CN112200433B - Power plant thermal performance analysis and optimization system - Google Patents
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Abstract
The invention provides a thermal performance analysis and optimization system for a power plant, which comprises: a model management and rule management module; an index calculation and benchmarking module; a measurement data display and analysis module; the performance online analysis module: comparing and analyzing different data types of the same measuring point by using a comparison table, wherein the data types comprise a measured value, a verification value and an expected value, the comparison result comprises an absolute deviation and a relative deviation, and a large deviation can be identified by colors; a performance offline analysis module; an operation and maintenance decision support module; and a trend early warning and fault diagnosis module. The system takes a thermodynamic model as a core, establishes a performance analysis model according to the equipment characteristics and the system logic, performs economic analysis, diagnosis and prediction on the equipment and the system by utilizing real-time and historical operating data and simulation data of the thermodynamic model, provides decision support for operation mode and technical improvement for an owner, and finally helps a power plant, a unit and the equipment to maintain a higher performance level.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a thermal performance analysis and optimization system for a power plant.
Background
With the development of the power market, power plants put higher and higher requirements on the operating economy of the units. The method has the advantages of improving the production efficiency and the equipment availability ratio, optimizing the resource allocation, reducing the power generation cost to the maximum extent, keeping the unit always running under the optimal working condition on the premise of ensuring the safe running of the unit, reducing the coal consumption rate to the maximum extent, practically improving the running economy, establishing a brand new operating mechanism which takes economic benefits as the center, reduces the cost as the core and takes energy conservation and efficiency improvement as the key points, and being an important means for improving the competitive strength of the power plant. The thermal performance test of the steam turbine is an industrial test for acquiring the thermal characteristics of the steam turbine under a specified working condition by using a thermal measurement method in a specific thermal circulation system, is an important means for evaluating the operating energy consumption level of the steam turbine and acquiring various thermodynamic indexes of a unit, auxiliary equipment of the unit and the system, and plays an important role in the aspects of operation optimization, state monitoring and evaluation, technical transformation, economy and safety evaluation and the like of the steam turbine unit.
The analysis of the thermal performance of power plants starts later in China, but after the middle of the eighties of the 20 th century, more research works are done in the aspects of electric power academy of sciences, western-safety heating institute, western-safety transportation university, Qinghua university, Shanghai transportation university, southeast university, Zhejiang university and North China electric power university in various provinces, and a corresponding online thermal performance analysis system is developed. However, the existing online thermal performance analysis system generally has the following defects:
1) the correlation analysis capability of the equipment thermodynamic performance measuring points cannot be established, the capability of providing analysis tools and analysis processes for equipment management personnel by improving the transparency of the equipment operation information through big data analysis is lacked, and the real-time monitoring and fault diagnosis capability of the equipment thermodynamic performance is poor;
2) a thermal performance analysis model cannot be established according to the characteristics of the equipment and the logic of the system, the model cannot be dynamically corrected by using real-time and historical operating data, and further the economic analysis, diagnosis and prediction of the equipment and the system cannot be performed on the basis of the corrected accurate model;
3) the system only has conventional functions such as index display analysis and the like, and does not have advanced functions such as performance prediction, decision support and the like;
4) the data processing capability is weak, the intelligent learning and modeling capability of the algorithm is not realized, and the functions of index analysis, equipment diagnosis, fault early warning and the like cannot be realized;
5) a thermodynamic system built in the module implementation process cannot guarantee the authenticity of the simulation engine and the reliability of a calculation result.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a thermal performance analysis and optimization system of a power plant, which takes a thermal model as a core, establishes a performance analysis model according to equipment characteristics and system logic, and utilizes real-time and historical operating data and simulation data of the thermal model to perform economic analysis, diagnosis and prediction of equipment and a system, thereby providing decision support for operation mode and technical improvement for an owner and finally helping the power plant, a unit and equipment to maintain a higher performance level.
In order to realize the technical scheme, the invention provides a thermal performance analysis and optimization system of a power plant, which comprises: model management and rules management Module: the method is used for establishing a model for important equipment or a system of the gas turbine unit, performing model training by adopting a large amount of field measurement historical data under normal operation conditions, monitoring any deviation between the actual operation condition and the normal operation state of the equipment or the system in real time after the model training is finished, and establishing a unit performance model, a thermodynamic system model and an equipment state model; index calculation and benchmarking module: the key data which are used for displaying, benchmarking, analyzing and evaluating the performance indexes of the power plant energy consumption and relevant to the maintenance of the gas turbine body comprise: the method comprises the following steps of (1) supplying power standard gas consumption, generating standard gas consumption, gas turbine efficiency, steam turbine heat consumption, steam turbine internal efficiency, generator efficiency, waste heat boiler efficiency, plant power consumption rate, compressor pressure ratio, compressor efficiency, turbine exhaust temperature, each cylinder efficiency of a steam turbine, each water pump efficiency, heat exchanger end difference and condenser end difference; the measurement data display and analysis module: the system comprises a trend graph, a plurality of measuring points, a plurality of data acquisition units and a plurality of data processing units, wherein the trend graph is used for displaying time sequence data of the measuring points, the abscissa of the trend graph is time, the ordinate of the trend graph is measuring point value, the data acquisition units are used for displaying the time sequence data of the measuring points, the data acquisition units are used for drawing a continuous curve in a period of time for a certain measuring point, and the time interval is changed; the performance online analysis module: comparing and analyzing different data types of the same measuring point by using a comparison table, wherein the data types comprise a measured value, a verification value and an expected value, the comparison result comprises an absolute deviation and a relative deviation, and the absolute deviation can be identified by colors; a performance offline analysis module: for supporting user changes to the input data; the operation and maintenance decision support module: the method is used for providing an auxiliary decision for the operation of the power plant by using a simulator on the basis of index calculation and operation deviation analysis, generating an economic report in a customized manner by simulating historical data, combining the actual operation condition of the power plant, using an optimization algorithm, taking the best overall economy of a unit as a target function and taking the safety of the unit as a constraint condition, performing automatic optimization calculation of controllable parameters, giving an optimized operation mode and an optimized effect, providing compressor washing prediction, predicting the best compressor washing time according to the operation state of the gas turbine, and giving a corresponding washing effect; the trend early warning and fault diagnosis module: the intelligent energy-saving monitoring system comprises a trend early warning module and an alarm monitoring and analyzing module, and utilizes big data and artificial intelligence technology to carry out trend early warning and diagnosis on equipment energy consumption, so as to assist a power plant to realize early discovery, accurate diagnosis and quick repair of faults.
Preferably, the model management and rule management module specifically includes: the model creating and training module is used for establishing a model for important equipment or a system to be monitored, carrying out model training by adopting a large amount of field measurement historical data under a normal operation condition, monitoring any deviation between the actual operation condition and the normal operation condition of the equipment or the system in real time after the model training is finished, ensuring that the fault can be timely found and predicted at an early stage before the destructive fault really occurs, calculating an expected value of each measuring point in the trained model, and giving an alarm and filing if the expected value is obviously deviated from the current actual value; the model adjusting and optimizing module updates the model in a background online semi-autonomous learning mode of the module, automatically pushes the model to a model training interface through the background, retrains the model and optimizes the model; after the model is established, the system can automatically obtain a residual error initial value and other important default setting parameters through data training so as to allow the model to be quickly put into use, and a user can conveniently change the model parameters at any time and record the trace of parameter modification in the system; and the real-time monitoring and comprehensive display module is used for carrying out real-time monitoring and visual display by a main line of the unit-system-equipment, and assisting a user to master the real-time operation condition of each equipment at the first time.
Preferably, the performance indexes of the power plant energy consumption displayed, benchmarked and analyzed and evaluated in the index calculation and benchmarked module comprise a main machine body and each thermodynamic system and equipment, the economic index division is gradually divided from inside to outside, and specifically comprises the calculation of boiler efficiency, steam turbine heat consumption rate, high pressure cylinder efficiency, medium pressure cylinder efficiency, plant power consumption rate, condenser vacuum, condensate supercooling degree, end difference of each heater, unit water replenishing rate, shaft seal steam leakage amount and more complex and deep performance calculation functions, wherein the performance indexes comprise various performance parameters of the unit and main auxiliary machines thereof, including power, efficiency, heat consumption, output, cavitation degree, end difference, temperature rise, heat transfer coefficient and cleanliness.
Preferably, the key data related to the maintenance of the combustion engine body displayed, benchmarked and analyzed and evaluated in the index calculation and benchmarked module comprises: equivalent operation hours, equivalent start-stop times and equivalent operation hours from the next maintenance.
Preferably, the measurement data display and analysis module comprises a plant index overview module, and the plant index overview module is used for displaying a plant-level thermodynamic system diagram, important performance indexes and key measuring points, and can distinguish different types of data and data quality through different fonts and background colors.
Preferably, the online performance analysis module includes: the unit running state prompting module is used for prompting the running mode of the unit at a certain moment; the numerical value comparison table module is used for comparing and analyzing different data types of the same measuring point by using a comparison table, the data types comprise a measured value, a verification value and an expected value, the comparison result comprises an absolute deviation and a relative deviation, and the absolute deviation can be identified by colors; the system distinguishes different data types with different suffixes, M represents a measured value, which is a value obtained directly from a sensor or a value calculated from a sensor value; v represents a verification value, and is a calculated value obtained by measuring redundancy by utilizing error transfer and thermodynamic relation according to a measured value and uncertainty; e represents an expected value which is a numerical value calculated by using heat balance under different environmental conditions and assuming that the equipment is brand new or optimal; the measuring point verification module is used for comparing the measured value with the verification value, calculating a punishment value of a certain measuring point at a certain moment, and analyzing according to the punishment value to determine whether the measuring point value is reliable or not, wherein the larger the punishment value is, the more unreliable the measuring point value is; and the loss analysis module is used for analyzing and displaying the influence of each parameter deviation on the economy in the operation of the power plant, wherein the economical influence comprises output deviation and efficiency deviation.
Preferably, the performance offline analysis module is configured to support a user to change input data, and includes: the system comprises a thermodynamic model, a control module and a control module, wherein the thermodynamic model is used for calculating various indexes under different input data, including circulation efficiency, circulation net efficiency, circulation total output and net output, circulation total heat consumption and net heat consumption.
Preferably, the trend early warning module specifically includes: a data cleaning module: in order to prevent signal failure or incorrect information from being used incorrectly, the collected signals are firstly subjected to data verification and support verification functions which are adaptive to different time periods; a model selection module: according to different equipment and systems, model modeling is carried out by applying the principles of accuracy and real-time responsiveness, and after training, the model can be used by all relevant departments and can be maintained autonomously and a new model is developed; a model creation and training module: establishing a unit performance model, a main thermodynamic system model and an equipment state model by a second party according to real-time and historical data; according to the monitoring range and the object, an early warning model is established, the measuring points are associated, rule definition and model training are carried out, and a key device and system monitoring model can be automatically established from the available measuring points by adopting various modeling technologies; a model parameter setting module: after the model is created, the system can automatically obtain residual initial values and other important default setting parameters through data training so as to allow the model to be put into use quickly, and a user can change the model parameters at any time and conveniently and record the trace of parameter modification in the system. A model adjustment and optimization module: equipment and systems of the power plant can change continuously due to technical transformation, unit maintenance and normal aging, and the model can change at any time; and a measuring point calculating module: some variables which cannot be directly obtained can be obtained through calculating measuring points, including time average values, direct signals, residual errors, calculation variables and verification measuring points can be used for calculating signals, expected values, residual errors and residual error alarms in original measuring points, calculation measuring points and prediction measuring points can be used as input values of the calculation measuring points, the calculation measuring points are positioned under a project and can be quoted by models and rules under the project, and the configuration function of the calculation measuring points is arranged under the project management and is positioned at the same level with the model configuration and the rule configuration; the early warning rule management module: the method comprises the steps of providing management and early warning judgment of a rule based on an expression, wherein the rule can be freely defined by signals, residual values and thresholds, when the behavior of the power plant conforms to the definition, a rule warning can be generated, and expected values, residual errors and residual error warnings in an original measuring point, a calculated measuring point and a predicted measuring point can be used as input values of the rule; a model version management module: and the edition, operation and isolation of the historical model version are realized through the model version management technology.
Preferably, the alarm monitoring and analyzing module specifically includes: plant level alarm monitoring module: providing a plant-level alarm unified monitoring interface, pushing and displaying real-time alarms in real time, wherein the display content comprises alarm levels, power plants, units, projects, models, alarm content, states, first early warning time, recent early warning time, actual values, expected values, accumulated time, early warning times, historical times, alarm levels, trainers, training time and the like, and the plant-level real-time alarm total number, unit alarm distribution, system alarm distribution, project and model alarm distribution; an alarm analysis module: aiming at a certain alarm, the system provides an alarm depth analysis function, can play back the real-time value, the predicted value, the residual error and the early warning curve of the model corresponding to the alarm in the alarm period, vividly present the running state of the equipment in the alarm period, and assist the user in carrying out depth analysis on the alarm; a history alarm query module: the system provides a function of querying historical alarm information, comprehensive query of the historical alarm information is realized through condition filtering and full-text fuzzy search matching functions, alarm details can be traced, alarm analysis is carried out, and real-time values, expected values, residual values and residual exceedance of equipment in a historical alarm time period are visually displayed.
The thermal performance analysis and optimization system for the power plant provided by the invention has the beneficial effects that:
1) the power plant thermal performance analysis and optimization system takes a thermodynamic model as a core, establishes a performance analysis model according to equipment characteristics and system logic, and utilizes real-time and historical operating data and simulation data of the thermodynamic model to analyze, diagnose and predict the economy of the equipment and the system, so as to provide decision support for operation mode and technical improvement for owners and finally help the power plant, the unit and the equipment to maintain higher performance level.
2) Compared with the traditional thermal performance analysis system, the thermal performance analysis and optimization system of the power plant has the advantages that the thermal system built in the module implementation process takes the actual situation of the power plant as the reference, the authenticity of the whole simulation engine and the reliability of a calculation result are guaranteed, the calculation is more accurate, the function is stronger, the system not only has the conventional functions of index display analysis and the like, but also has advanced functions of performance prediction, decision support and the like.
3) The thermal performance analysis and optimization system of the power plant can run deviation analysis and aging analysis to complete the performance analysis function of the unit by arranging a special measurement data display and analysis module, a performance online analysis module and a performance offline analysis module, find out the deviation between an actual performance value and a target performance value and the reason and the parameter causing the deviation, and analyze the reason for generating the deviation and the improved measures. The system can provide an updated value of the unit performance calculation to guide the load increase/decrease or load distribution, can obtain the optimized indexes of all parameters of the unit under different operation conditions by using a data mining method, analyzes the reason of the deviation of the actual calculated value of the key economic performance index and the historical optimal value, and analyzes the deviation to controllable influence factors and maintainable influence factors, thereby guiding the operation and maintenance of the unit.
4) The thermal performance analysis and optimization system of the power plant can establish a thermal performance analysis model according to the equipment characteristics and the system logic, dynamically correct the model by utilizing real-time and historical operating data, and then carry out economic analysis, diagnosis and prediction on the equipment and the system based on the corrected accurate model; the intelligent learning and modeling capability of the algorithm is achieved, and the functions of index analysis, equipment diagnosis, fault early warning and the like can be realized.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the present invention.
FIG. 2 is an architecture diagram of the model management and rules management module of the present invention.
FIG. 3 is an architecture diagram of a measurement data presentation and analysis module according to the present invention.
FIG. 4 is a diagram of the architecture of the performance on-line analysis module of the present invention.
FIG. 5 is a block diagram of a trend pre-warning and fault diagnosis module according to the present invention.
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 embodiments of 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 obtained by a person skilled in the art without making any inventive step are within the scope of the present invention.
Example (b): a thermal performance analysis and optimization system for a power plant.
Referring to fig. 1 to 5, a thermal performance analysis and optimization system for a power plant, which takes a thermal model as a core, establishes a performance analysis model according to equipment characteristics and system logic, and performs economic analysis, diagnosis and prediction of equipment and systems by using real-time and historical operating data and simulation data of the thermal model, thereby providing decision support for operation mode and technical improvement for an owner, and finally helping the power plant, a unit and equipment to maintain a higher performance level. The thermal performance analysis and optimization system comprises the following modules:
model management and rule management module: the method is used for establishing a model for important equipment or a system of the gas turbine unit, performing model training by adopting a large amount of field measurement historical data under normal operation conditions, monitoring any deviation between the actual operation condition and the normal operation condition of the equipment or the system in real time after the model training is completed, and establishing a unit performance model, a thermodynamic system model and an equipment state model. The method specifically comprises the following steps:
11) and the data verification module is used for eliminating the influence of unqualified signals (such as sensor faults) on the model, wherein the unqualified signals (such as sensor faults) are meaningless to the training and monitoring of the model. The representative cases for sensor verification are too large in fluctuation, too fast in change, continuous without change, out of limit and the like, and different parameterization methods are developed for different cases. In addition, the detection of outliers is an effective solution for multi-sensor verification, and the data verification module adopts a method of coarse error elimination based on density, and the method uses the distance between data and the number of samples in a range to determine whether the outliers are coarse error points. The specific method is that given fixed values of p (normal value ratio) and D (distance), when the data amount which is more than D away from a certain data point exceeds the ratio p, the data point is regarded as an outlier, and therefore the aim of verifying the sensor data is achieved.
12) The system can monitor any deviation between the actual operation condition and the normal operation state (expected value) of the equipment or the system of the gas turbine unit in real time after the model training is finished, and can timely discover and predict faults in an early stage before destructive faults really occur. The model creation and training module may be used for various different systems, devices, and sub-devices of the power plant, including unit performance models, thermodynamic system models, and device state models. In actual operation, the system calculates an expected value of each measuring point in the trained model, and if the expected value is obviously deviated from the current actual value, the system gives an alarm in time and archives the alarm.
13) The model adjustment and optimization module, the equipment that has in the current model is ageing to be reflected only in the aspect of economic decline, and does not arouse the safety in production accident, belongs to normal ageing, simultaneously because unit overhauls and technical transformation, will certainly exert an influence to current model, if the model after training in earlier stage does not do the correction, the early warning model can send out continuous warning to these normal relation characteristic changes in subsequent real-time supervision process to the false alarm rate rises, increases treatment personnel's work burden. Therefore, the system updates the existing model in a background online semi-autonomous learning mode of the module. When the normal relation characteristics of the gas turbine unit equipment and the system of the power plant change, such as technical transformation, unit maintenance and normal aging, the model trained in the early stage can send an alarm to the system and the equipment at the initial stage of subsequent use, at the moment, a processing person determines that the alarm is caused by normal relation change through flow operation, the model is automatically pushed to a model training interface by a system background, and the model is retrained, so that the false alarm rate is reduced.
14) And after the model is established, the system can automatically obtain a residual error initial value and other important default setting parameters through data training so as to allow the model to be quickly put into use. The user can change these model parameters at any time and conveniently, recording traces of parameter modifications in the system. Taking the adjustment of the measuring point parameters as an example, the condition parameters of the proper measuring point residual errors are set, the sensitivity of the model can be adjusted, the early warning triggered by the instantaneous fluctuation of the measured value is filtered, and the control model outputs the early warning information to be in line with the actual use.
15) And the system provides real-time monitoring and comprehensive display of model early warning and rule early warning. The model early warning is based on a pre-established model and a training result, the actual running state of the system and the equipment and any deviation between the actual running state and a normal running state (expected value) are monitored in real time, and the condition that the deviation exceeds an early warning range is early warned; the rule early warning is based on production experience, real-time monitoring is carried out on a preset rule, and early warning is carried out on the condition that the rule exceeds a set range of the rule. Through the model early warning and the rule early warning, the fault can be timely found and predicted at an early stage before the destructive fault really occurs. The system carries out real-time monitoring and visual display on fault early warning results (including model early warning and rule early warning) by a main line of unit-system-equipment, and assists a user to master real-time operation conditions of each equipment at the first time. The real-time monitoring and comprehensive display module also provides a plant-level high-level function monitoring and analyzing function, and realizes whole plant early warning trend monitoring, real-time fault handling rate statistics, each unit early warning, each system early warning, each project (equipment) early warning, benchmarking analysis of each model early warning and early warning level distribution analysis. Through the functions of multi-dimensional analysis, statistics, linkage, drilling and the like, the running conditions of the equipment in the jurisdiction range can be visually displayed, and the real-time data, the historical data information and the like of key measuring points of the running information of the equipment can be visually displayed.
(II) an index calculation and benchmarking module: the method is used for displaying, benchmarking, analyzing and evaluating performance indexes of power plant energy consumption and key data related to maintenance of the gas turbine body. The performance index is used for evaluating the energy consumption of the power plant, and not only comprises a main machine body, but also comprises various thermodynamic systems and equipment. The specific economic index is divided from large to small and is divided from inside to outside step by step. Such as conventional boiler efficiency, turbine heat rate, high pressure cylinder efficiency, intermediate pressure cylinder efficiency, plant power rate, condenser vacuum, condensate supercooling degree, end difference of each heater, unit water replenishing rate, shaft seal steam leakage and the like, and more complex and deep performance calculation functions, including real-time calculation of various performance parameters of the unit and main auxiliary machines thereof, such as power, efficiency, heat rate, output, cavitation degree, end difference, temperature rise, heat transfer coefficient, cleanliness and the like. The index calculation and benchmarking functions can also display, benchmarking and analyze key data related to maintenance of the gas turbine body, such as Equivalent Operating Hours (EOH), equivalent start-stop times, equivalent operating hours from next maintenance and the like. The raw data and basic calculation methods for these indices are derived from the combustion engine control system. The main performance indexes of index calculation and benchmarking module display, calculation, benchmarking and analysis comprise: the system comprises a power supply standard gas consumption, a power generation standard gas consumption, a gas turbine efficiency, a steam turbine heat consumption, a steam turbine internal efficiency, a generator efficiency, a waste heat boiler efficiency, a plant power consumption rate, a compressor pressure ratio, a compressor efficiency, a turbine exhaust temperature, each cylinder efficiency of a steam turbine, each water pump efficiency, a heat exchanger end difference, a condenser end difference and the like.
(III) a measurement data display and analysis module: the system comprises a trend graph, a plurality of measuring points, a plurality of data acquisition units and a plurality of data processing units, wherein the trend graph is used for displaying time sequence data of the measuring points, the abscissa of the trend graph is time, the ordinate of the trend graph is measuring point value, the data acquisition units are used for displaying the time sequence data of the measuring points, the data acquisition units are used for drawing a continuous curve in a period of time for a certain measuring point, and the time interval is changed; the method specifically comprises the following steps:
31) the plant-wide index overview module can display a plant-level thermodynamic system diagram, important performance indexes and key measuring points, and can distinguish different types of data and data quality through different fonts and background colors. And the function is used for comparing the data of the same measuring point at different moments, calculating the absolute difference and the relative difference of numerical values at different moments and marking the difference in different degrees by using colors.
32) And the measuring point trend graph module is used for displaying time sequence data of measuring points, the abscissa of the trend graph is time, the ordinate of the trend graph is measuring point numerical value, and a plurality of measuring point data can be displayed in one trend graph and can be used for distinguishing different measuring points by colors.
33) And the duration curve module (duration curve) can draw a duration curve in a period of time for a certain measuring point and support the change of the time interval.
(IV) a performance online analysis module: and comparing and analyzing different data types of the same measuring point by using a comparison table, wherein the data types comprise a measured value, a verification value and an expected value, and the comparison result comprises an absolute deviation and a relative deviation and can identify the absolute deviation by using colors. The method specifically comprises the following steps:
41) and the unit running state prompting module is used for prompting the running mode of the unit at a certain moment.
42) And the numerical value comparison table module is used for comparing and analyzing different data types of the same measuring point by using a comparison table, wherein the data types comprise a measured value, a verification value and an expected value, and the comparison result comprises an absolute deviation and a relative deviation and can identify the absolute deviation by using colors. The system distinguishes different data types with different suffixes, M represents a measured value, which is a value obtained directly from a sensor or a value calculated from a sensor value; v represents a verification value, and is a calculated value obtained by measuring redundancy by utilizing error transfer and thermodynamic relation according to a measured value and uncertainty; e represents the expected value, which is calculated using the thermal equilibrium under different environmental conditions, assuming the plant is brand new or optimal.
43) And the measuring point verification module is used for comparing the measured value with the verification value, calculating a punishment value (Penalty) of a certain measuring point at a certain moment, and analyzing and determining whether the measuring point numerical value is reliable or not according to the punishment value, wherein the larger the punishment value is, the more unreliable the measuring point numerical value is.
44) And the loss analysis module is used for analyzing and displaying the influence of each parameter deviation on the economy in the operation of the power plant, wherein the economical influence comprises output deviation and efficiency deviation.
(V) a performance off-line analysis module: the method is used for supporting a user to change input data, such as ambient temperature, atmospheric pressure, ambient humidity, condenser pressure, circulating water inlet temperature, fuel low heat value and auxiliary machine power consumption, and various indexes under different input data, such as circulating efficiency, circulating net efficiency, circulating total output and net output, circulating total heat consumption and net heat consumption, are calculated by using a thermodynamic model.
And (VI) the operation and maintenance decision support module: the method is used for providing an auxiliary decision for the operation of the power plant by using a simulator on the basis of index calculation and operation deviation analysis, generating an economic report in a customized manner through historical data simulation, combining the actual operation condition of the power plant, performing automatic optimization calculation of controllable parameters by using an optimization algorithm, taking the optimal overall economy of a unit as a target function and taking the safety of the unit as a constraint condition, giving an optimal operation mode and an optimal effect, providing compressor washing prediction, predicting the optimal compressor washing time according to the operation state of the gas turbine, and giving a corresponding washing effect.
(VII) a trend early warning and fault diagnosis module: the system comprises a trend early warning module and an alarm monitoring and analyzing module, and aims at the core target of equipment management, large data and artificial intelligence technology are utilized, field knowledge is combined, trend early warning and diagnosis are carried out on equipment, the early detection, accurate diagnosis and quick repair of faults are realized by a power plant, the power plant is assisted to reduce the loss caused by equipment faults, and the operation and maintenance level of power plant equipment is improved. A specialized diagnosis platform facing a power plant system and equipment is established in the trend early warning and fault diagnosis module, a model containing a main system and auxiliary systems of a gas turbine unit of the power plant and the equipment is integrated, and the early warning and diagnosis of auxiliary equipment of the power plant are focused to assist in making a customized maintenance strategy. The trend early warning and the fault diagnosis are established on the basis of digitalization of the whole life cycle of power plant equipment, the running condition of the equipment is timely known by comprehensively monitoring and index-based management of early warning, faults, historical defects and the like related to the gas turbine unit equipment of the power plant, for example, before the equipment has defects in the running process, an alarm can be sent out in advance according to a big data analysis engine, the health state of the equipment is evaluated by combining expert resources and a knowledge base, and standard work fluidization diagnosis processing is carried out on the alarm.
Wherein trend early warning module specifically includes:
a data cleaning module: to prevent signal failure or incorrect information from being used incorrectly, the acquired signal is first data validated and a validation function is supported that accommodates different periods of time.
A model selection module: the modeling mode applies the principles of accuracy and real-time responsiveness according to the difference of equipment and systems. After training, not only all relevant departments can use the model (operation, maintenance, diagnosis experts and the like), but also the model can be maintained independently and a new model can be developed.
A model creation and training module: establishing a unit performance model, a main thermodynamic system model and an equipment state model according to real-time and historical data; according to the monitoring range and the object, an early warning model is established, measuring points are associated, the work such as rule definition and model training is carried out, and the following functions are realized: key equipment and system monitoring models are automatically created from available survey points using a variety of modeling techniques. The normal state of the process and components of the critical equipment and systems of the power plant can be monitored.
A model parameter setting module: after the model is created, the system can automatically obtain residual initial values and other important default setting parameters through data training so as to allow the model to be quickly put into use. The user can change these model parameters at any time and conveniently, recording traces of parameter modifications in the system.
A model adjustment and optimization module: the equipment and systems of the power plant are subject to constant changes due to technical modifications, unit maintenance and normal aging, and the model should be able to change at any time.
And a measuring point calculating module: some variables which cannot be directly obtained, such as time average values, can be obtained by calculating the measuring points. Direct signals, residuals, calculated variables, verified points, etc. may all be used to calculate the signal. Expected values, residual errors and residual error alarms in the original measuring point (origin sensor), the calculated measuring point (ComputedSensor) and the predicted measuring point (StateEstimation) can be used as input values of the calculated measuring point. The calculation measuring points are positioned under the engineering and can be referred by the model and the rule under the engineering, so that the configuration function of the calculation measuring points is arranged under the engineering management and is positioned at the same level with the configuration of the model and the configuration of the rule.
The early warning rule management module: and providing management and early warning judgment of rules based on expressions. The rules can be freely defined by signals, residuals and thresholds, and a rule alarm is generated when the behavior of the power plant meets the definition. The expected values, residual errors, residual alarms in the original measuring point (OriginalSensor), the calculated measuring point (ComputedSensor) and the predicted measuring point (StateEstimation) can all be used as input values for the Rules (Rules).
A model version management module: and the edition, operation and isolation of the historical model version are realized through the model version management technology.
The alarm monitoring and analyzing module specifically comprises:
plant level alarm monitoring module: and a plant-level alarm unified monitoring interface is provided, and real-time alarm is pushed and displayed in real time. The display contents comprise alarm levels, power plants, units, projects, models, alarm contents, states, first early warning time, recent early warning time, actual values, expected values, accumulated time, early warning times, historical times, alarm levels, trainers, training time and the like. And plant-level real-time alarm total number, unit alarm distribution, system alarm distribution, engineering and model alarm distribution and the like.
An alarm analysis module: aiming at a certain alarm, the system provides an alarm depth analysis function: the real-time value, the predicted value, the residual error and the early warning curve of the model corresponding to the alarm in the alarm time period can be played back, the running state of the equipment in the alarm time period is vividly presented, and a user is assisted in carrying out deep analysis on the alarm.
A history alarm query module: provides a function of inquiring historical alarm. Through the functions of condition filtering, full-text fuzzy search matching and the like, comprehensive query of historical alarm information is realized, alarm details can be traced, alarm analysis is carried out, and real-time values, expected values, residual values and residual overruns of equipment (measuring points) in a historical alarm time period are visually displayed.
The power plant thermal performance analysis and optimization system covers key thermal equipment and systems of a power plant. Key thermodynamic devices and systems include: the system comprises a gas turbine, a steam turbine, a waste heat boiler (comprising a high-pressure steam system, a low-pressure steam system and a reheat steam system), a water feeding pump, a circulating water system, a vacuum pump, a condenser, a condensate pump, an open-close water system, a shaft seal heating system, a condensed water recycling pump, a steam extraction and heat supply system, an auxiliary steam system, a performance heater and a flue gas hot water heat exchanger.
The key indexes include: power supply gas consumption, heat supply gas consumption, combined cycle efficiency, steam turbine efficiency, heat consumption, exhaust-heat boiler efficiency, gas turbine efficiency, plant power consumption, compressor pressure ratio, compressor efficiency, pump efficiency (circulating pump, condensing pump, feed pump, vacuum pump), condenser end difference.
The loss analysis parameters include: the output of the gas turbine, the operation state of HCO, the efficiency of each cylinder of the steam turbine, the exhaust gas temperature of the waste heat boiler and the pressure of a condenser.
Compared with the traditional thermal performance analysis system, the thermal performance analysis and optimization system of the power plant has the advantages that the thermal system built in the module implementation process takes the actual situation of the power plant as the reference, the authenticity of the whole simulation engine and the reliability of a calculation result are guaranteed, the calculation is more accurate, the function is stronger, the system not only has the conventional functions of index display analysis and the like, but also has advanced functions of performance prediction, decision support and the like.
The thermal performance analysis and optimization system of the power plant can run deviation analysis and aging analysis to complete the performance analysis function of the unit by arranging a special measurement data display and analysis module, a performance online analysis module and a performance offline analysis module, find out the deviation between an actual performance value and a target performance value and the reason and the parameter causing the deviation, and analyze the reason for generating the deviation and the improved measures. The system can provide an updated value of the unit performance calculation to guide the load increase/decrease or load distribution, can obtain the optimized indexes of all parameters of the unit under different operation conditions by using a data mining method, analyzes the reason of the deviation of the actual calculated value of the key economic performance index and the historical optimal value, and analyzes the deviation to controllable influence factors and maintainable influence factors, thereby guiding the operation and maintenance of the unit.
The thermal performance analysis and optimization system of the power plant can establish a thermal performance analysis model according to the equipment characteristics and the system logic, dynamically correct the model by utilizing real-time and historical operating data, and then carry out economic analysis, diagnosis and prediction on the equipment and the system based on the corrected accurate model; the intelligent learning and modeling capability of the algorithm is achieved, and the functions of index analysis, equipment diagnosis, fault early warning and the like can be realized.
The above description is only for the preferred embodiment of the present invention, but the present invention should not be limited to the embodiment and the disclosure of the drawings, and therefore, all equivalent or modifications that do not depart from the spirit of the present invention are intended to fall within the scope of the present invention.
Claims (6)
1. A power plant thermal performance analysis and optimization system, comprising:
model management and rules management Module: the method is used for establishing a model for important equipment or a system of the gas turbine unit, performing model training by adopting a large amount of field measurement historical data under normal operation conditions, monitoring any deviation between the actual operation condition and the normal operation state of the equipment or the system in real time after the model training is completed, and establishing a unit performance model, a thermodynamic system model and an equipment state model, and specifically comprises the following steps: the model creating and training module is used for establishing a model for important equipment or a system to be monitored, carrying out model training by adopting a large amount of field measurement historical data under a normal operation condition, monitoring any deviation between the actual operation condition and the normal operation condition of the equipment or the system in real time after the model training is finished, ensuring that the fault can be timely found and predicted at an early stage before the destructive fault really occurs, calculating an expected value of each measuring point in the trained model, and giving an alarm and filing if the expected value is obviously deviated from the current actual value; the model adjusting and optimizing module updates the model in a background online semi-autonomous learning mode of the module, automatically pushes the model to a model training interface through the background, retrains the model and optimizes the model; after the model is established, the system can automatically obtain a residual error initial value and other important default setting parameters through data training so as to allow the model to be quickly put into use, and a user can conveniently change the model parameters at any time and record the trace of parameter modification in the system; the real-time monitoring and comprehensive display module is used for carrying out real-time monitoring and visual display by a main line of the unit-system-equipment, and assisting a user to master the real-time running condition of each equipment at the first time;
index calculation and benchmarking module: the key data which are used for displaying, benchmarking, analyzing and evaluating the performance indexes of the power plant energy consumption and relevant to the maintenance of the gas turbine body comprise: the method comprises the following steps of (1) supplying power standard gas consumption, generating standard gas consumption, gas turbine efficiency, steam turbine heat consumption, steam turbine internal efficiency, generator efficiency, waste heat boiler efficiency, plant power consumption rate, compressor pressure ratio, compressor efficiency, turbine exhaust temperature, each cylinder efficiency of a steam turbine, each water pump efficiency, heat exchanger end difference and condenser end difference;
the measurement data display and analysis module: the system comprises a trend graph, a plurality of measuring points, a plurality of data acquisition units and a plurality of data processing units, wherein the trend graph is used for displaying time sequence data of the measuring points, the abscissa of the trend graph is time, the ordinate of the trend graph is measuring point value, the data acquisition units are used for displaying the time sequence data of the measuring points, the data acquisition units are used for drawing a continuous curve in a period of time for a certain measuring point, and the time interval is changed;
the performance online analysis module: comparing and analyzing different data types of the same measuring point by using a comparison table, wherein the data types comprise a measured value, a verification value and an expected value, the comparison result comprises an absolute deviation and a relative deviation, and the absolute deviation can be identified by colors; the method specifically comprises the following steps: the unit running state prompting module is used for prompting the running mode of the unit at a certain moment; the numerical value comparison table module is used for comparing and analyzing different data types of the same measuring point by using a comparison table, the data types comprise a measured value, a verification value and an expected value, the comparison result comprises an absolute deviation and a relative deviation, and the absolute deviation can be identified by colors; the system distinguishes different data types with different suffixes, M represents a measured value, which is a value obtained directly from a sensor or a value calculated from a sensor value; v represents a verification value, and is a calculated value obtained by measuring redundancy by using error transfer and thermodynamic relation according to a measured value and uncertainty; e represents an expected value which is a numerical value calculated by using heat balance under different environmental conditions and assuming that the equipment is brand new or optimal; the measuring point verification module is used for comparing the measured value with the verification value, calculating a punishment value of a certain measuring point at a certain moment, and analyzing according to the punishment value to determine whether the measuring point value is reliable or not, wherein the larger the punishment value is, the more unreliable the measuring point value is; the loss analysis module is used for analyzing and displaying the influence of each parameter deviation on the economy in the operation of the power plant, wherein the economical influence comprises output deviation and efficiency deviation;
a performance offline analysis module: for supporting user changes to the input data;
the operation and maintenance decision support module: the method is used for providing an auxiliary decision for the operation of the power plant by using a simulator on the basis of index calculation and operation deviation analysis, generating an economic report in a customized manner by simulating historical data, combining the actual operation condition of the power plant, using an optimization algorithm, taking the best overall economy of a unit as a target function and taking the safety of the unit as a constraint condition, performing automatic optimization calculation of controllable parameters, giving an optimized operation mode and an optimized effect, providing compressor washing prediction, predicting the best compressor washing time according to the operation state of the gas turbine, and giving a corresponding washing effect;
the trend early warning and fault diagnosis module: including trend early warning module and warning monitoring and analysis module, utilize big data and artificial intelligence technique, carry out trend early warning and diagnosis to equipment energy consumption, supplementary power plant realizes early discovery, accurate diagnosis and the quick restoration of trouble, the trend early warning module specifically includes: a data cleaning module: in order to prevent signal failure or incorrect information from being used incorrectly, the collected signals are firstly subjected to data verification and support verification functions which are adaptive to different time periods; a model selection module: according to different equipment and systems, model modeling is carried out by applying the principles of accuracy and real-time responsiveness, and after training, the model can be used by all relevant departments and can be maintained autonomously and a new model is developed; a model creation and training module: establishing a unit performance model, a main thermodynamic system model and an equipment state model by a second party according to real-time and historical data, establishing an early warning model according to a monitoring range and an object, associating measuring points, performing rule definition and model training, and automatically establishing a key equipment and system monitoring model from the available measuring points by adopting various modeling technologies; a model parameter setting module: after the model is established, the system can automatically obtain a residual error initial value and other important default setting parameters through data training so as to allow the model to be quickly put into use, a user can conveniently change the model parameters at any time, and the trace of parameter modification is recorded in the system; a model adjustment and optimization module: equipment and systems of the power plant can change continuously due to technical transformation, unit maintenance and normal aging, and the model can change at any time; and a measuring point calculating module: some variables which cannot be directly obtained can be obtained through calculating measuring points, including time average values, direct signals, residual errors, calculation variables and verification measuring points can be used for calculating signals, expected values, residual errors and residual error alarms in original measuring points, calculation measuring points and prediction measuring points can be used as input values of the calculation measuring points, the calculation measuring points are positioned under a project and can be quoted by models and rules under the project, and the configuration function of the calculation measuring points is arranged under the project management and is positioned at the same level with the model configuration and the rule configuration; the early warning rule management module: the method comprises the steps of providing management and early warning judgment of a rule based on an expression, wherein the rule can be freely defined by signals, residual values and thresholds, when the behavior of the power plant conforms to the definition, a rule warning can be generated, and expected values, residual errors and residual error warnings in an original measuring point, a calculated measuring point and a predicted measuring point can be used as input values of the rule; a model version management module: and the edition, operation and isolation of the historical model version are realized through the model version management technology.
2. The power plant thermodynamic performance analysis and optimization system of claim 1, wherein the performance indexes for power plant energy consumption display, benchmarking and analysis evaluation in the index calculation and benchmarking module include a main machine body and each thermodynamic system and equipment, the economic index is divided from large to small, and is divided from inside to outside, specifically including boiler efficiency, turbine heat rate, high pressure cylinder efficiency, intermediate pressure cylinder efficiency, plant power rate, condenser vacuum, condensate supercooling degree, end difference of each heater, unit water replenishing rate, shaft seal steam leakage calculation, and more complex and deep performance calculation functions including real-time calculation of various performance parameters of the unit and its main auxiliary machines, including power, efficiency, heat consumption, output, cavitation degree, end difference, temperature rise, heat transfer coefficient, and cleanliness.
3. A power plant thermal performance analysis and optimization system as claimed in claim 2, wherein said index calculation and benchmarking module displaying, benchmarking and analytically evaluating key data related to maintenance of the gas turbine body comprises: equivalent operation hours, equivalent start-stop times and equivalent operation hours from the next maintenance.
4. A power plant thermodynamic performance analysis and optimization system according to claim 1 wherein the measurement data presentation and analysis module includes a plant wide index overview module for presenting plant level thermodynamic system diagrams, as well as important performance indicators and key points, and being able to distinguish different types of data and quality of data by different fonts and background colors.
5. The power plant thermal performance analysis and optimization system of claim 1, wherein the performance offline analysis module to support user changes to input data comprises: the system comprises a thermodynamic model, a control module and a control module, wherein the thermodynamic model is used for calculating various indexes under different input data, including circulation efficiency, circulation net efficiency, circulation total output and net output, circulation total heat consumption and net heat consumption.
6. A power plant thermal performance analysis and optimization system as claimed in claim 1, wherein the alarm monitoring and analysis module specifically comprises:
plant level alarm monitoring module: providing a plant-level alarm unified monitoring interface, pushing and displaying real-time alarms in real time, wherein the display content comprises alarm levels, power plants, units, projects, models, alarm content, states, first early warning time, recent early warning time, actual values, expected values, accumulated time, early warning times, historical times, alarm levels, trainers, training time and the like, and the plant-level real-time alarm total number, unit alarm distribution, system alarm distribution, project and model alarm distribution;
an alarm analysis module: aiming at a certain alarm, the system provides an alarm depth analysis function, can play back the real-time value, the predicted value, the residual error and the early warning curve of the model corresponding to the alarm in the alarm period, vividly present the running state of the equipment in the alarm period, and assist the user in carrying out depth analysis on the alarm;
a history alarm query module: the system provides a function of querying historical alarm information, comprehensive query of the historical alarm information is realized through condition filtering and full-text fuzzy search matching functions, alarm details can be traced, alarm analysis is carried out, and real-time values, expected values, residual values and residual exceedance of equipment in a historical alarm time period are visually displayed.
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