CN113703410A - Wisdom operation platform of wisdom power plant - Google Patents
Wisdom operation platform of wisdom power plant Download PDFInfo
- Publication number
- CN113703410A CN113703410A CN202111011433.3A CN202111011433A CN113703410A CN 113703410 A CN113703410 A CN 113703410A CN 202111011433 A CN202111011433 A CN 202111011433A CN 113703410 A CN113703410 A CN 113703410A
- Authority
- CN
- China
- Prior art keywords
- platform
- power plant
- intelligent
- module
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total 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], computer integrated manufacturing [CIM]
- G05B19/41875—Total 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], computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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
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 a power plant 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 system, the inspection platform, the digital twin platform, the fault diagnosis platform and the supervision 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 supervision 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 possibility 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 executing 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, the preset periodic test plans and the preset soot blowing plans, and generating periodic work prompts according to executable judgment results to remind operators to execute the 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 guide information of the parameters according to the parameter online adjustment guide information and the executable judgment result of the execution equipment corresponding to the parameter online adjustment guide 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 system is used for acquiring parameter online adjustment guide information, taking the parameter online adjustment guide information as an optimal operation adjustment mode of a parameter to be adjusted, acquiring 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 the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode in real time, generating and recording a comparison deviation result, and the comparison deviation result is sent to the operation assistant module so as to prompt the operator to compare the deviation result through the operation assistant module, meanwhile, the comparison deviation result is sent to the optimizer module which sends the comparison deviation result to the digital twin platform and the big data platform, so as to assist the twin model optimization iteration of the digital twin platform and the analysis sample update of the large 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 policy is as follows: the method comprises the steps of obtaining manually summarized equipment habits and equipment habits analyzed by a big data platform, establishing an equipment habit library based on the equipment habits, the current operation condition of the power plant and the normal operation 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 monitoring assistant module 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 corresponding relationship between the abnormal level and the alarm information level, so as to implement a graded alarm prompt for the abnormal parameter.
Further, the supervision plate assistant module is further used for sending the power plant data acquired in real time to the supervision plate terminal to generate a supervision plate picture for displaying, generating supervision plate reminding information and sending the supervision plate reminding information to the supervision plate terminal so as to remind an operator to check the supervision plate picture regularly.
The intelligent operation platform of the intelligent power plant provided by the invention at least comprises the following beneficial effects: the prison plate 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 prison plate assistant module can generate parameter abnormal alarm information in time to give an alarm so as to remind operators to process the abnormal parameters, and the operators are not required 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 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 a connection relationship between an intelligent operation platform and an 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 configured to collect, store, and analyze 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 data is not in the normal range, the operation auxiliary equipment is determined to belong to an abnormal parameter, and parameter abnormality alarm is carried out on abnormal parameter alarm information generated in time by the abnormal parameter.
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 behavior refers to the performance and characteristics of the plant, and the operation condition refers to the state of the production device and the production operation of 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 master the operation safety margin 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 devices and key parameters 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 possibility of each potential fault reason, so that an operator can refer to the probability of possibility of the potential reason 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 urgently and parameter alarm information sent by the patrol 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 for an operator to check and confirm.
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 alarm the parameter abnormity.
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 conditions is used as the operation habit model corresponding to the working conditions.
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 in the fault handling process 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, the set protection action starts the corresponding numerical parameter in order to ensure that the equipment does not exceed the allowable range to work).
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 the power plant data in real time and judge whether the power plant data have abnormal parameters, 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, so that 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 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.
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 using 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 optimum operating data is extracted from the previous same 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 the optimized adjustment guidance information of the parameters 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 the operator to perform optimized adjustment on the parameters 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 is used for acquiring parameter online adjustment guide information, taking the parameter online adjustment guide information as an optimal operation adjustment mode of a parameter to be adjusted, acquiring 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 the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode in real time, generating and recording a comparison deviation result, and the comparison deviation result is sent to the operation assistant module so as to prompt the operator to compare the deviation result through the operation assistant module, meanwhile, the comparison deviation result is sent to the optimizer module which sends the comparison deviation result to the digital twin platform and the big data platform, so as to assist the twin model optimization iteration of the digital twin platform and the analysis sample update of the large data platform.
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 comparison back to the operation assistant module and the optimizer module, and the operation assistant module and the optimizer module give an optimization 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 corresponding relationship between the abnormal level and the alarm information level, so as to implement a graded alarm prompt for the abnormal parameter. Further, the monitoring assistant module 101 collects alarm information of abnormal parameters according to a system and a device, 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 sheet recording work to remind the operator (operator) to page each system picture regularly, so that the operator (operator) 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, indicated by the appended claims, in which all terms are intended to be interpreted in their broadest reasonable sense unless otherwise indicated.
Claims (10)
1. An intelligent operation platform of an intelligent power plant is characterized in that,
the intelligent operation platform 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 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 operation assistant module is connected with the alarm analysis module and the supervision assistant module, and is used for receiving emergency information or fault reasons which are sent by the alarm analysis module and confirmed by operators, and generating a fault handling guidance scheme according to a preset fault handling guidance strategy aiming at the fault reasons or the emergency information, so that the operators can operate according to the fault handling guidance scheme to eliminate faults.
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 further used for acquiring standby auxiliary machine state data from the supervision assistant module, 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 executing equipment corresponding to the local test plans and the soot blowing plans according to the standby auxiliary machine state data, the power plant equipment abnormal data, the local measures, a preset local test plan and the soot blowing plans, and generating a regular work prompt according to an executable judgment result to remind an operator to perform regular work.
3. The intelligent operation platform of the intelligent power plant according to claim 1, further comprising an optimizer module connected to the operation assistant module, the digital twin platform, and the big data platform;
the optimization engineer module is used for acquiring twin simulation results from the digital twin platform, acquiring corresponding big data analysis results of historical optimal operating conditions from the big data platform, generating parameter online adjustment guidance information according to a preset parameter adjustment guidance strategy, the twin simulation results and the big data analysis results of the historical optimal operating conditions of the big data platform, and sending the parameter online adjustment guidance information to the operation assistant module;
the operation assistant module is further configured to output parameter optimization adjustment guidance information according to the parameter online adjustment guidance information and an executable judgment result of the execution device corresponding to the parameter online adjustment guidance information, so as to guide an operator to perform optimization adjustment on parameters of the execution device when the operator performs regular work.
4. The intelligent operation platform of the intelligent power plant according to claim 3, further comprising an evaluator module, the evaluator module being connected to the optimizer module;
the system is used for acquiring parameter online adjustment guide information, taking the parameter online adjustment guide information as an optimal operation adjustment mode of a parameter to be adjusted, acquiring 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 the performance difference between the actual operation adjustment mode and the optimal operation adjustment mode in real time, generating and recording a comparison deviation result, and the comparison deviation result is sent to the operation assistant module so as to prompt the operator to compare the deviation result through the operation assistant module, meanwhile, the comparison deviation result is sent to the optimizer module, and the comparison deviation result is sent to the digital twin platform and the big data platform by the optimizer module, to assist in twin model optimization iteration of the digital twin platform and analysis sample update of the big data platform.
5. 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 all operators 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 operators.
6. 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.
7. 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 manually summarized equipment habits and equipment habits analyzed by a big data platform, establishing an equipment habit library based on the equipment habits, the current operation condition of the power plant and presetting 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.
8. The intelligent operation platform of the intelligent power plant according to claim 7, 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 operation safety margin and performance distribution of key equipment and key parameters.
9. The intelligent operation platform of the intelligent power plant according to claim 1, wherein 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 achieve a graded alarm prompt for the abnormal parameter.
10. 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111011433.3A CN113703410B (en) | 2021-08-31 | 2021-08-31 | Wisdom operation platform of wisdom power plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111011433.3A CN113703410B (en) | 2021-08-31 | 2021-08-31 | Wisdom operation platform of wisdom power plant |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113703410A true CN113703410A (en) | 2021-11-26 |
| CN113703410B CN113703410B (en) | 2022-11-04 |
Family
ID=78657892
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111011433.3A Active CN113703410B (en) | 2021-08-31 | 2021-08-31 | Wisdom operation platform of wisdom power plant |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113703410B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116088398A (en) * | 2023-04-10 | 2023-05-09 | 中国电力工程顾问集团西南电力设计院有限公司 | Be used for wisdom prison dish alarm system of thermal power plant |
| CN117148805A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | Multi-scene adaptive power plant equipment early warning method and system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9400819D0 (en) * | 1989-11-02 | 1994-03-16 | Combustion Eng | Plant monitor system |
| US20180356780A1 (en) * | 2017-06-12 | 2018-12-13 | General Electric Company | Methods and systems for controlling generating units and power plants for improved performance |
| CN110033186A (en) * | 2019-04-10 | 2019-07-19 | 华能澜沧江水电股份有限公司 | A kind of huge hydroelectric power plant's intelligence prison disc system |
| CN110556033A (en) * | 2019-07-30 | 2019-12-10 | 华电青岛发电有限公司 | Operation guiding system based on typical and accident case base of thermal power plant |
| US20200096958A1 (en) * | 2018-09-21 | 2020-03-26 | Microsoft Technology Licensing, Llc | Controlling Devices Using a Rich Representation of their Environment |
| CN111670479A (en) * | 2018-01-29 | 2020-09-15 | 费森尤斯医疗护理德国有限责任公司 | Monitoring operational actions for dialysis equipment |
| US20200374202A1 (en) * | 2019-05-23 | 2020-11-26 | Am Technologies Inc. | Industrial field services automation platform |
| CN112817280A (en) * | 2020-12-04 | 2021-05-18 | 华能国际电力股份有限公司玉环电厂 | Implementation method for intelligent monitoring alarm system of thermal power plant |
-
2021
- 2021-08-31 CN CN202111011433.3A patent/CN113703410B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9400819D0 (en) * | 1989-11-02 | 1994-03-16 | Combustion Eng | Plant monitor system |
| US20180356780A1 (en) * | 2017-06-12 | 2018-12-13 | General Electric Company | Methods and systems for controlling generating units and power plants for improved performance |
| CN111670479A (en) * | 2018-01-29 | 2020-09-15 | 费森尤斯医疗护理德国有限责任公司 | Monitoring operational actions for dialysis equipment |
| US20200096958A1 (en) * | 2018-09-21 | 2020-03-26 | Microsoft Technology Licensing, Llc | Controlling Devices Using a Rich Representation of their Environment |
| CN110033186A (en) * | 2019-04-10 | 2019-07-19 | 华能澜沧江水电股份有限公司 | A kind of huge hydroelectric power plant's intelligence prison disc system |
| US20200374202A1 (en) * | 2019-05-23 | 2020-11-26 | Am Technologies Inc. | Industrial field services automation platform |
| CN110556033A (en) * | 2019-07-30 | 2019-12-10 | 华电青岛发电有限公司 | Operation guiding system based on typical and accident case base of thermal power plant |
| CN112817280A (en) * | 2020-12-04 | 2021-05-18 | 华能国际电力股份有限公司玉环电厂 | Implementation method for intelligent monitoring alarm system of thermal power plant |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116088398A (en) * | 2023-04-10 | 2023-05-09 | 中国电力工程顾问集团西南电力设计院有限公司 | Be used for wisdom prison dish alarm system of thermal power plant |
| CN117148805A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | Multi-scene adaptive power plant equipment early warning method and system |
| CN117148805B (en) * | 2023-10-30 | 2024-01-12 | 国网江苏省电力有限公司南通供电分公司 | Multi-scene adaptive power plant equipment early warning method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113703410B (en) | 2022-11-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108154291B (en) | Power supply plant performance index evaluation method and system based on strategy library | |
| CN108596229B (en) | Method and system for monitoring and diagnosing online abnormity | |
| CN108628291B (en) | Expert intelligent diagnosis system based on simulation platform in waste incineration plant | |
| CN113703410B (en) | Wisdom operation platform of wisdom power plant | |
| CN108133316B (en) | Detection method for familial defects of power equipment of power supply plant | |
| CN109240244B (en) | Data-driven equipment running state health degree analysis method and system | |
| CN108345282B (en) | Pump station operation abnormity diagnosis method and system based on artificial intelligence | |
| CN100444070C (en) | Setting method for fault diagnosis and accident prediction | |
| CN106302739A (en) | A kind of Industrial Boiler monitoring and analysis aid decision cloud platform system | |
| CN104638764A (en) | Intelligent state diagnosis and overhauling system for power distribution network equipment | |
| CN110925031B (en) | High-power indirect air cooling steam turbine management and control system | |
| CN110469456B (en) | Wind power plant intelligent control method and system for reducing pitch fatigue | |
| CN109461093A (en) | Electric quantity measuring system Accuracy Evaluation and station service energy consumption analysis system | |
| CN112783127B (en) | Enterprise energy station-oriented comprehensive energy real-time optimization operation and maintenance management system and method | |
| WO2022095406A1 (en) | Real-time evaluation system for operating performance and economical performance of desulfurization device, and application method | |
| CN115437344A (en) | Digital twin comprehensive energy fault diagnosis system, method, device and medium | |
| CN111596643A (en) | Visual dynamic energy consumption diagnosis, analysis and pre-control system based on big data | |
| CN109885019A (en) | A kind of fan condition automatic judging method | |
| CN115017214A (en) | Hydropower station auxiliary equipment operation state analysis early warning method, device and storage medium | |
| CN117217630A (en) | Equipment reliability maintenance method and system for intelligent power plant | |
| JP6930592B2 (en) | Warning, Irregular, Unfavorable Mode Predictors and Methods | |
| CN108520302A (en) | A kind of diesel engine failure diagnosis system | |
| CN116070129A (en) | Intelligent diagnosis system for hydropower centralized control accident | |
| CN114091811A (en) | Maintenance decision system for circulating water pump of nuclear power plant and design method | |
| Sultanov et al. | Electrical generator unit technical and economic indexes parameter study |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |