, if the operation parameter is not in the normal range, the operation auxiliary equipment is determined to belong to an abnormal parameter, and parameter abnormality alarm information is generated in time for the abnormal parameter to perform parameter abnormality alarm.
Further, in another implementation manner of this embodiment, the preset exception parameter determination policy is: the method comprises the steps of obtaining manually summarized equipment habits and equipment habits analyzed by a big data platform, and establishing an equipment habit library to judge whether abnormal parameters exist in the power plant data or not according to the equipment habit library based on the equipment habits, the current operation working condition of the power plant and a preset normal operation interval of the power plant data.
Specifically, in this embodiment, the plant habit refers to the performance and characteristics of the plant, and the operation condition refers to the production state of the production device and the plant in the power plant. The equipment habit library stores the state parameter range of each equipment in the power plant under the normal operation condition. It should be understood that the operating parameters of each of the devices in the plant are within the above-mentioned ranges of the state parameters under normal operating conditions.
Furthermore, in another implementation manner of this embodiment, the monitoring assistant module 101 is further configured to determine a safety boundary according to the equipment habit, obtain economic condition distribution counted by the big data platform, and draw a real-time operation envelope line or contour diagram of the key parameters and the working conditions of the key equipment, so that an operator can grasp operation safety margins and performance distribution of the key equipment and the key parameters.
Specifically, the safety boundary indicates that the operation parameter is unsafe when the operation parameter is within a reasonable operation range and exceeds a reasonable parameter range (i.e., the above-mentioned state parameter range of the equipment under the normal operation condition), the safety boundary is a boundary between the reasonable parameter range and an unreasonable parameter range, and the economic condition distribution indicates that the power generation equipment is in an operation mode of generating the most electric quantity under the condition of saving the production cost most.
The key device and the key parameter are determined by the user, which is not limited in the present invention.
The alarm analysis module 102 is connected with the DCS system 3, the routing inspection platform 4, the digital twin platform 5, the fault diagnosis platform 6 and the supervisory assistant module 101, and is configured to receive alarm information from the DCS system 3, the routing inspection platform 4, the digital twin platform 5, the fault diagnosis platform 6 and the supervisory assistant module 101, analyze the alarm information according to a preset alarm analysis policy, and generate an alarm information analysis result for an operator to check and confirm, where the alarm information analysis result includes one or more potential fault reasons and a probability of each potential fault reason. Besides one or more potential fault reasons, the alarm information analysis result also comprises the probability of each potential fault reason, so that an operator can refer to the probability of the potential reasons to confirm the final fault reason.
Specifically, a DCS system 3 (distributed control system) is a new-generation instrument control system based on a microprocessor and adopting a design principle of decentralized control function, centralized display operation, and consideration of both division, autonomy and comprehensive coordination, in an intelligent power plant, the DCS system 3 is used for remote control of a generator set, an inspection platform 4 is used for inspection and inspection of power plant equipment, a digital twin platform 5 is used for analog simulation, and a fault diagnosis platform 6 is used for finding faults of the equipment or the system.
In this embodiment, the alarm analysis module 102 receives alarm information of various aspects in the smart power plant, for example, DCS alarm information sent by the DCS system 3, alarm information that needs to be handled emergently and is sent by the inspection platform 4, the digital twin platform 5 and the fault diagnosis platform 6, and parameter alarm information sent by the supervisory assistant module 101, and performs anomaly and fault analysis on the alarm information according to a preset alarm analysis strategy to give a potential fault cause and a probability corresponding to the fault cause, so as to allow an operator to check and confirm the potential fault cause and the probability.
Specifically, the preset alarm analysis strategy can perform alarm analysis by taking the operation habits of the sensors, the equipment and the system in the power plant as a core, wherein the operation habits comprise the potential fault ranges of the sensors, the equipment and the system and the judgment conditions of various faults.
An operation habit library is pre-established in the alarm analysis module 102, on one hand, the operation habit library stores manually summarized operation habits, and on the other hand, the operation habit library stores large data platforms to analyze and count the operation habits according to the power plant data. The alarm analysis module 102 analyzes the reasons of the potential abnormalities and faults in the alarm information in real time according to the operation habits of the sensors, the equipment and the system, gives out potential fault reasons, and sorts the potential fault reasons according to probability for operators to manually check and confirm.
For example: under the rated working condition of the water supply system, the outlet flow of the water supply pump is consistent with the water supply flow, if the water supply flow is inconsistent with the outlet flow of the water supply pump, the internal leakage of a recirculation door can happen, the valve of the water supply system is abnormal, and the like. Similarly, a normal parameter value range can be set for each operation parameter in advance, if the operation parameter is not in the normal range, the operation parameter is determined to belong to an abnormal parameter, and parameter alarm information is generated in time for the abnormal parameter to carry out parameter abnormal alarm.
Furthermore, the operator can confirm the prompted fault or select the non-prompted 'other faults', and the confirmation result of the operator is sent to the big data platform 2 for collecting samples on line and perfecting the operation habit model through big data modeling.
Specifically, the operation habit is the operation performed by a normal operator according to an operation rule, especially a plurality of same working conditions, the adjustment modes are basically the same, and the basically same operation adjustment mode of the same working condition is used as the operation habit model corresponding to the working condition.
On the other hand, the confirmation result (fault cause signal) of the operator is sent to the operation assistant module 103 to instruct the operator to complete the handling of the fault.
And the operation assistant module 103 is connected with the alarm analysis module 102 and the monitoring assistant module 101, and is configured to receive emergency information or a fault reason that is sent by the alarm analysis module 102 and confirmed by an operator. Specifically, the emergency is sent by the platform such as the inspection platform 4, and specifically includes that the unit and the equipment in the power plant suddenly trip (lose electricity), the heater leaks (the water level is high), the operation is quitted, the regulating valve is abnormally blocked and cannot act, and the like.
And aiming at the fault reason or the emergency information, generating a fault handling guidance scheme according to a preset fault handling guidance strategy so that an operator can operate according to the fault handling guidance scheme to eliminate the fault.
Specifically, the preset fault handling guidance strategy is as follows: according to the fault reason confirmed by the alarm analysis module 102 or the emergency triggered by the inspection platform, the operation steps of the fault and the accident are guided according to the operation rules. Further, the operation assistant module can acquire the parameter change condition of the operation parameter to be adjusted during fault handling in real time, automatically monitor the parameter, display the parameter change condition in a curve form, and prompt the control range of the operation parameter to be adjusted, the related protection and the action value thereof (the related protection and the action value thereof, namely, the protective fixed value parameter, and the set protection action starts the corresponding numerical parameter to ensure that the equipment does not work beyond the allowable range).
More specifically, in this embodiment, the operation rules refer to written documents of various operations and monitoring of the crew system and equipment failures and various accidents performed by the operator of the electrical conductivity plant. The operation regulations must be written in advance before the power plant is put into production and can be implemented after being approved by a certain approval program. The method is a basic file for the operation of the power plant, ensures that all operations of the power plant are in regulation and circulation, and has very important significance for ensuring the safe operation of a unit.
According to the intelligent operation platform of the intelligent power plant, the supervision assistant module in the intelligent operation platform can monitor power plant data in real time and judge whether abnormal parameters exist in the power plant data, and when the abnormal parameters exist, the supervision assistant module can timely generate parameter abnormal alarm information to give an alarm to remind an operator to process the abnormal parameters, and the operator does not need to judge whether the abnormal parameters exist according to experience. In addition, an alarm analysis module in the platform can analyze alarm information to obtain fault reasons, and analyzes the fault reasons confirmed by operators to generate a fault handling guidance scheme for the operators to refer to, so that the workload of the operators is reduced, and the loss of the power plant caused by the error adjustment of operation parameters or the failure of effective handling of the power plant fault due to the error judgment of the operators is avoided. And furthermore, the labor reduction and the efficiency improvement of the power plant can be realized, and the operation and management level of the power plant is improved.
In another embodiment of the invention, the operation assistant module 103 is further connected with the inspection platform 4, the overhaul platform 7 and the intrinsic safety platform 8;
the maintenance platform 7 is used for taking 'synchronous monitoring, dynamic analysis, intelligent diagnosis and autonomous decision-making' as targets, focusing on large data mining of equipment state parameters, evaluating the health state of the equipment in real time, early warning and prejudging the running risk of the equipment, configuring production elements such as 'people, machines, materials, methods and rings' by self, and realizing the evolution of maintenance management means from planned maintenance, after-event maintenance to accurate maintenance and predicted maintenance. This ampere of platform 8 is used for the safety in production standardization of power plant, promotes the safety control level, and personnel, environment, equipment risk initiative precontrol are solved, and personnel's action is standardized, stops personnel's occurence of failure.
The operation assistant module 103 is further configured to obtain state data of the standby auxiliary machine from the supervision assistant module 101, obtain abnormal data of the power plant equipment from the inspection platform 4, obtain local measures (the local measures refer to safety measures that cannot be performed through remote operation and need to be performed locally) from the inspection platform 7 and the intrinsic safety platform 8, perform executable determination on the execution devices corresponding to the periodic test plan and the soot blowing plan according to the state data of the standby auxiliary machine, the abnormal data of the power plant equipment, the local measures, and a preset periodic test plan and a preset soot blowing plan, and generate a periodic work prompt according to an executable determination result to remind an operator to perform periodic work.
Specifically, in this embodiment, the periodic test plan is to make periodic work of the equipment, so that the fault and hidden danger of the equipment can be found in time, and the precautionary measures can be processed or made in time, thereby ensuring the normal standby of the standby equipment and the long-term safe and reliable operation of the operating equipment. The regular test plan can be revised in real time according to the operating condition of the power plant and the execution condition of regular work, and the soot blowing plan is as follows: during the operation of the boiler burning the solid fuel, the accumulated dust on the heating surface is periodically blown off to improve the heat transfer efficiency and keep the original output of the boiler.
And performing executable judgment on the execution equipment corresponding to the periodic test plan or the soot blowing plan, namely judging whether the execution equipment meets preset executable conditions, if so, forming a periodic work prompt of the periodic test plan, and reminding operators to execute the periodic work under an allowed condition (namely, under the condition of meeting the operation rule).
In another embodiment of the present invention, the intelligent operation platform further comprises an optimizer module 104, wherein the optimizer module 104 is connected with the operation assistant module 103, the digital twin platform 5 and the big data platform 2;
the optimizer module 104 is configured to obtain a twin simulation result from the digital twin platform 5, obtain a big data analysis result of the corresponding historical optimal operating condition from the big data platform 2, generate parameter online adjustment guidance information according to the twin simulation result and the big data analysis result of the historical optimal operating condition of the big data platform, and send the parameter online adjustment guidance information to the operation assistant module 103;
specifically, the twin simulation result is a result obtained by simulating a real situation by using a digital twin platform, and the digital twin platform integrates a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process by using data such as a physical model, sensor updating, operation history and the like, and completes mapping in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected.
The big data analysis result of the historical optimum operating condition means that the best operating data is extracted from the same previous operation as the historical optimum operating condition. Specifically, historical operation data is stored in the big data platform 2, and the optimizer module 104 extracts the optimal operation data from the historical operation data as a big data analysis result of the historical optimal operation condition.
The parameter online adjustment guidance information includes specific adjustment modes of the operation parameters to be adjusted.
The operation assistant module 103 is further configured to output parameter optimization adjustment guidance information according to the parameter online adjustment guidance information and an executable determination result (the executable determination result is generated according to the regular work, the device state, and the executable condition determination) of the execution device corresponding to the parameter online adjustment guidance information, so as to guide an operator to perform optimization adjustment on the parameter of the execution device when the operator performs the regular work, thereby achieving optimal safety, economy, and environmental protection.
Further, in this embodiment, key parameters that need to be optimized and adjusted, such as steam temperature, wall temperature, NOx emission concentration, and the like, may be preset by a user, and the optimizer module 104 generates parameter online adjustment guidance information only for the key parameters, so that the operation assistant module 103 outputs the optimized and adjusted guidance information of the key parameters according to the parameter online adjustment guidance information of the key parameters and the executable determination result of the corresponding execution device. The key parameters are preset, so that the optimizer module 104 only analyzes the key parameters, the occupation of resources of the optimizer module 104 can be reduced, the analysis efficiency of the optimizer module 104 is improved, and the analysis speed is improved.
In another embodiment of the present invention, as shown in fig. 1, the intelligent operation platform further includes an evaluator module 105, the evaluator module is connected to the optimizer module 105;
the system comprises a parameter online adjustment guiding information acquisition module, an optimization deviation module, an operation assistant module, an optimization deviation module, a digital twin platform and a big data platform, wherein the parameter online adjustment guiding information acquisition module is used for acquiring parameter online adjustment guiding information, the parameter online adjustment guiding information is used as an optimal operation adjustment mode of parameters to be adjusted, the actual operation adjustment mode of the parameters to be adjusted of an operator is acquired, the actual operation adjustment mode of the parameters to be adjusted is compared with the optimal operation adjustment mode, the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode is tracked and evaluated in real time, a comparison deviation result is generated and recorded, the comparison deviation result is sent to the operation assistant module, the operator is prompted to compare the deviation result through the operation assistant module, meanwhile, the comparison deviation result is sent to the optimizer module, the optimizer module sends the comparison deviation result to the digital twin platform and the big data platform, and the optimization iteration of a twin model of the digital twin platform and the updating of an analysis sample of the big data platform are assisted.
In yet another embodiment of the present invention, the evaluator module 105, as shown in FIG. 2, is also connected to the big data platform 2; and the system is used for acquiring the operation analysis results of each operator from the big data platform 2.
For example, the analysis of the operation behavior of the operator, such as many actual operations, large deviation of the result (which means that the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode is large), and importance (such as affecting safety, economy, and environmental protection).
The specific analysis process is to compare the same operation, extract the best operation parameter, analyze the best operation parameter, and obtain the analysis result of safety, economy and environmental protection.
And establishing an operation habit library of operators based on the operation analysis result, and providing an operation improvement and promotion suggestion by combining the actual operation adjustment mode of the current operators.
Specifically, the operation data is stored in the big data platform, the big data platform analyzes the operation data, and an operation analysis result (operation habit) is generated, so that an operation habit library is established, the operation habit library stores the operation habits, and the operation habits comprise the optimal operation of normal operation adjustment, regular work, unit start and stop, and accident exception handling.
The evaluator module 105 compares the actual operation adjustment mode with the optimal operation adjustment mode in the operator behavior library, and feeds the actual operation adjustment mode back to the operation assistant module and the optimizer module, and the operation assistant module and the optimizer module give an optimization operation adjustment suggestion.
For example: when the waste heat boiler is in a rated working condition, operators are advised to control the temperature of the main steam to reach the rated temperature by adjusting the temperature-reducing water to a set flow.
In another embodiment of the present invention, the intelligent operation platform further includes an intelligent control module 106, and the intelligent control module 106 is connected to the operation assistant module 103 and is configured to perform one or more of intelligent automatic start-stop control of the unit, variable-operating-condition unattended process control, cold-end intelligent frequency conversion control, primary frequency modulation homologous optimization control of the gas turbine, thermoelectric load intelligent scheduling control, or intelligent water island control according to the parameter online adjustment guidance information output by the operation assistant module 103.
According to the intelligent operation platform provided by the invention, the intelligent automatic start-stop control of the unit, the variable working condition unattended process control, the intelligent variable frequency control of the cold end, the primary frequency modulation homologous optimization control of the gas turbine, the intelligent scheduling control of the thermoelectric load and the intelligent water island control are realized through the intelligent control module 106, so that the intelligent operation platform is more intelligent, the operation management level of a power plant is further improved, the work of operators is reduced, and the work efficiency of the operators is improved.
In another embodiment of the present invention, the monitoring assistant module 101 is further configured to obtain an alarm information level of the abnormal parameter according to the abnormal level of the abnormal parameter and a preset correspondence between the abnormal level and the alarm information level, so as to implement a graded alarm prompt on the abnormal parameter. Further, the monitoring assistant module 101 collects alarm information of abnormal parameters according to a system and equipment, and performs quantitative safety scoring.
In another embodiment of the present invention, the monitoring assistant module 101 is further configured to send the power plant data obtained in real time to the monitoring terminal to generate a monitoring screen for displaying, and generate monitoring reminding information to be sent to the monitoring terminal to remind an operator to check the monitoring screen regularly. In this embodiment, the monitoring assistant module 101 replaces the original operation record form copying work to remind the operator (operating personnel) to turn over each system picture regularly, so that the operator (operating personnel) can find out the parameter abnormality in time. Furthermore, parameter index analysis such as the change rate, the change amplitude, the fluctuation frequency and the like of preset important parameters is displayed in the monitoring picture, so that operating personnel can find the abnormal trend of the parameters in time. Furthermore, the monitoring assistant module 101 stores the power plant parameters according to a time sequence, so that a user can call and analyze the abnormal conditions of the historical parameters at any time through the monitoring terminal.
The terms and expressions used in the specification of the present invention have been set forth for illustrative purposes only and are not meant to be limiting. It will be appreciated by those skilled in the art that changes could be made to the details of the above-described embodiments without departing from the underlying principles thereof. The scope of the invention is, therefore, to be determined only by the following claims, in which all terms are to be interpreted in their broadest reasonable sense unless otherwise indicated.

Claims (9)

1. An intelligent operation platform of an intelligent power plant is characterized in that,
wisdom operation platform including supervision disc assistant module, alarm analysis module, optimization teacher's module and operation assistant module, wherein:
the monitoring assistant module is connected with the big data platform and the alarm analysis module and used for acquiring power plant data from the big data platform in real time, determining abnormal parameters in the power plant data according to a preset abnormal parameter determination strategy, and generating parameter abnormal alarm information to give an alarm, wherein the power plant data comprises main equipment operation parameter data and auxiliary equipment operation parameter data;
the alarm analysis module is connected with the DCS system, the inspection platform, the digital twin platform, the fault diagnosis platform and the monitoring assistant module, and is used for receiving alarm information from the DCS system, the inspection platform, the digital twin platform, the fault diagnosis platform and the monitoring assistant module, analyzing the alarm information according to a preset alarm analysis strategy, and generating an alarm information analysis result for operators to check and confirm, wherein the alarm information analysis result comprises one or more potential fault reasons and the probability of each potential fault reason;
the optimization engineer module is connected with the operation assistant module, the digital twin platform and the big data platform and is used for acquiring a twin simulation result from the digital twin platform, acquiring a big data analysis result of a corresponding historical optimal operation condition from the big data platform, generating parameter online adjustment guidance information according to a preset parameter adjustment guidance strategy, the twin simulation result and the big data analysis result of the historical optimal operation condition of the big data platform, and sending the parameter online adjustment guidance information to the operation assistant module;
the operation assistant module is connected with the alarm analysis module and the supervision assistant module, and is configured to receive emergency information or a fault reason sent by the alarm analysis module and confirmed by an operator, generate a fault handling guidance scheme according to a preset fault handling guidance strategy for the fault reason or the emergency information, so that the operator performs operation according to the fault handling guidance scheme to eliminate a fault, and output parameter optimization adjustment guidance information according to the parameter online adjustment guidance information and an executable judgment result of an execution device corresponding to the parameter online adjustment guidance information, so as to guide the operator to perform optimization adjustment on a parameter of the execution device when the operator performs regular work.
2. The intelligent operation platform of the intelligent power plant according to claim 1, wherein the operation assistant module is further connected with an inspection platform, a maintenance platform and an intrinsic safety platform;
the operation assistant module is also used for acquiring standby auxiliary machine state data, acquiring power plant equipment abnormal data from a patrol platform, acquiring local measures from a maintenance platform and an intrinsic safety platform, performing executable judgment on executive equipment corresponding to the local measures, performing regular work prompt according to the executable judgment result, and reminding operators to perform regular work.
3. The intelligent operation platform of the intelligent power plant according to claim 1, further comprising an evaluator module, the evaluator module being connected to the optimizer module;
the online parameter adjustment system comprises an optimization engineer module, a digital twin platform, a large data platform, a parameter online adjustment guidance information acquisition module, an actual operation adjustment mode, a comparison deviation result and an operation assistant module, wherein the actual operation adjustment mode is used for acquiring the parameter online adjustment guidance information, the parameter online adjustment guidance information is used as the optimal operation adjustment mode of a parameter to be adjusted, the actual operation adjustment mode of the parameter to be adjusted of an operator is acquired, the actual operation adjustment mode is compared with the optimal operation adjustment mode, the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode is tracked and evaluated in real time, the comparison deviation result is generated and recorded, the comparison deviation result is sent to the operation assistant module, the operator is prompted to compare the deviation result through the operation assistant module, the comparison deviation result is sent to the optimization engineer module, the optimization engineer module sends the comparison deviation result to the digital twin platform and the large data platform, and the optimization iteration of the twin model of the digital twin platform and the analysis sample updating of the large data platform are assisted.
4. The intelligent operation platform of the intelligent power plant according to claim 3, wherein the evaluator module is further connected to the big data platform;
the operation behavior database is used for acquiring operation analysis results of each operator from the big data platform, establishing an operator operation behavior library, and providing operation improvement and promotion suggestions by combining the actual operation adjustment mode of the current operator.
5. The intelligent operation platform of the intelligent power plant according to claim 1, further comprising an intelligent control module, wherein the intelligent control module is connected to the operation assistant module, and is configured to perform one or more of unit intelligent automatic start-stop control, variable-operating-condition unattended process control, cold-end intelligent frequency conversion control, gas turbine primary frequency modulation homologous optimization control, thermoelectric load intelligent scheduling control or intelligent water island control according to the parameter online adjustment guidance information output by the operation assistant.
6. The intelligent operation platform of the intelligent power plant according to claim 1, wherein the preset abnormal parameter determination policy is: the method comprises the steps of obtaining equipment habits summarized artificially and equipment habits analyzed by a big data platform, establishing an equipment habit library based on the equipment habits, the current operation condition of a power plant and presetting of the normal operation interval of the data of the power plant, and judging whether abnormal parameters exist in the data of the power plant according to the equipment habit library.
7. The intelligent operation platform of the intelligent power plant according to claim 6, wherein the supervision assistant module is further configured to determine a safety boundary according to equipment habits, obtain economic condition distribution counted by the big data platform, and draw a real-time operation envelope or contour map of key parameters and key equipment conditions, so that an operator can master the operation safety margin and performance distribution of key equipment and key parameters.
8. The intelligent operation platform of an intelligent power plant according to claim 1, wherein the supervision assistant module is further configured to obtain an alarm information grade of an abnormal parameter according to an abnormal level of the abnormal parameter and a preset corresponding relationship between the abnormal level and the alarm information grade, so as to achieve a graded alarm prompt for the abnormal parameter.
9. The intelligent operation platform of the intelligent power plant according to claim 1, wherein the supervision assistant module is further configured to send the power plant data acquired in real time to the supervision terminal to generate a supervision picture for display, generate supervision reminding information, and send the supervision reminding information to the supervision terminal to remind an operator to check the supervision picture regularly.
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