CN111046018A

CN111046018A - Multi-objective optimization-based power generation equipment operation condition library construction method and device

Info

Publication number: CN111046018A
Application number: CN201911075866.8A
Authority: CN
Inventors: 刘双白; 朱龙飞; 张德利; 张春雷; 周卫庆
Original assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; Nanjing Institute of Technology
Current assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; Nanjing Institute of Technology
Priority date: 2019-11-06
Filing date: 2019-11-06
Publication date: 2020-04-21
Anticipated expiration: 2039-11-06
Also published as: CN111046018B

Abstract

The invention provides a method and a device for constructing a power generation equipment operation condition library based on multi-objective optimization, wherein the method comprises the following steps: acquiring historical working condition data of power generation equipment; establishing a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when the multi-element state estimation is carried out by utilizing a power generation equipment operation condition library; the multi-target optimization model is solved by using a multi-target genetic algorithm to obtain an optimal clustering class number, and then a power generation equipment operation condition library is obtained, wherein the power generation equipment operation condition library is obtained by using a data clustering algorithm to extract typical operation conditions of the historical operation condition data based on the optimal clustering class number, the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number, the extraction of the operation condition library can be completed from the angle of multi-target optimization by constructing the multi-target optimization model and using the multi-target genetic algorithm for optimization, and the estimation precision is high and the operation time is short when the multi-target optimization is carried out by using the operation condition library.

Description

Multi-objective optimization-based power generation equipment operation condition library construction method and device

Technical Field

The invention relates to the technical field of electric power, in particular to a method and a device for constructing a power generation equipment operation condition library based on multi-objective optimization.

Background

With the continuous improvement of the information automation degree of the power plant, a large amount of historical operating data of the unit is stored in a historical/real-time database, the application of the large amount of historical operating data of the unit processed by using a big data modeling technology in unit monitoring, diagnosis and optimization is also widely researched, and the research at the front edge is more and more focused on the aspects of accuracy and reliability of a data modeling method.

The multivariate state estimation method based on historical typical working conditions is called as a research hotspot in recent years, has the characteristics of simple structure and easy realization, is particularly suitable for engineering application, and the key to the successful application of the method is to obtain a stable and reliable working condition library.

At present, the following methods are mainly adopted in the aspect of typical working condition library construction research: the Min-Max method is that data extraction is carried out on historical operation data of the unit by manually setting a certain interval, and the maximum and minimum values are stored; the data clustering method is used for classifying the historical operation data of the unit by using a data clustering algorithm and finally determining a typical operation condition; the information quantity optimization method is characterized in that specific indexes are extracted according to the information quantity of data reaction, and sufficient data are searched to form a typical working condition library.

However, the working condition library constructed by the method cannot give consideration to both quantity and precision, so that the estimation precision is poor or the operation time is too long when the working condition library is used for multi-element state estimation, and the method is not beneficial to popularization and application.

Disclosure of Invention

The invention provides a method and a device for constructing a power generation equipment operation condition library based on multi-objective optimization, electronic equipment and a computer readable storage medium, which can at least partially solve the problems in the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme:

in a first aspect, a method for constructing a power generation equipment operation condition library based on multi-objective optimization is provided, which comprises the following steps:

acquiring historical working condition data of power generation equipment;

establishing a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when the multi-element state estimation is carried out by utilizing a power generation equipment operation condition library;

and solving the multi-target optimization model by using a multi-target genetic algorithm to obtain an optimal clustering class number so as to obtain a power generation equipment operation condition library, wherein the power generation equipment operation condition library is obtained by extracting typical operation conditions of the historical operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number.

Further, the multi-objective optimization model is as follows:

wherein, F_RMSE(x) Expressing estimation error, T (x) expressing estimation time, x expressing cluster number, L being minimum working condition number contained in power generation equipment operation working condition library, H beingAnd the maximum working condition number contained in the power generation equipment operation working condition library.

Further, the data clustering algorithm comprises: a density clustering-based method, an LDA clustering algorithm or a K-means clustering algorithm, and a fuzzy C-means clustering algorithm.

Further, the solving of the multi-objective optimization model by using the multi-objective genetic algorithm to obtain an optimal clustering class number includes:

generating an initial population, the initial population comprising a plurality of individuals;

clustering the historical working condition data by taking each individual as a clustering class number to obtain a plurality of corresponding alternative working condition libraries;

carrying out regression estimation on test data by utilizing an alternative working condition library to obtain a corresponding estimation value;

obtaining the estimation error and estimation time of the alternative working condition library according to the estimation value;

acquiring the fitness of the working condition library according to the estimation error and the estimation time;

selecting next generation individuals according to the fitness, and generating a new population after crossing and mutation;

and carrying out iterative calculation according to the new population until a termination condition is reached to obtain the optimal clustering number.

Further, the following formula is adopted when regression estimation is performed on test data by using an alternative working condition library to obtain a corresponding estimation value:

wherein the content of the first and second substances,

representing the corresponding estimated value of the candidate condition library i, D_iA library i of alternative operating conditions is represented,

and (5) representing test data of the application candidate condition library i.

Further, when the estimation error and estimation of the candidate condition library are obtained according to the estimation value, the following formula is adopted:

RMSE_iand (4) representing an estimation error corresponding to the alternative working condition library i, namely a root mean square error, and l representing the number of test data groups, wherein the estimation time is directly obtained by timing.

Further, the obtaining the fitness of the condition library according to the estimation error and the estimation time comprises:

judging whether the estimated time is less than a first preset threshold value;

if yes, the estimation error is used as the fitness of the working condition library.

Further, the obtaining the fitness of the condition library according to the estimation error and the estimation time further includes:

if not, judging whether the estimation time is less than a second preset threshold value;

and when the estimated time is less than a second preset threshold, calculating the fitness of the working condition library by adopting a fitness calculation formula.

Further, the fitness calculation formula is as follows:

g(x)＝F_RMSE(x)+λT(x)

wherein g (x) represents a fitness, F_RMSE(x) The estimation error is shown, T (x) shows the estimation time, x shows the cluster number, and lambda is a preset parameter.

Further, the next generation of individuals are selected according to the fitness, and a new population is generated after crossing and mutation, which comprises the following steps:

sequencing the alternative working condition libraries according to the sequence of the fitness from small to large;

selecting a preset number of individuals in the top ranking as a superior parent population;

carrying out genetic operation on the rest population to generate a new sub-population;

and combining the superior parent population and the new child population to obtain a new population.

Further, before clustering the historical operating condition data to obtain the operating condition library of the power generation equipment, the method further comprises the following steps:

and preprocessing the historical working condition data.

Further, the preprocessing the historical operating condition data includes:

and detecting and deleting the catastrophe points in the historical working condition data.

Further, the historical operating condition data comprises a plurality of mechanical performance monitoring parameter values of the power generation equipment;

the preprocessing of the historical working condition data comprises the following steps:

acquiring the fluctuation rate of a mechanical performance monitoring parameter value in the historical working condition data within a preset time interval;

judging whether the fluctuation rate is larger than a pre-acquired standard deviation or not;

and if not, deleting the historical working condition data in the preset time interval.

In a second aspect, a generating equipment operation condition library construction device based on multi-objective optimization is provided, which includes:

the historical working condition data acquisition module is used for acquiring historical working condition data of power generation equipment;

the modeling module is used for establishing a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when the multi-element state estimation is carried out by utilizing the power generation equipment operation condition library;

and the optimizing module is used for solving the multi-target optimization model by using a multi-target genetic algorithm to obtain an optimal clustering class number so as to obtain a power generation equipment operation condition library, the power generation equipment operation condition library is obtained by extracting typical operation conditions of the historical operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number.

Further, the optimizing module comprises:

an initial population generating unit that generates an initial population including a plurality of individuals;

the clustering unit is used for clustering the historical working condition data by taking each individual as a clustering class number to obtain a plurality of corresponding alternative working condition libraries;

the regression estimation unit is used for carrying out regression estimation on test data by utilizing an alternative working condition library to obtain a corresponding estimation value;

the first calculation unit is used for acquiring the estimation error and the estimation time of the alternative working condition library according to the estimation value;

the second calculating unit is used for acquiring the fitness of the working condition library according to the estimation error and the estimation time;

the population updating unit is used for selecting next generation individuals according to the fitness, and generating a new population after crossing and mutation;

and the iterative calculation unit is used for performing iterative calculation according to the new population until a termination condition is reached to obtain the optimal clustering class number.

Further, the second calculation unit includes:

a first judgment subunit for judging whether the estimation time is less than a first preset threshold value;

and the first fitness calculating subunit takes the estimation error as the fitness of the working condition library if the estimation time consumption is less than a first preset threshold value.

Further, the second calculation unit further includes:

a second judgment subunit, if the estimated time is not less than the first preset threshold, judging whether the estimated time is less than a second preset threshold;

and the second fitness calculating subunit calculates the fitness of the working condition library by adopting a fitness calculating formula when the estimated time is less than a second preset threshold.

Further, the population updating unit includes:

the sequencing subunit sequences the alternative working condition libraries according to the sequence of the fitness from small to large;

selecting a child unit from the superior parent population, and selecting a preset number of individuals ranked in the front as the superior parent population;

a sub-population generating subunit for performing genetic operation on the remaining population to generate a new sub-population;

and a population updating child unit for combining the superior parent population and the new child population to obtain a new population.

Further, still include:

and the data preprocessing module is used for preprocessing the historical working condition data.

Further, the data preprocessing module comprises:

and the mutation point removing unit is used for detecting and deleting the mutation points in the historical working condition data.

the data preprocessing module comprises:

the fluctuation rate acquisition unit is used for acquiring the fluctuation rate of a mechanical performance monitoring parameter value in the historical working condition data within a preset time interval;

a fluctuation rate judging unit for judging whether the fluctuation rate is larger than a pre-acquired standard deviation;

and the fluctuation data deleting unit deletes the historical working condition data in the preset time interval if the fluctuation rate is not greater than the pre-acquired standard deviation.

In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing when executing the program:

acquiring historical working condition data of power generation equipment;

In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements:

acquiring historical working condition data of power generation equipment;

The invention provides a method and a device for constructing a power generation equipment operation condition library based on multi-objective optimization, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring historical working condition data of power generation equipment; establishing a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when the multi-element state estimation is carried out by utilizing a power generation equipment operation condition library; and solving the multi-target optimization model by using a multi-target genetic algorithm to obtain an optimal clustering class number so as to obtain a power generation equipment operation condition library, wherein the power generation equipment operation condition library is obtained by extracting typical operation conditions of the historical operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number. The method comprises the steps of establishing a multi-target optimization model, optimizing by using a multi-target genetic algorithm, extracting an operation condition library from the multi-target optimization angle, enabling the obtained operation condition library to be used for purposes such as state monitoring and modeling, meeting the application requirements of the operation condition library, and solving the problems that the estimation precision is poor or the operation time is too long when the operation condition library is used for multi-state estimation, and the popularization and the application are not facilitated due to the fact that the quantity and the precision cannot be considered at the same time.

In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. In the drawings:

FIG. 1 is a schematic diagram of an architecture between a server S1 and a client device B1 according to an embodiment of the present invention;

FIG. 2 is a block diagram of the server S1, the client device B1 and the database server S2 according to an embodiment of the present invention;

FIG. 3 is a first flowchart of a method for constructing a power generation equipment operating condition library based on multi-objective optimization according to an embodiment of the present invention;

fig. 4 shows the specific steps of step S300 in fig. 3;

FIG. 5 shows the detailed steps of step S350 in FIG. 4;

fig. 6 shows the detailed steps of step S370 in fig. 4;

fig. 7 is a comparison graph of an evaluation value and an actual measurement value after evaluation of test data is performed by using a power generation equipment operation condition library constructed by the multi-objective optimization-based power generation equipment operation condition library construction method provided by the embodiment of the invention;

FIG. 8 is a second flowchart illustrating a method for constructing a multi-objective optimization-based power plant operation condition library according to an embodiment of the present invention;

fig. 9 shows a specific step of step S10 in fig. 8;

FIG. 10 is a first block diagram of a device for constructing a multi-objective optimization-based power generation equipment operating condition library according to an embodiment of the present invention;

FIG. 11 is a structural block diagram II of a generating equipment operating condition library construction device based on multi-objective optimization in the embodiment of the invention;

fig. 12 is a block diagram of an electronic device according to an embodiment of the invention.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

The working condition library constructed in the prior art cannot give consideration to both quantity and precision, so that the estimation precision is poor or the operation time is too long when the working condition library is used for multi-element state estimation, and the popularization and the application are not facilitated.

In order to solve the technical problems in the prior art, embodiments of the present invention provide a method for constructing a power generation equipment operating condition library based on multi-objective optimization, wherein a multi-objective optimization model is constructed, optimization is performed by using a multi-objective genetic algorithm, extraction of the operating condition library can be completed from the multi-objective optimization angle, and the obtained operating condition library can be used for state monitoring, modeling, and the like, so as to meet the application requirements of the operating condition library, and solve the problems that the number and the precision cannot be considered, and the estimation precision is poor or the operation time is too long when the operating condition library is used for performing multi-element state estimation, and the popularization and the application are not facilitated.

In view of the above, the present application provides a multi-objective optimization-based power generation equipment operation condition library construction apparatus, which may be a server S1, referring to fig. 1, the server S1 may be communicatively connected to at least one client device B1, the client device B1 may send condition data to the server S1, and the server S1 may receive the condition data online. The server S1 can preprocess the acquired working condition data online or offline, and establishes a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when a power generation equipment operation working condition library is used for carrying out multi-element state estimation; and solving the multi-target optimization model by using a multi-target genetic algorithm to obtain an optimal clustering class number so as to obtain a power generation equipment operation condition library, wherein the power generation equipment operation condition library is obtained by extracting typical operation conditions of the operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number.

Additionally, referring to FIG. 2, the server S1 may also be communicatively coupled to at least one database server S2, the database server S2 being configured to store historical operating condition data and the client device B1 setting the required parameters. The database server S2 sends the historical working condition data to the server S1 on line, and the server S1 can receive the historical working condition data on line and then construct a power generation equipment operation working condition library according to the historical working condition data.

It is understood that the client device B1 may include a temperature sensor, a voltage meter, an ammeter, and other detection devices.

The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.

FIG. 3 is a first flowchart of a method for constructing a power generation equipment operating condition library based on multi-objective optimization in the embodiment of the present invention. As shown in FIG. 3, the method for constructing the multi-objective optimization-based power generation equipment operating condition library can comprise the following steps:

step S100: historical operating condition data of a power generation device is obtained.

According to the operation principle and the characteristics of the power generation equipment, measuring points of the power generation equipment are sorted, a plurality of parameters related to the operation characteristics of the power generation equipment are selected to form a state vector of the equipment, for example, for a coal mill, the parameters related to the mechanical operation of the coal mill mainly comprise the bearing temperature of a reduction gearbox, the lubricating oil temperature, the lubricating oil pressure, the motor current and the like, and the parameters can be combined into mechanical performance monitoring parameters of the coal mill to be used as working condition library parameters representing the mechanical performance of the coal mill.

And then, acquiring historical working condition data of continuous operation of the equipment within a period of time or within a plurality of periods of time according to the determined working condition library parameters, wherein the historical data acquisition principle is that all working conditions must cover the full working condition range of unit operation. For example, for a coal mill, the most important criteria to pick are the unit load and the mill motor current, i.e., for a coal mill, the unit load and the mill current must cover the maximum and minimum operating values.

Step S200: establishing a multi-objective optimization model by taking the minimum estimation error and the minimum estimation time as targets when the multi-element state estimation is carried out by utilizing a power generation equipment operation condition library;

wherein two optimization objectives are determined: 1. the estimation effect is best, generally, the coverage range is wider as the number of the working conditions contained in the working condition library is larger, and the probability that a new working condition is searched to be similar to the new working condition is larger, so that the corresponding estimation effect is better; 2. the calculation time is shortest, and the number of working condition vectors contained in the working condition library in the actual calculation process is smaller, so that the number of working conditions participating in calculation is smaller, and the corresponding calculation speed is higher.

Specifically, the number of the finally determined working conditions (or called working condition types) contained in the power generation equipment operation working condition library is used as an optimization variable x, the estimation effect is expressed by the root mean square RMSE of the error, the determined optimization target is the root mean square minimum of the error and the estimation time T minimum, and the multi-objective optimization model is as follows:

wherein, F_RMSE(x) And T (x) represents an estimation error, x represents a clustering class number when the estimation is used, L is the minimum working condition number contained in the power generation equipment operating condition library, and H is the maximum working condition number contained in the power generation equipment operating condition library. Wherein H and L are preset values.

Step S300: and solving the multi-target optimization model by using a multi-target genetic algorithm to obtain an optimal clustering class number so as to obtain a power generation equipment operation condition library, wherein the power generation equipment operation condition library is obtained by extracting typical operation conditions of the historical operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number.

The multi-target genetic algorithm HSGA _ II is adopted for solving, the algorithm can be specifically realized by adopting the existing open source software, such as MATLAB, or realized by adopting a python genetic algorithm toolbox, and a user only needs to input data and set iteration times.

The data clustering algorithm comprises the following steps: a density clustering-based method, an LDA clustering algorithm or a K-means clustering algorithm, a fuzzy C-mean clustering algorithm and the like.

According to the method for constructing the power generation equipment operation condition library based on multi-objective optimization, provided by the embodiment of the invention, the multi-objective optimization model is constructed, the multi-objective genetic algorithm is utilized for optimization, the operation condition library can be extracted from the multi-objective optimization angle, the obtained operation condition library can be used for state monitoring, modeling and other purposes, the application requirements of the operation condition library are met, and the problems that the estimation precision is poor or the operation time is too long when the operation condition library is utilized for multi-element state estimation due to the fact that the quantity and the precision cannot be considered at the same time, and the popularization and the application are not facilitated are solved.

Fig. 4 shows the specific steps of step S300 in fig. 3. As shown in fig. 4, this step S300 may include the following:

step S310: an initial population is generated, the initial population comprising a plurality of individuals.

The number of the individual units is selected according to the application requirement, for example, 30 to 120, such as 50, 80, 100, etc., can be selected.

Step S320: and clustering the historical working condition data by taking each individual as a clustering class number to obtain a plurality of corresponding alternative working condition libraries.

Wherein, a density-based clustering method or other clustering algorithms can be adopted.

Step S330: and performing regression estimation on test data by using an alternative working condition library to obtain a corresponding estimation value.

Specifically, the following formula is adopted:

wherein the content of the first and second substances,

corresponding estimation for representing candidate working condition library iEvaluation, D_iA library i of alternative operating conditions is represented,

Step S340: and obtaining the estimation error and the estimation time of the alternative working condition library according to the estimation value.

Specifically, the following formula is adopted:

Step S350: and acquiring the fitness of the working condition library according to the estimation error and the estimation time.

Step S360: and judging whether a termination condition is reached.

If yes, go to step S380, otherwise go to step S370.

The termination condition may be iteration times, that is, whether the iteration times reach a preset iteration time is judged, if yes, the genetic algorithm is terminated, the currently obtained optimal solution is used as an optimal clustering class number, if not, the total cluster is updated, and then the steps S320 to S370 are repeatedly executed until the termination condition is reached, that is: and carrying out iterative calculation according to the new population until a termination condition is reached to obtain the optimal clustering number.

The iteration times are set by a user according to actual requirements, the higher the iteration times, the better the accuracy of the obtained working condition library, but the longer the processing time, the lower the iteration times, the shorter the processing time, but the worse the accuracy of the obtained working condition library, and for a distance, the iteration times may be set to 8 to 50 times, such as 10, 20, 30, 40, and the like.

Step S370: and selecting next generation individuals according to the fitness, and generating a new population after crossing and mutation.

Step S380: and obtaining the optimal clustering class number according to the fitness.

By optimizing the multi-objective optimization model by adopting the method, the optimal solution can be obtained in the shortest time, and the efficiency of the method is improved.

Fig. 5 shows a specific step of step S350 in fig. 4. Referring to fig. 5, this step S350 may include the following:

step S351: judging whether the estimated time is less than a first preset threshold value or not;

if so, go to step S352, otherwise, go to step S353.

Specifically, the first preset threshold is set by the user according to the precision requirement, and may be, for example, between 0.5S and 3S, such as 1S, 2S, 2.5S, and the like.

Step S352: and taking the estimation error as the fitness of the working condition library.

Step S353: judging whether the estimated time is less than a second preset threshold value or not;

if yes, go to step S354, otherwise go to step S355.

Specifically, the second preset threshold is set by the user according to the precision requirement, and may be, for example, between 4S and 7S, such as 5S, 6S, 5.5S, and the like.

Step S354: and calculating the fitness of the working condition library by adopting a fitness calculation formula.

The fitness calculation formula is as follows:

g(x)＝F_RMSE(x)+λT(x)

wherein g (x) represents a fitness, F_RMSE(x) And T (x) represents an estimation error, x represents a clustering class number when the estimation is used, and lambda is a preset parameter and is set by a user.

Step S355: the initial population is regenerated and returns to step S320.

Fig. 6 shows the specific steps of step S370 in fig. 4. Referring to fig. 6, the step S370 specifically includes the following steps:

step S371: and sequencing the alternative working condition libraries according to the sequence of the fitness from small to large.

Step S372: and selecting the individuals with the preset number ranked in the front as the superior parent population.

Step S373: and (4) carrying out genetic operation on the rest population to generate a new sub-population.

Step S374: and combining the superior parent population and the new child population to obtain a new population.

The following describes, by way of example, a process of solving the multi-objective optimization model by using a multi-objective genetic algorithm to obtain an optimal cluster class number:

(1) an initial population is generated, assumed to include n individuals, denoted x₁,x₂,…,x_n；

(2) Selecting a method based on density clustering, and dividing each x_iClustering the historical working condition data as a clustering class number to obtain n kinds of alternative working condition libraries D₁,…,D_n；

(3) Using a library D of conditions for each based on the estimation principle_iThe regression estimation is carried out on the test data, and the calculation formula of the multivariate state estimation is adopted as

And calculating the root mean square error RMSEi corresponding to each working condition library and the corresponding estimation time ti.

(4) Solving the obtained Pareto leading edge solution set { D_j,RMSE_j,t_j,x_jJ ═ 1, …, p } is evaluated for fitness calculation, using the following principle:

① if t_jLess than or equal to 1s, adding RMSE_jAs the fitness of the working condition library;

② if 1s<t_j<5s, calculating the fitness of the working condition library by adopting a weighting mode;

in particular, if 1s<t_jThe requirement on the model precision in the calculation time period is considered to be higher, and lambda is taken to be 0.1; if 2s<t_jNot more than 3, taking lambda as 0.2; if 3s<t_jAnd (5) considering that the specific gravity of the calculation time needs to be increased in the calculation time period, and taking lambda as 0.3.

③ if t_jIf the time is more than or equal to 5s, the working condition library is considered to be unsuitable,the working condition library is not used as a candidate working condition library;

in addition, when t corresponds to all the initial working condition libraries_jAnd if the time is more than or equal to 5 seconds, the initial population is considered to need to be adjusted, the initial population is returned to be regenerated, and the steps are executed again. This is an extreme case and all D matrices are too large resulting in too long a computation time, regardless of the optimal solution falling within this range.

(5) Sorting the Pareto front edge solution sets according to the fitness;

(6) selecting the individuals in the top 50% of the sequence as the superior parent population, and carrying out genetic operation on the rest populations, including selection, crossing and variation, to generate a new sub population, wherein the new generation population is the sum of the superior parent population and the sub population;

(7) judging whether a termination condition is reached, and outputting an optimal solution in the current population if the termination condition is reached; if the termination condition is not met, repeating the steps 2-6.

Specifically, a group of parameters is used for representing the running state of a cold end system of a certain unit, and 21 parameters including condenser vacuum, circulating water pump motor current, circulating water pump outlet pressure, condenser end difference, condensate pump motor current, condensate pump outlet pressure and the like are selected. And selecting 20000 groups of normal operation historical data, constructing a system working condition library by applying the invention to the front 18000 groups of data, and carrying out verification calculation on the rear 2000 groups of data.

The appropriate parameters are selected and optimized by using the NSGA-II genetic algorithm, and the obtained results are shown in table 1 and fig. 7, so that the evaluation value of the power generation equipment operation condition library constructed by the method for constructing the power generation equipment operation condition library based on the multi-objective optimization provided by the embodiment of the invention after evaluating the test data is very close to the measured value, thereby verifying that the power generation equipment operation condition library constructed by the method for constructing the power generation equipment operation condition library based on the multi-objective optimization provided by the embodiment of the invention has high estimation precision and short operation time.

TABLE 1 optimization results

FIG. 8 is a second flowchart illustrating a method for constructing a multi-objective optimization-based power plant operation condition library according to an embodiment of the present invention; as shown in fig. 8, the method for constructing a library of operating conditions of power generation equipment based on multi-objective optimization may further include, based on the steps included in the method for constructing a library of operating conditions of power generation equipment based on multi-objective optimization shown in fig. 3:

step S10: and preprocessing the historical working condition data.

The method comprises the steps of preprocessing historical working condition data, wherein the preprocessing comprises the detection and elimination of data mutation points and the detection of data fluctuation rate, once the data mutation points are detected, the group of data is directly eliminated, the data fluctuation rate is considered to be abnormal data according to the standard deviation of each parameter measurement, namely the fluctuation of certain parameter data in a certain period of time is smaller than the standard deviation, and all data vectors in the period of continuous time are eliminated.

Specifically, referring to fig. 9, the historical operating condition data includes various mechanical performance monitoring parameter values of the power generation equipment, and the step S10 may include the following technical contents:

step S11: and acquiring the fluctuation rate of a mechanical performance monitoring parameter value in the historical working condition data within a preset time interval.

The fluctuation rate is obtained by dividing the change value of the mechanical performance monitoring parameter value in the preset time interval by the time.

Step S12: judging whether the fluctuation rate is greater than a pre-acquired standard deviation or not;

if yes, the preprocessing flow is ended, and if no, step S13 is executed.

Step S13: and deleting the historical working condition data in the preset time interval.

In an alternative embodiment, the step S10 may further include: and detecting and deleting the catastrophe points in the historical working condition data.

The historical working condition data are preprocessed, abnormal data are eliminated, and the precision of a follow-up process can be effectively improved.

Based on the same inventive concept, the embodiment of the present application further provides a device for constructing a power generation equipment operation condition library based on multi-objective optimization, which can be used for implementing the method described in the above embodiment, as described in the following embodiments. Because the principle of solving the problems of the generating equipment operation condition library construction device based on multi-objective optimization is similar to that of the method, the implementation of the generating equipment operation condition library construction device based on multi-objective optimization can refer to the implementation of the method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

FIG. 10 is a first block diagram of a device for constructing a multi-objective optimization-based power generation equipment operating condition library according to an embodiment of the present invention. As shown in fig. 10, the apparatus for constructing a library of operating conditions of a power generation facility based on multi-objective optimization includes: the system comprises a historical operating condition data acquisition module 10, a modeling module 20 and an optimization module 30.

The historical operating condition data acquisition module 10 acquires historical operating condition data of a power generation device.

The modeling module 20 establishes a multi-objective optimization model with the objective of minimizing estimation error and minimizing estimation time when the power generation equipment operation condition library is used for multi-element state estimation.

The optimizing module 30 uses a multi-target genetic algorithm to solve the multi-target optimization model to obtain an optimal clustering class number, so as to obtain a power generation equipment operation condition library, wherein the power generation equipment operation condition library is obtained by extracting typical operation conditions of the historical operation condition data based on the optimal clustering class number by using a data clustering algorithm, and the number of the typical operation conditions in the power generation equipment operation condition library is equal to the optimal clustering class number.

The multi-objective optimization-based power generation equipment operation condition library construction device provided by the embodiment of the invention can complete extraction of the operation condition library from the multi-objective optimization angle by constructing a multi-objective optimization model and optimizing by using a multi-objective genetic algorithm, and the obtained operation condition library can be used for state monitoring, modeling and other purposes, so that the application requirements of the operation condition library are met, and the problems that the estimation precision is poor or the operation time is too long when the operation condition library is used for multi-element state estimation due to the fact that the quantity and the precision cannot be considered at the same time, and the popularization and the application are not facilitated are solved.

In an alternative embodiment, the optimizing module 30 may include: the device comprises an initial population generating unit, a clustering unit, a regression estimating unit, a first calculating unit, a second calculating unit, a population updating unit and an iterative calculating unit.

An initial population generating unit generates an initial population, wherein the initial population comprises a plurality of individuals;

Clustering each individual as a clustering class number to cluster the historical working condition data to obtain a plurality of corresponding alternative working condition libraries by the clustering unit;

The regression estimation unit performs regression estimation on test data by using an alternative working condition library to obtain a corresponding estimation value;

specifically, the following formula is adopted:

wherein the content of the first and second substances,

The first calculation unit acquires the estimation error and the estimation time of the alternative working condition library according to the estimation value;

specifically, the following formula is adopted:

The second calculating unit acquires the fitness of the working condition library according to the estimation error and the estimation time;

the population updating unit selects the next generation of individuals according to the fitness, and generates a new population after crossing and mutation;

and the iterative calculation unit performs iterative calculation according to the new population until a termination condition is reached to obtain the optimal clustering class number.

The termination condition can be iteration times, namely whether the iteration times reach a preset iteration time is judged, if yes, the genetic algorithm is terminated, the currently obtained optimal solution is used as the optimal clustering class number, if not, the total cluster is updated, and then all modules work repeatedly until the termination condition is reached, namely: and carrying out iterative calculation according to the new population until a termination condition is reached to obtain the optimal clustering number.

As can be understood by those skilled in the art, by optimizing the multi-objective optimization model by adopting the method, the optimal solution can be obtained in the shortest time, and the efficiency of the method is improved.

In an alternative embodiment, the second computing unit comprises: the device comprises a first judgment subunit, a second judgment subunit, a first adaptability calculation subunit and a second adaptability calculation subunit.

The first judging subunit judges whether the estimation time is less than a first preset threshold value;

And if the estimation time is less than a first preset threshold value, the first fitness calculating subunit takes the estimation error as the fitness of the working condition library.

If the estimated time is not less than the first preset threshold, the second judgment subunit judges whether the estimated time is less than a second preset threshold;

And when the estimated time is less than a second preset threshold value, the second adaptability calculation subunit calculates the adaptability of the working condition library by adopting an adaptability calculation formula.

The fitness calculation formula is as follows:

g(x)＝F_RMSE(x)+λT(x)

In an alternative embodiment, the population update unit includes: the system comprises a sequencing subunit, a superior parent population selecting subunit, a child population generating subunit and a population updating subunit.

The sorting subunit sorts the alternative working condition libraries according to the sequence of the fitness from small to large;

the superior parent population selection child unit selects individuals with preset number in the top sequence as the superior parent population;

the sub-population generating subunit performs genetic operation on the rest population to generate a new sub-population;

and the population updating child unit combines the superior parent population and the new child population to obtain a new population.

FIG. 11 is a structural block diagram II of a power generation equipment operating condition library construction device based on multi-objective optimization in the embodiment of the invention. As shown in fig. 11, the apparatus for constructing a library of operating conditions of a power generation device based on multi-objective optimization may further include, on the basis of the modules shown in fig. 10: and the data preprocessing module 1.

And the data preprocessing module 1 is used for preprocessing the historical working condition data.

In an alternative embodiment, the data preprocessing module comprises: and the mutation point removing unit is used for detecting and deleting mutation points in the historical working condition data. The data preprocessing module comprises: the device comprises a fluctuation rate acquisition unit, a fluctuation rate judgment unit and a fluctuation data deletion unit.

The fluctuation rate obtaining unit obtains the fluctuation rate of a mechanical performance monitoring parameter value in the historical working condition data within a preset time interval;

The fluctuation rate judging unit judges whether the fluctuation rate is larger than a pre-acquired standard deviation or not;

and if the fluctuation rate is not greater than the pre-acquired standard deviation, the fluctuation data deleting unit deletes the historical working condition data in the preset time interval.

In an alternative embodiment, the historical operating condition data includes a plurality of mechanical performance monitoring parameter values for the power generation equipment;

The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the program:

acquiring historical working condition data of power generation equipment;

From the above description, the electronic device provided by the embodiment of the invention can be used for constructing the power generation equipment operation condition library, constructing the multi-objective optimization model, performing optimization by using the multi-objective genetic algorithm, extracting the operation condition library from the multi-objective optimization angle, and using the obtained operation condition library for state monitoring, modeling and other purposes, so that the requirement of application of the operation condition library is met, and the problems that the estimation precision is poor or the operation time is too long when the operation condition library is used for multi-element state estimation due to the fact that the quantity and the precision cannot be considered at the same time and the popularization and application are not facilitated are solved.

Referring now to FIG. 12, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.

As shown in fig. 12, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.

In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the invention includes a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring historical working condition data of power generation equipment;

As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present invention can be used for constructing a power generation equipment operation condition library, constructing a multi-objective optimization model, performing optimization by using a multi-objective genetic algorithm, and completing extraction of the operation condition library from the multi-objective optimization perspective, where the obtained operation condition library can be used for state monitoring, modeling, and the like, so as to meet the requirements of application of the operation condition library, and solve the problems that the estimation accuracy is poor or the operation time is too long when the operation condition library is used for performing multivariate state estimation, which cannot give consideration to both quantity and accuracy, and is not beneficial to popularization and application.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A multi-objective optimization-based power generation equipment operation condition library construction method is characterized by comprising the following steps:

acquiring historical working condition data of power generation equipment;

2. The multi-objective optimization-based power generation equipment operating condition library construction method according to claim 1, wherein the multi-objective optimization model is as follows:

wherein, F_RMSE(x) And T (x) represents an estimation error, x represents a clustering class number when the estimation is used, L is the minimum working condition number contained in the power generation equipment operating condition library, and H is the maximum working condition number contained in the power generation equipment operating condition library.

3. The multi-objective optimization-based power generation equipment operating condition library construction method according to claim 1, wherein the data clustering algorithm comprises: a density clustering-based method, an LDA clustering algorithm or a K-means clustering algorithm, and a fuzzy C-means clustering algorithm.

4. The multi-objective optimization-based power generation equipment operation condition library construction method according to claim 1, wherein the solving of the multi-objective optimization model by using the multi-objective genetic algorithm to obtain the optimal clustering class number comprises the following steps:

5. The method for constructing the multi-objective optimization-based power generation equipment operation condition library according to claim 4, wherein the following formula is adopted when regression estimation is performed on test data by using an alternative condition library to obtain a corresponding estimation value:

wherein the content of the first and second substances,

6. The method for constructing the multi-objective optimization-based power generation equipment operation condition library according to claim 5, wherein when the estimation error and the estimation time of the alternative condition library are obtained according to the estimation value, the following formula is adopted:

7. The method for constructing the multi-objective optimization-based power generation equipment operation condition library according to claim 5, wherein the step of obtaining the fitness of the condition library according to the estimation error and the estimation time comprises the following steps:

judging whether the estimated time is less than a first preset threshold value or not;

and if so, taking the estimation error as the fitness of the working condition library.

8. The method for constructing a multi-objective optimization-based power generation equipment operating condition library according to claim 7, wherein the step of obtaining the fitness of the operating condition library according to the estimation error and the estimation time further comprises the steps of:

and when the estimated time consumption is less than a second preset threshold value, calculating the fitness of the working condition library by adopting a fitness calculation formula.

9. The multi-objective optimization-based power generation equipment operating condition library construction method according to claim 8, wherein the fitness calculation formula is as follows:

g(x)＝F_RMSE(x)+λT(x)

10. The method for constructing the multi-objective optimization-based power generation equipment operating condition library according to claim 5, wherein the step of selecting next generation individuals according to the fitness, and generating a new population after crossing and mutation comprises the following steps:

11. The multi-objective optimization-based power generation equipment operation condition library construction method according to claim 1, wherein before clustering the historical condition data to obtain the power generation equipment operation condition library, the method further comprises the following steps:

and preprocessing the historical working condition data.

12. The multi-objective optimization-based power generation equipment operating condition library construction method according to claim 11, wherein the preprocessing of the historical operating condition data comprises:

13. The multi-objective optimization-based power generation equipment operation condition library construction method according to claim 11, wherein the historical condition data comprises a plurality of mechanical performance monitoring parameter values of the power generation equipment;

judging whether the fluctuation rate is greater than a pre-acquired standard deviation or not;

14. A generating equipment operation condition library construction device based on multi-objective optimization is characterized by comprising the following steps:

15. The multiobjective optimization-based power generation equipment operating condition library construction device according to claim 14, wherein the data clustering algorithm comprises: a density clustering-based method, an LDA clustering algorithm or a K-means clustering algorithm, and a fuzzy C-means clustering algorithm.

16. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 14, wherein the optimizing module comprises:

an initial population generating unit that generates an initial population, the initial population including a plurality of individuals;

17. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 16, wherein the second calculation unit comprises:

a first judgment subunit that judges whether the estimation time is less than a first preset threshold;

18. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 17, wherein the second calculation unit further comprises:

a second judgment subunit, configured to, if the estimated time is not less than the first preset threshold, judge whether the estimated time is less than a second preset threshold;

and the second adaptability calculation subunit calculates the adaptability of the working condition library by adopting an adaptability calculation formula when the estimated time is less than a second preset threshold value.

19. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 16, wherein the population updating unit comprises:

and the population updating child unit is used for combining the superior parent population and the new child population to obtain a new population.

20. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 14, further comprising:

21. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 20, wherein the data preprocessing module comprises:

and the mutation point removing unit is used for detecting and deleting mutation points in the historical working condition data.

22. The multi-objective optimization-based power generation equipment operating condition library construction device according to claim 21, wherein the historical operating condition data comprises a plurality of mechanical performance monitoring parameter values of the power generation equipment;

the data preprocessing module comprises:

23. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for constructing a library of operating conditions of a power generation device based on multi-objective optimization according to any one of claims 1 to 13 when executing the program.

24. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for constructing a library of operating conditions of a multi-objective optimization-based power generation plant according to any one of claims 1 to 13.