CN116644851A - Thermal power plant equipment control method and system combined with load optimization configuration - Google Patents
Thermal power plant equipment control method and system combined with load optimization configuration Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
A thermal power plant equipment control method and system combining load optimization configuration belong to the field of intelligent control, and comprise the following steps: collecting and analyzing historical electricity and heat supply information of a user to generate a thermoelectric load of the user; the equipment basic information of the thermoelectric equipment is interacted, and an equipment operation parameter mapping library is constructed; performing fitting distribution of thermal load and electrical load based on the thermoelectric load, and calling a mapping library to perform coal consumption calculation; executing the control optimizing of the coal consumption, and reserving N optimizing control schemes; carrying out scheme selection according to N schemes to generate a selected control scheme; device control of the thermoelectric device is performed by selecting a control scheme. The application solves the technical problem of low heating and electricity efficiency caused by lack of comprehensiveness and accuracy of a control scheme in the prior art, realizes intelligent control and full life cycle management of the thermal power plant equipment, and achieves the technical effects of optimizing equipment operation, reducing energy consumption and improving comprehensiveness and efficiency of equipment control.
Description
Technical Field
The application relates to the field of intelligent control, in particular to a thermal power plant equipment control method and system combined with load optimization configuration.
Background
The thermal power plant is an important component of the electric power and heat supply system in China, and the rationality of the equipment control method directly influences the energy utilization efficiency and the environmental protection level. The traditional thermal power plant equipment control method mainly depends on manual experience, high-precision monitoring, automatic optimal configuration and intelligent control are difficult to realize, and the control scheme lacks comprehensiveness and accuracy, so that the control efficiency is low. Although the applications of information technology and automatic control in thermal power plant equipment are gradually increased in recent years, most of the applications are concentrated on a certain control link, and comprehensive equipment monitoring and optimal control are difficult to realize.
Disclosure of Invention
The application provides a thermal power plant equipment control method and a system combined with load optimization configuration, which aim to solve the problem that the control scheme in the prior art lacks of comprehensiveness
The technical problem of low heating power efficiency is caused by the usability and the accuracy.
In view of the above problems, the present application provides a thermal power plant equipment control method and system that incorporates load optimization configuration.
The first aspect of the application provides a thermal power plant equipment control method combined with load optimization configuration, which comprises the steps of collecting historical electricity utilization and heat supply information of a user, analyzing based on the historical electricity utilization and heat supply information, and generating a thermoelectric load of the user; equipment basic information of the interactive thermoelectric equipment is used for constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of the coal consumption, the electric load and the thermal load in unit time; performing fitting distribution of thermal load and electrical load based on the thermoelectric load, calling an equipment operation parameter mapping library, and performing coal consumption calculation through a formula; executing the control optimizing of the coal consumption, and reserving N optimizing control schemes; taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, and carrying out scheme selection of the N optimizing control schemes to generate a selected control scheme; device control of the thermoelectric device is performed by selecting a control scheme.
In another aspect of the disclosure, a thermal power plant equipment control system combined with load optimization configuration is provided, the system comprises a user thermoelectric load module, a user thermoelectric load module and a control module, wherein the user thermoelectric load module is used for collecting historical electricity and heat supply information of a user, analyzing the historical electricity and heat supply information based on the historical electricity and heat supply information and generating a user thermoelectric load; the parameter mapping library construction module is used for interacting equipment basic information of the thermoelectric equipment and constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of coal consumption, electric load and thermal load in unit time; the coal consumption calculation module 13 performs fitting distribution of the thermal load and the electrical load based on the thermoelectric load, invokes the equipment operation parameter mapping library and performs coal consumption calculation through a formula; the optimizing control scheme module is used for executing the control optimizing of the coal consumption and reserving N optimizing control schemes; the selected control scheme module is used for taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, and carrying out scheme selection of the N optimizing control schemes to generate a selected control scheme; and the thermoelectric device control module is used for controlling the thermoelectric device through the selected control scheme.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
because the thermoelectric load of the user is generated by analyzing the historical electricity utilization and heat supply information of the user, the control according to the actual demand of the user is realized, and the energy utilization demand is met; the equipment operation parameter mapping library is constructed, so that the coal consumption can be calculated according to the actual operation parameters of the thermoelectric equipment, and the accurate prediction of the coal consumption condition can be realized; executing coal consumption control optimizing to obtain various schemes and realize scheme selection with higher degree of freedom; the control scheme is selected by comprehensively considering factors such as coal consumption, equipment stability and equipment calling time length, so that safe, stable and efficient scheme selection is realized; according to the technical scheme for optimally controlling the thermal power plant equipment, the technical problem that the heating and electricity efficiency is low due to the fact that the control scheme lacks comprehensiveness and accuracy in the prior art is solved, intelligent control and full life cycle management of the thermal power plant equipment are achieved, and the technical effects of optimizing equipment operation, reducing energy consumption and improving comprehensiveness and efficiency of equipment control are achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic diagram of a possible flow chart of a thermal power plant equipment control method combined with load optimization configuration according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a thermal power plant equipment control method combined with load optimization configuration to generate a user thermoelectric load according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a thermal power plant equipment control method combined with load optimization configuration to generate a selected control scheme according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible configuration of a thermal power plant equipment control system according to an embodiment of the present application, which is combined with load optimization configuration.
Reference numerals illustrate: the system comprises a user thermoelectric load module 11, a parameter mapping library construction module 12, a coal consumption calculation module 13, an optimizing control scheme module 14, a selected control scheme module 15 and a thermoelectric device control module 16.
Detailed Description
The technical scheme provided by the application has the following overall thought:
the embodiment of the application provides a thermal power plant equipment control method and system combining load optimization configuration, which are used for collecting historical data, analyzing power consumption load, generating coal consumption according to equipment operation data, mapping the power consumption load and the coal consumption, controlling and optimizing the coal consumption to obtain various control schemes, and selecting the control scheme based on the coal consumption and equipment stability to optimally control thermoelectric equipment. The technical effects of optimizing the operation of the equipment, reducing the energy consumption and improving the comprehensiveness and efficiency of the equipment control are achieved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a thermal power plant equipment control method in combination with load optimization configuration, the method including:
step S100: collecting historical electricity consumption and heat supply information of a user, and analyzing based on the historical electricity consumption and heat supply information to generate a thermoelectric load of the user;
specifically, the historical electricity consumption information refers to electricity consumption data of a user over a certain period of time, and includes information such as electricity consumption amount and electricity consumption time. The historical heat supply information refers to heat supply data of the thermal power plant in a historical period corresponding to historical power utilization, and comprises heat supply quantity, heat supply time and other information.
Analyzing by adopting methods such as time sequence analysis, regression analysis, time sequence model and the like based on the historical electricity and heat supply information, for example, calculating average electricity consumption power and electricity consumption of historical data of a user, and taking the average electricity consumption power and the electricity consumption as thermoelectric load parameters representing the basic energy consumption level of the user; extracting characteristic parameters of a user history load curve, such as peak value, valley value, multiplying power, area and the like, and representing the change rule of the thermoelectric demand of the user; analyzing the periodic variation of the user history data, and extracting characteristic parameters representing the periodic variation, such as daily period, weekly period and annual period parameters; and collecting data such as the geographic position, population, building type and the like of the user, calculating the energy density parameter of the user by combining the historical energy information, breaking through the limitation of single user data, and representing the energy level of the user group of the same type in the area. The thermoelectric load of the user is obtained by deep analysis of the historical electricity consumption and heat supply information, and a data basis is provided for optimizing the control of the thermal power plant equipment.
Step S200: equipment basic information of the interactive thermoelectric equipment is used for constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of coal consumption, electric load and thermal load in unit time;
specifically, firstly, basic performance parameters such as capacity, efficiency, coal consumption coefficient and the like of various thermoelectric devices in a thermal power plant are collected, and a device basic information base is constructed. And then, according to parameters in the equipment basic information base, establishing a mapping relation between the operation parameters of each equipment under different operation conditions and the coal consumption, and between the generated electric power and the generated heat, and forming an equipment operation parameter mapping base. The mapping parameters in the equipment operation parameter mapping library are the corresponding relations among theoretical coal consumption, electricity generation and heat supply of the equipment in unit time under normal working conditions, and are determined according to the parameters such as capacity, thermodynamic efficiency, mechanical efficiency, heat transfer efficiency and the like of the equipment.
For example, the basic information of a certain generator set is: the installed capacity is 50MW, the mechanical efficiency is 98%, the power generation efficiency is 42%, and the mapping parameters in the operation parameter mapping library can be set as follows: the corresponding parameter values of the power-coal consumption-power generation amount can be respectively as follows: 40MW-120t/h-16.8MW,45MW-135t/h-18.9MW,50MW-150t/h-21.0MW.
The mapping relation between the operation parameters of the equipment under different working conditions is deduced through the basic information of the equipment, an equipment operation parameter mapping library is established, theoretical coal consumption and productivity rules of the equipment under different output conditions are reflected, and a foundation is laid for optimizing the operation control of the equipment.
Step S300: and carrying out fitting distribution of the thermal load and the electrical load based on the thermoelectric load, calling the equipment operation parameter mapping library, and carrying out coal consumption calculation through a formula, wherein the calculation formula is as follows:
wherein B is the total coal consumption, T is the operation time of the thermoelectric equipment,for the electrical load of the j-th thermoelectric device at time t,>for the thermal load of the j-th thermoelectric device at time t,>the thermal load at time t is +.>The electrical load is +.>Is/are/is>The number of the thermoelectric devices is the number of the whole plant operation at the time t;
specifically, firstly, fitting and distributing the thermal load and the electrical load according to the thermoelectric load of a user, determining the electric quantity and the heat required to be produced by various generator sets and boilers in a given period T, and obtaining the production targets of various devicesAnd. Then, calling a device operation parameter mapping library to search the output target of each device>And- >Coal consumption parameter under corresponding working condition>. Finally, the coal consumption parameters of all the devices at different moments are calculatedAnd carrying out integral summation, and calculating to obtain the total coal consumption B of the thermal power plant in a given period T.
For example, the installed capacities of 3 thermal power plant devices are respectively 45MW, 35MW and 50MW, and the corresponding coal consumption amounts of the installed capacities are respectively: b1 =120t/h, b2=110t/h, b3=150t/h, if three devices continue to run at power at installed capacity, the total plant coal consumption per hour=120+110+150=380 t/h; the coal consumption of the whole plant at each moment is +.>Integration is performed on the time axis to obtain the total coal consumption B over a given period T. If the given period T is 10 hours, the total coal consumption of 3 thermal power plant devices under different working conditions per hour in the period is respectively as follows: />=344t/h，/>=375t/h，/>=399t/h，/>=327t/h，/>=398t/h，/>=407t/h，/>=365t/h，/>=390t/h，/>=370t/h，/>=425 t/h, then the total coal consumption is 3800t by summing the coal consumption for all time periods.
The fitting distribution of the thermal load and the electrical load is carried out based on the thermoelectric load, the coal consumption of each device under different working conditions in a certain period is calculated according to the device operation parameter mapping library, and data support is provided for the establishment of a control scheme of the thermal power plant, so that the optimization under different thermal power plant device control schemes is realized, and the comprehensiveness and the accuracy of the thermal power plant device control are improved.
Step S400: executing the control optimizing of the coal consumption, and reserving N optimizing control schemes;
specifically, various control factors influencing the coal consumption are collected according to the actual condition of the thermal power plant, and a scheme capable of minimizing the actual coal consumption is searched by adjusting the control factors on the premise of meeting the given thermoelectric load of a user. For example, a thermal power plant has 3 power generating units with different efficiencies, and the power distribution scheme can affect the total coal consumption, so that the power distribution scheme of the power generating unit with the minimum total coal consumption can be found through calculation under the condition of ensuring that the user load is met. As another example, where two different heating value coals are available for a thermal power plant, then calculations can be made to determine which coals are selected at different loads to reduce the amount of coal consumed.
Firstly, according to the actual condition of a thermal power plant, all control factors influencing the coal consumption, such as the power and the number of generator sets, the boiler load, the fan rotating speed, the output of a furnace feeder and the like, are collected, and a mapping model between the factors and the coal consumption is established. And secondly, according to the equipment characteristics and the operation limiting conditions, determining the adjustable range of each control factor, and providing limiting conditions for the follow-up scheme optimization. Then, adopting genetic algorithm, simulated annealing algorithm and other optimizing algorithms, and finding out a control factor parameter scheme capable of minimizing the predicted coal consumption of the model through calculation and iteration on the premise of meeting given output conditions. And substituting the optimized control scheme into a coal consumption model of the thermal power plant for operation, verifying that the coal consumption model can effectively reduce the coal consumption, and simultaneously performing thermodynamic test to ensure that the control scheme meets various limiting conditions of safe and stable operation. And finally, selecting N schemes with the smallest coal consumption from all verified control schemes as alternatives for optimizing operation of the thermal power plant, and storing the alternatives into a database of a control system to provide important data support for subsequent scheme selection and adjustment.
By adopting the optimizing algorithm to find out various control schemes and parameters thereof which can effectively reduce the coal consumption of the thermal power plant, the alternative schemes can realize lower coal consumption, so that the control schemes are not single selection schemes any more, the comprehensiveness of the control schemes is improved, the accuracy of the control schemes is further improved, the operation of the thermal power plant equipment is optimized, the energy consumption is reduced, and the energy conversion efficiency of the thermal power plant is improved.
Step S500: taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, and selecting the schemes of the N optimizing control schemes to generate a selected control scheme;
specifically, the obtained N coal consumption optimization control schemes are subjected to scheme selection according to two standards of coal consumption and equipment stability, and a better optimization control scheme is selected as a selected control scheme. Firstly, theoretical coal consumption data corresponding to N control schemes are used as first reference data, the minimum coal consumption which can be realized under the same output conditions of all the schemes is represented, and the scheme with the minimum coal consumption is selected. Then, collecting equipment parameter data of N control schemes in thermodynamic verification and simulation verification, and analyzing and calculating, wherein the equipment parameter data comprise outlet temperature, pressure, flow and the like; and judging the influence and stability of each scheme on key equipment (such as a boiler, a steam turbine and the like) of the thermal power plant, and selecting the scheme with the smallest influence and stability on the equipment as second reference data. And finally, comprehensively judging the first reference data and the second reference data, and selecting a control scheme which is excellent in both coal consumption and equipment stability as the selected control scheme of the invention. The selected scheme can complete the set output task with lower coal consumption, can not cause great influence on the stable operation of equipment, and is a preferred scheme for achieving high-efficiency low-carbon operation in N optimizing control schemes of the obtained thermal power plant.
Compared with the existing single scheme selection method, the method not only considers the economic index of coal consumption when selecting the control scheme, but also considers the safety coefficient of equipment stability, and the control scheme is comprehensive and accurate by adopting a multi-element comprehensive judgment mode, so that the energy conversion efficiency is improved, and meanwhile, the stability of the control scheme is ensured.
Step S600: device control of the thermoelectric device is performed by the selected control scheme.
Specifically, the obtained selected control scheme is sent to a thermal power plant equipment control system for adaptively controlling the thermoelectric equipment in real time, so that the automatic operation of the equipment is realized. Firstly, parameter set values of all control factors in a selected control scheme, such as a power distribution coefficient of a generator set, a fan rotating speed, a coal amount of a coal feeder and the like, are sent to a corresponding equipment control system to guide equipment to achieve a working state required by the scheme. Then, the thermal power plant detects various equipment operation parameters such as outlet temperature, pressure, flow and the like, and feeds back detection data to a control system, and the control system adopts a PID control algorithm and the like to compare the feedback data with a set value of a selected control scheme in real time so as to calculate control deviation. And then, the control system sends control signals, such as increment signals, to the control system of each device according to the control deviation so as to adjust the working parameters of each device, such as valve opening, fan frequency and the like, and each device corrects the working parameters according to the control signals, so that the actual working conditions gradually meet the requirements of the selected control scheme. Finally, through the closed-loop control, the actual working state of each device is gradually consistent with the selected control scheme. The selected control scheme is successfully applied to automatic operation management of the thermal power plant, and the online regulation and control function is completed, so that the thermal power plant is guided to operate efficiently, low-carbon and stably.
The provided selected control scheme is converted into an automatic equipment control instruction on line, so that the equipment is intelligently regulated, the thermal power plant equipment can be driven to reach an optimal operation state more quickly and accurately, the load change can be dynamically tracked, the stable operation is ensured, and the technical effects of optimizing the equipment operation, reducing the energy consumption and improving the comprehensiveness and the efficiency of the equipment control are realized.
Further, as shown in fig. 2, the embodiment of the present application further includes:
step S110: performing time sequence trend analysis based on the historical electricity consumption and heat supply information, and fitting to obtain a basic thermoelectric load;
step S120: setting an interception window, and reading window electricity and heat supply information in the interception window;
step S130: setting a fluctuation tolerance value of fluctuation according to the window electricity consumption and heat supply information;
step S140: a user thermoelectric load is generated based on the fluctuating tolerance value and the base thermoelectric load.
Specifically, time sequence analysis is carried out on long-term historical electricity consumption and heat supply data of a user, a main periodic change rule of the data is extracted, a curve fitting method is adopted to obtain a basic thermoelectric load curve, and the basic thermoelectric load curve represents the basic thermoelectric demand level of the user. The time sequence analysis can analyze the period components of the historical data by adopting the techniques such as Fourier transformation and the like, and extract main change rules such as daily period, weekly period, annual period and the like. Fitting of the basic thermoelectric load curve can be achieved by adopting a method of a polynominal curve, an exponential curve, a cubic B spline curve and the like so as to obtain a smooth representative curve.
And setting a reasonable time window, and reading the user electricity and heat supply history data in the latest time window as window electricity and heat supply information, wherein the length of the time window is determined according to the data change frequency and can be set as data of nearly 1 week, half month or 1 month. The window electricity and heat supply information contains actual energy data of the user in the latest time window, and the window electricity and heat supply information is closer to the current actual demand of the user and reflects the current thermoelectric demand level and change trend of the user. And calculating the variation range of the user energy according to the window electricity consumption and the heat supply information, wherein the variation range is used as a fluctuation tolerance value representing the variation of the thermoelectric load of the user, and the fluctuation tolerance value can be set as standard deviation, variance, outlier limit value or the like of window data so as to measure the variation amplitude of the user energy. The fluctuation tolerance value is inversely proportional to the window length, and the shorter the window, the larger the fluctuation tolerance value which represents the current user can change.
The basic thermoelectric load represents the average level of the long-term thermoelectric demand of the user, the fluctuation tolerance value represents the fluctuation range of the energy demand of the user in a near term, and the thermoelectric load of the user is generated through the fluctuation tolerance value and the basic thermoelectric load by adopting methods such as an superposition method, a confidence interval method, a time parameter model, a conditional probability model and the like. For example, for the superposition method, the fluctuating tolerance value is superimposed on the base thermoelectric load curve to generate a user thermoelectric load with dynamic range, and if the base thermoelectric load is 50MW and the fluctuating tolerance value is + -10%, the user thermoelectric load may be set to 45-55MW.
The thermoelectric load of the user is generated through fluctuation of the tolerance value and the basic thermoelectric load, the parameters of basic energy consumption requirements of the user are contained, dynamic parameters of recent internal energy consumption changes are contained, the thermoelectric requirements of the user in a current period and a future period are comprehensively and accurately reflected, ideal basic input conditions are provided for optimal control and scheduling of the thermoelectric power plant, and therefore accuracy of a follow-up control scheme is improved.
Further, the embodiment of the application further comprises:
step S510: reading historical operating data of the thermoelectric device;
step S520: performing equipment life cycle node division on the historical working data to generate a multi-node identifier;
step S530: setting a time attenuation coefficient, and calculating a data true value of the historical working data based on the time attenuation coefficient and the multi-node identifier to obtain a true value calculation result;
step S540: and performing operation stability calculation according to the true value calculation result and the historical working data to obtain equipment stability data.
Specifically, historical operation data of each key device of the thermal power plant, such as valve opening, water supply amount, bearing temperature, inlet temperature, pressure and other process parameter data, are read from a historical database, and the data comprise working information of the device in a long-term operation process, and reflect dynamic changes of the operation state of the device. According to the theoretical life cycle of the equipment, the historical operation data are divided into different stages, such as a running-in early stage, a running-in middle stage, a running-in later stage, a stabilizing early stage, a stabilizing middle stage, a stabilizing later stage, a fault multiple stage and the like. The stages correspond to the performance and stability changes of the equipment, and the current operation stage of the equipment can be accurately judged according to the divided life cycle of the equipment. And dividing the life cycle of the equipment by adopting the technologies such as an expert judgment method, a cluster analysis method and the like to obtain multi-node identifiers divided according to the life cycle of the equipment.
Because the collection time span of the historical working data is longer, the equipment performance and state can change in the process, and larger deviation can be introduced by directly analyzing the historical working data, in order to reduce the deviation, the timeliness of the data needs to be considered, the time attenuation coefficient is set to weight the data in different time periods, and the data true value reflecting the current equipment performance is calculated. First, a time decay function, such as an exponential function, is determined according to the period in which the thermal power plant equipment is located, then, the relative change of importance and decay rate of data in different periods are determined according to the change of the data, and the weight of the data in different periods is calculated, wherein the more recent the historical data, the greater the weight is given. And finally, setting weights acquired according to the time attenuation coefficients for the historical working data in different periods according to the multi-node identification, and calculating the true value of the data to obtain a true value calculation result.
After the calculation result of the real value after the time attenuation processing is obtained, the current running condition of the equipment can be accurately judged. The method adopts Markov process and other technologies to combine the true value calculation result and the historical working data, analyzes the indexes such as the failure rate, the working fluctuation and the like of the equipment, further calculates the running stability of the equipment, and provides a judgment basis for the subsequent equipment control scheme selection.
Further, the embodiment of the application further comprises:
step S541: performing abnormal call through the historical working data, and recording fault frequency, fault value, working fluctuation frequency and working fluctuation value, wherein the fault value is a representation value of the severity of the fault, and the working fluctuation value is a stable output representation value for generating power or heating;
step S542: carrying out real characteristic adjustment on the fault frequency, the fault value, the working fluctuation frequency and the working fluctuation value according to the real value calculation result;
step S543: and calculating the running stability of the equipment according to the adjusted fault frequency, the adjusted fault value, the adjusted working fluctuation frequency and the adjusted working fluctuation value, and obtaining equipment stability data.
Specifically, based on historical operating data of the thermoelectric device, various faults and operating surge events encountered by the device may be detected as abnormal calls. By analyzing the historical working data, indexes such as fault frequency, fault value, working fluctuation frequency, working fluctuation value and the like are counted and recorded. Wherein, the fault value represents the severity of the fault event and can be quantitatively described by indexes such as shutdown time or economic loss; the working fluctuation value represents the fluctuation degree of the power generation or heat supply output of the equipment, and can be described by indexes such as standard deviation of output power or temperature.
The obtained calculation result of the real value of the time attenuation coefficient can reflect the real working state of the equipment in the current period. And correcting the recorded indexes such as fault frequency, fault value, working fluctuation frequency, working fluctuation value and the like by adopting methods such as weighted summation or probability statistics and the like according to the calculation result of the real value, reducing errors caused by time variation and obtaining the real characteristic values of the indexes. After the real characteristics of the fault index and the work fluctuation index are obtained, the running stability of the equipment can be accurately estimated, then the running stability of the equipment is calculated according to the fault frequency, the fault value, the work fluctuation frequency and the work fluctuation value by adopting the technologies of Markov process or gray correlation analysis and the like, the calculated result is used as equipment stability data, a basis is provided for the selection of an equipment control scheme, and the accuracy of the control scheme is further improved.
Further, as shown in fig. 3, the embodiment of the present application further includes:
step S550: setting a duration threshold based on the user thermoelectric load;
step S560: judging whether the thermoelectric device calling time lengths in the N optimizing control schemes all meet the time length threshold value;
step S570: when thermoelectric equipment which cannot meet the time threshold exists in the thermoelectric equipment calling time, performing thermoelectric equipment abnormal calling identification corresponding to the optimizing control scheme;
Step S580: and taking the abnormal call identification result as third reference data, and carrying out scheme selection of the N optimizing control schemes according to the first reference data, the second reference data and the third reference data to generate a selected control scheme.
In particular, the consumer thermoelectric load reflects the consumer side electrical and thermal demands, the trend and magnitude of which directly affect the invocation of thermoelectric devices. According to the data statistics characteristics of the thermoelectric load of the user, setting the upper limit value of the calling time length of the thermoelectric device as a time length threshold value, and meanwhile, setting the time length threshold value should not cause overload operation of the device while meeting the basic requirements of the thermoelectric load of the user.
N optimizing control schemes which are reserved through executing the coal consumption control optimizing correspond to different thermoelectric equipment calling time lengths. Judging N optimizing control schemes, and if the equipment calling time length in a certain scheme exceeds a set time length threshold value, indicating that the scheme has no feasibility. If judging that the calling duration exceeds the threshold value in a certain scheme, abnormal calling identification is needed to be carried out on the scheme and related equipment in the forms of Boolean variables or nomenclature and the like, and abnormal schemes are eliminated in subsequent scheme screening.
The first reference data obtained according to the coal consumption and the second reference data obtained according to the equipment stability are used for evaluating the economical efficiency and the safety of each scheme, and the third reference data obtained according to the duration threshold is used for judging the feasibility of the scheme. The three types of reference data are synthesized, N optimizing control schemes are screened and judged by adopting a fuzzy judgment method or a weighing method and other technologies, and a selected control scheme which is economical, safe and feasible is selected, so that the accuracy and the control quality of the equipment control scheme are improved.
Further, the embodiment of the application further comprises:
step S581: setting short-time start association coefficients of all thermoelectric devices based on the device basic information;
step S582: and setting an increment association value, calculating an influence value of the abnormal call according to the equipment abnormal call identifier, the short-time starting association coefficient and the increment association value, and generating the third reference data according to an influence value calculation result.
In particular, the starting process of the thermoelectric device often consumes a large amount of starting energy, and frequent starting and stopping can bring a large burden to the device, affecting the service life thereof. The maximum number of times each thermal power plant device is allowed to start in a short time, namely, the short-time start-up association coefficient is set according to basic parameters such as capacity, power and the like of the thermal power plant device, wherein the larger the capacity is, the smaller the short-time start-up association coefficient of the device should be generally set.
First, an incremental correlation value is set, which represents a coefficient by which the degree of resistance of the device to subsequent starts gradually increases during continuous use. And then, calculating the influence value of the equipment abnormal call in each scheme by combining the obtained abnormal call identifier and the set short-time starting association coefficient. The larger the impact value, the more serious the negative impact on the device by the optimized control scheme. And finally, taking the influence value calculation result of each scheme as third reference data, and taking the third reference data as a basis for generating a selected control scheme for selecting a scheme with smaller influence value on equipment after the control scheme is implemented so as to avoid overload on the equipment.
By considering the starting relevance of the thermoelectric equipment, the potential influence of different control schemes on the equipment can be accurately evaluated, the scheme which can accelerate equipment degradation in N optimizing control schemes is avoided, a judgment basis is provided for optimizing the equipment control scheme, the safety and the utilization efficiency of the equipment are improved, and the control comprehensiveness of the equipment of the thermoelectric power plant is improved.
Further, the embodiment of the application further comprises:
step S710: performing equipment anomaly analysis based on the equipment control result and a preset fitting result;
Step S720: performing abnormal clustering on the equipment abnormal analysis result;
step S730: generating overhaul feedback information through an abnormal clustering result;
step S740: and carrying out overhaul and maintenance on the thermoelectric equipment through the overhaul feedback information.
Specifically, the device control result refers to an actual working state after the thermoelectric device is controlled by the selected control scheme, and the preset fitting result refers to an expected working state after the thermoelectric device is controlled according to the selected control scheme. And comparing and analyzing the equipment control result and the preset fitting result by adopting technical means such as state estimation, data mining and the like, and detecting the abnormality such as overhigh temperature, overlarge vibration, lower output power and the like in the working process of the equipment.
The equipment anomaly analysis can detect a plurality of anomaly parameters or indexes, and the anomaly conditions are required to be classified and aggregated by adopting K-means clustering, hierarchical clustering, DBSCAN clustering and other methods to generate anomaly categories. The abnormal clustering is to classify the abnormal situation according to the reasons or influences of the abnormality, and provide references for the subsequent maintenance scheme formulation. After the abnormal clustering result is obtained, judging that the root cause of the abnormality of the thermoelectric equipment is located, and generating overhaul feedback information corresponding to the abnormality type so as to guide overhaul and maintenance of the equipment. Wherein, the overhaul feedback information should contain abnormal parts, analysis of abnormal reasons, overhaul suggestions and the like. And then, detecting and positioning the abnormal equipment, replacing parts or adjusting parameters and the like according to the overhaul feedback information so as to eliminate the abnormality and ensure that the thermoelectric equipment is restored to a normal working state.
By means of the difference analysis of the equipment control result and the preset fitting result, the abnormal condition of the equipment can be accurately detected, a refined and targeted decision basis is provided for equipment maintenance through abnormal clustering and overhaul feedback, the equipment downtime can be reduced to the greatest extent, and the working efficiency and the operation stability of the equipment are effectively improved.
In summary, the thermal power plant equipment control method combined with load optimization configuration provided by the embodiment of the application has the following technical effects:
the historical electricity and heat supply information of the user is collected, analysis is carried out based on the historical electricity and heat supply information, the thermoelectric load of the user is generated, the thermoelectric load of the user can be accurately predicted, and important references are provided for operation management of subsequent equipment. The equipment basic information of the thermoelectric equipment is interacted, and an equipment operation parameter mapping library is constructed according to the basic information, so that data support can be provided for calculation of the equipment coal consumption under different loads, and the comprehensiveness and accuracy of a subsequent control scheme are improved; the fitting distribution of the thermal load and the electrical load is carried out based on the thermoelectric load, the equipment operation parameter mapping library is called, the coal consumption is calculated through a formula, the corresponding total amount of the coal consumption of the equipment can be accurately calculated according to the thermoelectric load under different conditions, and data support is provided for the follow-up accurate generation control scheme; the control optimizing of the coal consumption is executed, N optimizing control schemes are reserved, multiple control schemes with the lowest coal consumption can be obtained, single selection of the control schemes is avoided, and the control schemes are enriched; taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, carrying out scheme selection of the N optimizing control schemes, generating a selected control scheme, limiting factors in multiple aspects for the selection of the control scheme, and improving the rationality and accuracy of the control scheme; the thermoelectric equipment is controlled by selecting a control scheme, so that the coal consumption can be reduced to the maximum extent, meanwhile, the equipment can be ensured to work stably, and the technical effects of optimizing the equipment operation, reducing the energy consumption and improving the control comprehensiveness and efficiency of the equipment are achieved.
Example two
Based on the same inventive concept as the thermal power plant equipment control method in combination with the load optimization configuration in the foregoing embodiments, as shown in fig. 4, an embodiment of the present application provides a thermal power plant equipment control system in combination with the load optimization configuration, the control system including:
the user thermoelectric load module 11 is used for collecting historical electricity consumption and heat supply information of a user, analyzing the historical electricity consumption and heat supply information based on the historical electricity consumption and heat supply information and generating a user thermoelectric load;
the parameter mapping library construction module 12 is used for interacting equipment basic information of the thermoelectric equipment, and constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of coal consumption, electric load and thermal load in unit time;
the coal consumption calculation module 13 performs fitting distribution of the thermal load and the electrical load based on the thermoelectric load, and invokes the equipment operation parameter mapping library to perform coal consumption calculation according to a formula, wherein the calculation formula is as follows:
wherein B is the total coal consumption, T is the operation time of the thermoelectric equipment,for the electrical load of the j-th thermoelectric device at time t,>for the thermal load of the j-th thermoelectric device at time t, >The thermal load at time t is +.>The electrical load is +.>Is/are/is>The number of the thermoelectric devices is the number of the whole plant operation at the time t;
the optimizing control scheme module 14 is used for executing the control optimizing of the coal consumption and reserving N optimizing control schemes;
the selected control scheme module 15 is configured to perform scheme selection of the N kinds of optimizing control schemes by using the coal consumption of the N kinds of optimizing control schemes as first reference data and using equipment stability data of the N kinds of optimizing control schemes as second reference data, so as to generate a selected control scheme;
a thermoelectric device control module 16 for performing device control of the thermoelectric device via the selected control scheme.
Further, the embodiment of the application further comprises:
the basic thermoelectric load module is used for carrying out time sequence trend analysis based on the historical electricity utilization and heat supply information and obtaining basic thermoelectric load through fitting;
the intercepting window module is used for setting an intercepting window and reading window electricity and heat supply information in the intercepting window;
the fluctuation tolerance value module is used for setting a fluctuation tolerance value of fluctuation according to the window electricity and heat supply information;
and a user thermoelectric load module that generates a user thermoelectric load based on the fluctuating tolerance value and the base thermoelectric load.
Further, the embodiment of the application further comprises:
the historical working data module is used for reading the historical working data of the thermoelectric equipment;
the multi-node identification module is used for carrying out equipment life cycle node division on the historical working data to generate a multi-node identification;
the real value calculation module is used for setting a time attenuation coefficient, and carrying out data real value calculation on the historical working data based on the time attenuation coefficient and the multi-node identifier to obtain a real value calculation result;
and the equipment stability data module is used for carrying out operation stability calculation according to the true value calculation result and the historical working data to obtain equipment stability data.
Further, the embodiment of the application further comprises:
the working fault recording module is used for carrying out abnormal call through the historical working data and recording fault frequency, fault value, working fluctuation frequency and working fluctuation value, wherein the fault value is a representation value of the severity of the fault, and the working fluctuation value is a stable output representation value for generating power or heating;
the real characteristic adjustment module is used for carrying out real characteristic adjustment on the fault frequency, the fault value, the working fluctuation frequency and the working fluctuation value according to the real value calculation result;
And the stability data obtaining module is used for calculating the running stability of the equipment according to the adjusted fault frequency, the adjusted fault value, the adjusted working fluctuation frequency and the adjusted working fluctuation value to obtain equipment stability data.
Further, the embodiment of the application further comprises:
a duration threshold setting module for setting a duration threshold based on the user thermoelectric load;
the control scheme judging module is used for judging whether the thermoelectric device calling time lengths in the N optimizing control schemes all meet the time length threshold;
the abnormal call identification module is used for carrying out abnormal call identification of the thermoelectric device corresponding to the optimizing control scheme when the thermoelectric device which cannot meet the duration threshold exists in the thermoelectric device call duration;
the selected control scheme module is used for taking the abnormal call identification result as third reference data, and selecting schemes of the N optimizing control schemes according to the first reference data, the second reference data and the third reference data to generate a selected control scheme.
Further, the embodiment of the application further comprises:
the starting association coefficient module is used for setting short-time starting association coefficients of all thermoelectric devices based on the device basic information;
And the third reference data module is used for setting an incremental association value, calculating an influence value of abnormal call according to the equipment abnormal call identifier, the short-time starting association coefficient and the incremental association value, and generating the third reference data according to an influence value calculation result.
Further, the embodiment of the application further comprises:
the equipment abnormality analysis module is used for carrying out equipment abnormality analysis based on the equipment control result and a preset fitting result;
the abnormal clustering module is used for carrying out abnormal clustering on the equipment abnormal analysis result;
the overhaul feedback information module is used for generating overhaul feedback information through the abnormal clustering result;
and the overhaul maintenance module is used for carrying out overhaul maintenance on the thermoelectric equipment through the overhaul feedback information.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. A thermal power plant equipment control method combined with load optimization configuration, characterized in that the method comprises:
collecting historical electricity consumption and heat supply information of a user, and analyzing based on the historical electricity consumption and heat supply information to generate a thermoelectric load of the user;
equipment basic information of the interactive thermoelectric equipment is used for constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of coal consumption, electric load and thermal load in unit time;
and carrying out fitting distribution of the thermal load and the electrical load based on the thermoelectric load, calling the equipment operation parameter mapping library, and carrying out coal consumption calculation through a formula, wherein the calculation formula is as follows:
wherein B is total coal consumption, T is thermoelectric device operation time, < >>For the electrical load of the j-th thermoelectric device at time t,>for the thermal load of the j-th thermoelectric device at time t,the thermal load at time t is +.>The electrical load is +.>Is used for the fuel consumption of the fuel cell,the number of the thermoelectric devices is the number of the whole plant operation at the time t;
executing the control optimizing of the coal consumption, and reserving N optimizing control schemes;
taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, and selecting the schemes of the N optimizing control schemes to generate a selected control scheme;
Device control of the thermoelectric device is performed by the selected control scheme.
2. The method of claim 1, wherein the method further comprises:
performing time sequence trend analysis based on the historical electricity consumption and heat supply information, and fitting to obtain a basic thermoelectric load;
setting an interception window, and reading window electricity and heat supply information in the interception window;
setting a fluctuation tolerance value of fluctuation according to the window electricity consumption and heat supply information;
a user thermoelectric load is generated based on the fluctuating tolerance value and the base thermoelectric load.
3. The method of claim 1, wherein the method further comprises:
reading historical operating data of the thermoelectric device;
performing equipment life cycle node division on the historical working data to generate a multi-node identifier;
setting a time attenuation coefficient, and calculating a data true value of the historical working data based on the time attenuation coefficient and the multi-node identifier to obtain a true value calculation result;
and performing operation stability calculation according to the true value calculation result and the historical working data to obtain equipment stability data.
4. A method as claimed in claim 3, wherein the method further comprises:
Performing abnormal call through the historical working data, and recording fault frequency, fault value, working fluctuation frequency and working fluctuation value, wherein the fault value is a representation value of the severity of the fault, and the working fluctuation value is a stable output representation value for generating power or heating;
carrying out real characteristic adjustment on the fault frequency, the fault value, the working fluctuation frequency and the working fluctuation value according to the real value calculation result;
and calculating the running stability of the equipment according to the adjusted fault frequency, the adjusted fault value, the adjusted working fluctuation frequency and the adjusted working fluctuation value, and obtaining equipment stability data.
5. The method of claim 1, wherein the method further comprises:
setting a duration threshold based on the user thermoelectric load;
judging whether the thermoelectric device calling time lengths in the N optimizing control schemes all meet the time length threshold value;
when thermoelectric equipment which cannot meet the time threshold exists in the thermoelectric equipment calling time, performing thermoelectric equipment abnormal calling identification corresponding to the optimizing control scheme;
and taking the abnormal call identification result as third reference data, and carrying out scheme selection of the N optimizing control schemes according to the first reference data, the second reference data and the third reference data to generate a selected control scheme.
6. The method of claim 5, wherein the method further comprises:
setting short-time start association coefficients of all thermoelectric devices based on the device basic information;
and setting an increment association value, calculating an influence value of the abnormal call according to the equipment abnormal call identifier, the short-time starting association coefficient and the increment association value, and generating the third reference data according to an influence value calculation result.
7. The method of claim 1, wherein the method further comprises:
performing equipment anomaly analysis based on the equipment control result and a preset fitting result;
performing abnormal clustering on the equipment abnormal analysis result;
generating overhaul feedback information through an abnormal clustering result;
and carrying out overhaul and maintenance on the thermoelectric equipment through the overhaul feedback information.
8. A thermal power plant control system in combination with a load optimization configuration, the system comprising:
the user thermoelectric load module is used for collecting historical electricity utilization and heat supply information of a user, analyzing the historical electricity utilization and heat supply information and generating a user thermoelectric load;
the parameter mapping library construction module is used for interacting equipment basic information of the thermoelectric equipment and constructing an equipment operation parameter mapping library according to the basic information, wherein the mapping parameters of the equipment operation parameter mapping library comprise the mapping of coal consumption, electric load and thermal load in unit time;
The coal consumption calculation module is used for carrying out fitting distribution of the thermal load and the electrical load based on the thermoelectric load, calling the equipment operation parameter mapping library and carrying out coal consumption calculation through a formula, wherein the calculation formula is as follows:
wherein B is total coal consumption, T is thermoelectric device operation time, < >>For the electrical load of the j-th thermoelectric device at time t,>for the thermal load of the j-th thermoelectric device at time t,the thermal load at time t is +.>The electrical load is +.>Is used for the fuel consumption of the fuel cell,the number of the thermoelectric devices is the number of the whole plant operation at the time t;
the optimizing control scheme module is used for executing control optimizing of the coal consumption and reserving N optimizing control schemes;
the selected control scheme module is used for taking the coal consumption of the N optimizing control schemes as first reference data, taking the equipment stability data of the N optimizing control schemes as second reference data, and carrying out scheme selection of the N optimizing control schemes to generate a selected control scheme;
a thermoelectric device control module for device control of the thermoelectric device by the selected control scheme.
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