CN112990552A - Equipment operation parameter short-time prediction method and system based on change rate - Google Patents

Equipment operation parameter short-time prediction method and system based on change rate Download PDF

Info

Publication number
CN112990552A
CN112990552A CN202110195530.6A CN202110195530A CN112990552A CN 112990552 A CN112990552 A CN 112990552A CN 202110195530 A CN202110195530 A CN 202110195530A CN 112990552 A CN112990552 A CN 112990552A
Authority
CN
China
Prior art keywords
change
rate
trend
time
change rate
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110195530.6A
Other languages
Chinese (zh)
Inventor
常峰
吴丞冉
周振波
李帅
崔孟立
姜渭国
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nodal Internet Beijing Technology Co ltd
Original Assignee
Nodal Internet Beijing Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nodal Internet Beijing Technology Co ltd filed Critical Nodal Internet Beijing Technology Co ltd
Priority to CN202110195530.6A priority Critical patent/CN112990552A/en
Publication of CN112990552A publication Critical patent/CN112990552A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Economics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • General Business, Economics & Management (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the invention discloses a method and a system for predicting equipment operation parameters in a short time based on a change rate, and relates to the technical field of equipment management. The method firstly takes the current time as t1Forward respectively calculating the change rate delta P of the running parameter P at adjacent time by interval time t valueiJudging the variation trend of the operation parameters by using the positive and negative sequence of the values of the variation rate, and endowing each variation rate delta P according to the variation trend of the operation parameters P of the equipmentiCorresponding weight ωiThen, the predicted change rate Δ P in different change trends is calculated based on the change rate and the weight, and then the change of the operating parameter P in the future Δ t time is calculated using the predicted change rates in different change trends. According to the scheme, the short-time prediction is carried out on the equipment operation parameters through the change rate, the algorithm structure is simple, and the algorithm does not need to be largeThe method has the advantages of measuring sample data, being slightly influenced by other parameters, being easy to deploy, effectively assisting equipment management personnel to predict the change trend of equipment parameters in time and ensuring the good operation of each equipment.

Description

Equipment operation parameter short-time prediction method and system based on change rate
Technical Field
The embodiment of the invention relates to the technical field of equipment management, in particular to a method and a system for predicting equipment operation parameters in a short time based on a change rate.
Background
The operation conditions of process industries (such as power plants, refinery plants, chemical plants and the like) are complex and changeable, and in order to ensure safe and good operation of each device, operators often monitor and judge the operation parameters of the device by means of systems such as DCS (distributed control system), SIS (system information system) and the like. However, due to the complexity of the operation parameters of the equipment, it is difficult for operators to have enough energy and reliable experience to predict the operation trend of each parameter, and equipment failure usually occurs when a system alarms, even inevitable loss is caused.
With the development of computer technology and related data prediction theory, many methods have corresponding application in the aspect of equipment failure and operation trend prediction, but also face many difficulties and have many defects. For example, parameter estimation can be realized by using a newton iteration algorithm and maximization of complete data, but the method is complex in calculation, so that the practical application is difficult; the failure time of the parameters can be predicted by a curve fitting method, but the inference generates a large error due to the fact that available historical data is short or incomplete; the intelligent prediction method based on the neural network also has the problems of difficult field deployment and insufficient flexibility and accuracy due to the limitations that the network structure is difficult to determine, a large number of samples are required, a large number of computing resources are occupied and the like.
Based on the defects in the prior art, the invention provides a method and a system for predicting the device operation parameters in a short time based on the change rate by analyzing the operation data of the enterprise field devices and investigating the habit of monitoring the operation parameters by operators, which are used for assisting the device managers to predict the change trend of the device parameters in a short time so as to make treatment measures in advance.
Disclosure of Invention
The invention provides a method and a system for short-time prediction of equipment operation parameters based on a change rate.
In order to achieve the purpose, the invention discloses the following technical scheme:
the invention provides a short-time prediction method of equipment operation parameters based on a change rate, which comprises the following steps:
step 1, determining an equipment operation parameter P to be predicted, and setting the current time as t1From t1The change rate of the running parameters of the adjacent time is calculated by respectively taking the values of the interval time t forward from the moment, namely:
Figure BDA0002945446120000021
to obtain delta P1、ΔP2、…、ΔPn；
Step 2, utilizing the change rate delta P1、ΔP2、…、ΔPnJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
step 3, endowing each change rate delta P according to the change trend of the equipment operation parameters PiCorresponding weight ωiCorrecting the predicted trend of the operation parameters by the weight;
step 4, calculating the predicted change rate delta P under different change trends based on the change rate and the weight;
and 5, calculating the change of the operating parameter P in the future delta tt time by using the predicted change rates of different change trends.
Based on the above scheme, further, in the step 2, the change rate Δ P is used1、ΔP2、…、ΔPnThe positive and negative sequence of each value judges the variation trend of the operation parameter, and the judgment rule is as follows:
when Δ P1、ΔP2、…、ΔPnThe number of continuous positive values in each value is a set ratio m/n of the total number and above, wherein m is a positive integer, m is less than or equal to n, and Δ P1、ΔP2、…、ΔPmWhen the values are all not negative values, the continuous rising trend is realized;
when Δ P1、ΔP2、…、ΔPnThe number of continuous negative values in each value is a set ratio of total number m/n and above, where m is a positive integer, m is not more than n, and Δ P1、ΔP2、…、ΔPmWhen all the values are not positive values, the trend is continuously descending;
when Δ P1、ΔP2、…、ΔPnWhen each value does not satisfy the continuous rising trend and the continuous falling trend, the value is a fluctuation trend.
Furthermore, in the step 3, each change rate Δ P is given according to the variation trend of the equipment operation parameter PiCorresponding weight ωiThe weighting rule is as follows:
for a continuously rising trend, the closer to the current time t1The larger the weight of the change rate of the negative value is, and meanwhile, in order to balance the influence of the change rate of the negative value on the operation trend of the parameter, the weight of the change rate of the negative value is properly increased;
for a continuously decreasing trend, the closer to the current time t1The larger the weight of the change rate of the positive value is, and meanwhile, in order to balance the influence of the change rate of the positive value on the operation trend of the parameter, the weight of the change rate of the positive value is properly increased;
for the trend of fluctuation, the closer to the current time t1The greater the weight of the rate of change of (i.e., ω i ≧ ωi+1,i＝1,2,…,n-1。
Further, in the step 4, the step of calculating the predicted change rate Δ P under different change trends based on the change rate and the weight includes the following steps:
for a continuously rising trend, each rate of change is multiplied by a corresponding weight and then added to obtain a predicted rate of change, i.e.
Figure BDA0002945446120000031
For a continuously decreasing trend, each willThe individual rates of change are multiplied by corresponding weights and then added to obtain the predicted rate of change, i.e.
Figure BDA0002945446120000032
For the fluctuation trend, the positive change rate is multiplied by the corresponding weight and added to obtain the predicted increase change rate, namely
Figure BDA0002945446120000033
Multiplying the negative rate of change by the corresponding weight and adding to obtain the predicted reduced rate of change, i.e.
Figure BDA0002945446120000034
Further, in the step 5, calculating the change of the operating parameter P in the future Δ t time by using the predicted change rates of different change trends, the method includes the following steps:
for the continuous rising trend, let Pmax be P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t time1,Pmax]The rising curve of the operation parameter in the future delta t time can be generated according to frequency access in the range;
for a continuously rising trend, let Pmin＝P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t timemin,P1]The variation can be carried out, and a descending curve of the operation parameter in the future delta t time can be generated in the range according to the frequency access;
for the tendency of fluctuation, let Pmin＝P1+ΔPNegative pole·Δt·C1And is
Figure BDA0002945446120000041
Let Pmax＝P1+ΔPIs just·Δt·C2And is
Figure BDA0002945446120000042
I.e. the operating parameter P will be at [ P ] in the future at timemin,Pmax]Variation within which operating parameters can be taken and generated according to frequencyThe fluctuation curve in the future at time.
Preferably, the plant operating parameters P as described above include parameters that do not change abruptly, such as temperature, pressure, power, and flow rate.
In another aspect, the present invention provides a system for short-term prediction of operating parameters of a plant based on a rate of change, comprising:
a change rate calculation unit for determining the equipment operation parameter P to be predicted and calculating the change rate from the current time t1Forward respectively calculating change rate delta P of running parameter of adjacent time at intervals of time ti；
A change tendency determination unit for utilizing the change rate Δ PiJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
a weight setting unit for giving each change rate Δ tP according to the change trend of the plant operation parameter PiCorresponding weight ωi；
The change rate prediction unit is used for calculating the predicted change rate delta P under different change trends based on the change rate and the weight;
and the operation parameter prediction unit is used for calculating the change of the operation parameter P in the future delta t time by using the predicted change rates of different change trends.
Based on the system, further, the variation trend determination unit determines a variation trend of the operation parameter, wherein the variation trend includes a continuous rising trend, a continuous falling trend and a fluctuation trend.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
1. according to the method and the system for predicting the equipment operation parameters in the short time based on the change rate, the change trend of the operation parameters is obtained according to the change rate values by calculating a plurality of change rate values of the equipment operation parameters which are close at the current moment, and then the future operation trend of the parameters is predicted in the short time based on different change trends. The algorithm is simple in structure, no matter how complex the site working conditions are and how the production elements are mutually influenced, the change rate can always reflect the change trend of the parameters at the next moment, and particularly, the change rate can be used for predicting the operation trend of the operation parameters which are not easy to change suddenly, such as temperature, pressure, power and the like, so that a good effect can be achieved. In addition, compared with the existing neural network and data iteration method, the method has the advantages that the change rate of a single parameter is calculated without occupying a large amount of server resources, and the calculation frequency can be increased, so that the accuracy of parameter prediction is ensured. Meanwhile, the method does not need a large number of historical data samples and pre-training models, is not influenced by equipment modification in accuracy and is easy to deploy.
2. According to the method and the system for predicting the equipment operation parameters in the short time based on the change rate, provided by the embodiment of the application, the continuous rising trend or the continuous falling trend of the parameters can be early warned by judging the change rate trend of the operation parameters. In addition, the short-time maximum value and the short-time minimum value of the operation parameters are calculated by utilizing the predicted change rate, and whether the parameters exceed the limit in a short time or not can be alarmed. The system can assist the equipment management personnel to predict the variation trend of the equipment parameters in time, and make treatment measures in advance according to the plan to ensure the good operation of each equipment.
3. According to the method and the system for predicting the equipment operation parameters in the short time based on the change rate, different weights are given to a plurality of change rates of the equipment operation parameters close to each other at the current moment, and personalized correction can be carried out on prediction of different operation parameters, so that accurate prediction of different parameters is achieved, and a more accurate effect is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flow chart of a method for short-term prediction of an operation parameter of a device based on a change rate according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of the rate of change calculation in the method of FIG. 1;
FIG. 3 is a schematic diagram of the short-term prediction of the main steam temperature variation at startup of the boiler in the embodiment 1;
FIG. 4 is a schematic diagram of the short-term prediction of main steam temperature variation during boiler shutdown in embodiment 2;
FIG. 5 is a schematic diagram of the short-term prediction of the active power change of the steam turbine in normal operation of the boiler in embodiment 3;
fig. 6 is a schematic structural diagram of a system for short-term prediction of an apparatus operating parameter based on a change rate according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and 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 invention.
Fig. 1 shows a schematic flow chart of a short-term prediction method for an equipment operation parameter based on a change rate according to an embodiment of the present invention.
Referring to fig. 1, the method is implemented as follows:
s1, determining the equipment operation parameter P to be predicted, and setting the current time as t1From t1The change rate of the running parameters of the adjacent time is calculated by respectively taking the values of the interval time t forward from the moment, namely:
Figure BDA0002945446120000071
to obtain delta P1、ΔP2、…、ΔPnAs shown in fig. 2;
in this step, the device operation parameters P to be predicted are parameters that are not abrupt in temperature, pressure, power, flow rate, etc., as far as possible, and the field operation signals represented by "0" and "1" cannot be predicted. In addition, when the variation rate of the operating parameter is calculated, the operation trend prediction of the parameter is influenced by taking the values at different time intervals t, so that the reasonable variation rate calculation time interval t is set according to the taking frequency of the field system.
S2, utilization rate of change DeltaP1、ΔP2、…、ΔPnJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
specifically, in this step, the utilization rate of change Δ P1、ΔP2、…、ΔPnThe positive and negative sequence of each value judges the variation trend of the operation parameter, and the judgment rule is as follows:
when Δ P1、ΔP2、…、ΔPnThe number of continuous positive values in each value is a set ratio m/n of the total number and above, wherein m is a positive integer, m is less than or equal to n, and Δ P1、ΔP2、…、ΔPnWhen the values are all not negative values, the continuous rising trend is realized;
when Δ P1、ΔP2、…、ΔPnThe number of continuous negative values in each value is a set ratio of total number m/n and above, where m is a positive integer, m is not more than n, and Δ P1、ΔP2、…、ΔPnWhen all the values are not positive values, the trend is continuously descending;
when Δ P1、ΔP2、…、ΔPnWhen each value does not satisfy the continuous rising trend and the continuous falling trend, the value is a fluctuation trend.
It should be noted that the setting ratio m/n in this step should be set according to the field situation, subject to the number of calculated change rates and the actual operation rule of the field data.
S3, assigning each change rate delta P according to the change trend of the equipment operation parameters PiCorresponding weight ωiCorrecting the predicted trend of the operation parameters by the weight;
specifically, in this step, each change rate Δ P is assigned according to the variation trend of the plant operating parameter PiCorresponding weight ωiThe weighting rule is as follows:
for a continuously rising trend, the closer to the current time t1The larger the weight of the change rate of the negative value is, and meanwhile, in order to balance the influence of the change rate of the negative value on the operation trend of the parameter, the weight of the change rate of the negative value is properly increased;
for a continuously decreasing trend, the closer to the current time t1The larger the weight of the change rate of the positive value is, and meanwhile, in order to balance the influence of the change rate of the positive value on the operation trend of the parameter, the weight of the change rate of the positive value is properly increased;
for the trend of fluctuation, the closer to the current time t1The greater the weight of the rate of change of (c), i.e. ωi≥ωi+1,i＝1,2,…,n-1。
It should be noted that, in this step, rules given to different change rate weights according to different parameter change trends are also affected by the actual operation rules of the field data, and the specific assignment should be determined according to the field situation.
S4, calculating the predicted change rate delta P under different change trends based on the change rate and the weight;
specifically, in this step, the calculating the predicted change rate Δ P under different change trends based on the change rate and the weight includes the following steps:
for a continuously rising trend, each rate of change is multiplied by a corresponding weight and then added to obtain a predicted rate of change, i.e.
Figure BDA0002945446120000081
For a continuously decreasing trend, each rate of change is multiplied by a corresponding weight and then summed to give a predicted rate of change, i.e. the rate of change is calculated
Figure BDA0002945446120000082
For the fluctuation trend, the positive change rate is multiplied by the corresponding weight and added to obtain the predicted increase change rate, namely
Figure BDA0002945446120000083
Multiplying the negative rate of change by the corresponding weight and adding to obtain the predicted reduced rate of change, i.e.
Figure BDA0002945446120000084
And S5, calculating the change of the operation parameter P in the future delta t time by using the predicted change rate of different change trends.
Specifically, in this step, the calculating the change of the operating parameter P in the future Δ t time by using the predicted change rates of different change trends includes the following steps:
for a continuously rising trend, let Pmax＝P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t time1,Pmax]Changing and generating a rising curve of the operation parameter in the future delta t time according to frequency access;
for a continuously rising trend, let Pmin＝P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t timemin,P1]Changing and generating a descending curve of the operation parameter in the future delta t time according to frequency access;
for the tendency of fluctuation, let Pmin＝P1+ΔPNegative pole·Δt·C1And is
Figure BDA0002945446120000091
Let Pmax＝P1+ΔPIs just·Δt·C2And is
Figure BDA0002945446120000092
I.e. the operating parameter P will be at [ P ] in the future at timemin,Pmax]And taking the value according to the frequency and generating a fluctuation curve of the operation parameter in the future delta t time.
The application process of the above embodiment method is described in more detail below with reference to specific implementation cases in enterprises. The implementation case respectively takes the main steam temperature when the boiler starts and stops and the active power short-time change trend of the steam turbine in normal operation as examples, and the process and the accuracy of the prediction method are explained.
Example 1 short-term prediction of temperature change of main steam at startup of boiler
Make the scene every 1 secondTaking data once, wherein the temperature T of main steam is at a certain moment T in the starting process1Forward 11 values are taken, namely: 313.78 deg.C, 313.77 deg.C, 313.76 deg.C, 313.75 deg.C, 313.74 deg.C, 313.73 deg.C, 313.72 deg.C, 313.71 deg.C, 313.70 deg.C, 313.69 deg.C, 313.68 deg.C;
obtaining the change rate delta T of the temperature of the main steam according to a calculation formula of the change rate1～ΔT10Respectively at 0.01 ℃/s, the positive and negative sequence of the change rate satisfies the judgment of the continuous rising trend of the parameters;
setting DeltaT1～ΔT10Corresponding weight ω1～ω10Respectively 0.2, 0.18, 0.12, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, the rate of change Δ T of the rising tendency of the main steam temperature T is 0.01 ℃/s;
suppose that the operator needs to predict the main steam temperature in the future t1Trend of 3 minutes after time, Tmax＝313.78+0.01*180＝315.58℃；
And then, sequentially and randomly increasing values from the range of [313.78,315.58] to generate a prediction curve, and simultaneously performing comparison verification by using actual operation data on site, wherein as shown in fig. 3, the prediction curve is basically consistent with the actual curve, that is, the accuracy of the prediction result is high.
Example 2 short-term prediction of temperature change of main steam during shutdown of boiler
The data is obtained every 1 second on site, and the temperature T of the main steam is at a certain moment T in the starting process1Forward 11 values are taken, namely: 427.52 deg.C, 427.54 deg.C, 427.55 deg.C, 427.56 deg.C, 427.57 deg.C, 427.58 deg.C, 427.60 deg.C, 427.61 deg.C, 427.62 deg.C, 427.63 deg.C, 427.64 deg.C;
obtaining the change rate delta T of the temperature of the main steam according to a calculation formula of the change rate1～ΔT10Respectively-0.02 ℃/s, -0.01 ℃/s, -0.02 ℃/s, -0.01 ℃/s and-0.01 ℃/s, the positive and negative sequence of the change rate satisfies the continuous decrease of the parameters and tends to declineJudging the potential;
setting DeltaT1～ΔT10Corresponding weight ω1～ω10Respectively 0.2, 0.18, 0.12, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, the rate of change Δ T of the downward trend of the main steam temperature T is-0.0128 ℃/s;
suppose that the operator needs to predict the main steam temperature in the future t1Trend of 3 minutes after time, Tmin＝427.52+(-0.0128)*180＝425.22℃；
And then, sequentially and randomly reducing the value from the range of [425.22,427.52] to generate a prediction curve, and simultaneously performing comparison verification by using the actual operation data on site, wherein as shown in fig. 4, the prediction curve is basically consistent with the actual curve, that is, the accuracy of the prediction result is higher.
Embodiment 3 short-term prediction of active power change of steam turbine during normal operation of boiler
The data is obtained every 1 second on site, and the active power of the steam turbine is at a certain moment t1Forward 11 values are taken, namely: 404.96MW, 405.01MW, 405.07MW, 405.12MW, 405.18MW, 404.73MW, 404.61MW, 404.67MW, 404.72MW, 404.78MW, 404.83 MW;
obtaining the change rate delta tP of the active power of the steam turbine according to the calculation formula of the change rate1～ΔtP10Respectively-0.05 MW/s, -0.06MW/s, 0.45MW/s, 0.12MW/s, -0.06MW/s, -0.05MW/s, and the positive and negative sequence of the change rate meets the judgment of the parameter fluctuation trend;
setting Δ P1～ΔP10Corresponding weight ω1～ω100.15, 0.12, 0.10, 0.08, 0.05, respectively, the rate of change Δ P of the trend of fluctuation of the active power P of the turbineIs justIs 0.057MW/s, Δ PNegative poleIs-0.044 MW/s;
suppose that an operator needs to predict the active power of the turbine in the future t1A trend of 3 minutes after the moment, then
Figure BDA0002945446120000111
Figure BDA0002945446120000112
And then, randomly taking values in sequence from the range of [403.64,406.67] to generate a prediction curve, and simultaneously performing comparison verification by using actual operation data on site, wherein as shown in fig. 5, the prediction curve is basically consistent with the actual curve, that is, the accuracy of the prediction result is high.
Based on the same inventive concept, an embodiment of the present application further provides a system for short-term prediction of an apparatus operating parameter based on a change rate, as shown in fig. 6, where the system includes:
a change rate calculation unit 1 for determining the equipment operation parameter P to be predicted and calculating the change rate from the current time t1Forward respectively calculating change rate delta P of running parameter of adjacent time at intervals of time ti；
A change tendency determination unit 2 for utilizing the change rate Δ PiJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
a weight setting unit 3 for giving each change rate Δ P according to the change trend of the plant operation parameter PiCorresponding weight ωi；
A change rate prediction unit 4 for calculating a predicted change rate Δ P under different change trends based on the change rate and the weight;
and an operating parameter prediction unit 5 for calculating the change of the operating parameter P in the future at time using the predicted change rates of the different change trends.
Based on the above system, further, the variation trend determination unit 2 determines the variation trend of the operation parameter, where the variation trend includes a continuous upward trend, a continuous downward trend, and a fluctuation trend.
For the content not described in detail in the system for short-term prediction of device operation parameters based on a change rate provided in the embodiment of the present application, reference may be made to the method for short-term prediction of device operation parameters based on a change rate provided in the above embodiment, and details are not described here again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and it will be apparent to those skilled in the art that any modification, improvement and equivalent substitution made without departing from the principle of the present invention are included in the protection scope of the present invention.

Claims (8)

1. A method for short-term prediction of plant operating parameters based on rate of change, the method comprising the steps of:
determining the equipment operation parameter P to be predicted, and setting the current time as t1From t1The change rate of the running parameters of the adjacent time is calculated by respectively taking the values of the interval time t forward from the moment, namely:
Figure FDA0002945446110000011
using rate of change Δ P1、ΔP2、…、ΔPnJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
assigning each rate of change Δ P according to the trend of the variation of the plant operating parameter PiCorresponding weight ωi；
Calculating the predicted change rate delta P under different change trends based on the change rate and the weight;
the predicted rate of change of the different trends is used to calculate the change in the operating parameter P over the future at time.
2. A method for short-term prediction of rate-of-change-based plant operating parameters according to claim 1, characterized in that the utilization rate of change Δ Ρ1、ΔP2、…、ΔPnThe positive and negative sequence of each value judges the variation trend of the operation parameters, and the judgment rule is as follows:
when Δ P1、ΔP2、…、ΔPnThe number of continuous positive values in each value is a set ratio m/n of the total number and above, wherein m is a positive integer, m is less than or equal to n, and Δ P1、ΔP2、…、ΔPmWhen the values are all not negative values, the continuous rising trend is realized;
when Δ P1、ΔP2、…、ΔPnThe number of continuous negative values in each value is a set ratio of total number m/n and above, where m is a positive integer, m is not more than n, and Δ P1、ΔP2、…、ΔPmWhen all the values are not positive values, the trend is continuously descending;
when Δ P1、ΔP2、…、ΔPnWhen each value does not satisfy the continuous rising trend and the continuous falling trend, the value is a fluctuation trend.
3. A method for short-term prediction of rate-of-change-based plant operating parameters according to claim 2, characterized in that each rate of change Δ Ρ is assigned according to the trend of change of the plant operating parameters PiCorresponding weight ωiThe weighting rule is as follows:
for a continuously rising trend, the closer to the current time t1The larger the weight of the change rate of (3), the more the weight of the change rate of a negative value is increased;
for a continuously decreasing trend, the closer to the current time t1The larger the weight of the change rate of (2), the more the weight of the change rate of the positive value is increased;
for the trend of fluctuation, the closer to the current time t1The greater the weight of the rate of change of (c), i.e. ωi≥ωi+1,i＝1,2,…,n-1。
4. A method for short-term prediction of rate-of-change-based plant operating parameters according to claim 3, wherein the calculating of the predicted rate of change Δ Ρ for different trends based on rate of change and weight comprises the steps of:
for a continuously rising trend, each rate of change is multiplied by a corresponding weight and then added to obtain a predicted rate of change, i.e.
Figure FDA0002945446110000021
For a continuously decreasing trend, each rate of change is multiplied by a corresponding weight and then summed to give a predicted rate of change, i.e. the rate of change is calculated
Figure FDA0002945446110000022
For the fluctuation trend, the positive change rate is multiplied by the corresponding weight and added to obtain the predicted increase change rate, namely
Figure FDA0002945446110000023
Multiplying the negative rate of change by the corresponding weight and adding to obtain the predicted reduced rate of change, i.e.
Figure FDA0002945446110000024
5. A method for short-term prediction of plant operating parameters based on rate of change according to claim 4, characterized in that the calculation of the change of the operating parameter P in the future at time using the predicted rate of change of different trend of change comprises the following steps:
for a continuously rising trend, let Pmax＝P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t time1,Pmax]Changing and generating a rising curve of the operation parameter in the future delta t time according to frequency access;
for a continuously rising trend, let Pmin＝P1+ Δ P Δ t, i.e. the operating parameter P will be [ P ] in the future Δ t timemin,P1]Changing and generating a descending curve of the operation parameter in the future delta t time according to frequency access;
for the tendency of fluctuation, let Pmin＝P1+ΔPNegative pole·Δt·C1And is
Figure FDA0002945446110000031
Let Pmax＝P1+ΔPIs just·Δt·C2And is
Figure FDA0002945446110000032
I.e. the operating parameter P will be at [ P ] in the future at timemin,Pmax]And taking the value according to the frequency and generating a fluctuation curve of the operation parameter in the future delta t time.
6. The method of any one of claims 1 to 5, wherein the plant operating parameters P include temperature, pressure, power and flow rate.
7. A system for short-term prediction of plant operating parameters based on rate of change, the system comprising:
a change rate calculation unit for determining the equipment operation parameter P to be predicted and calculating the change rate from the current time t1Forward respectively calculating change rate delta P of running parameter of adjacent time at intervals of time ti；
A change tendency determination unit for utilizing the change rate Δ PiJudging the variation trend of the operation parameters according to the positive and negative sequence of each value;
a weight setting unit for giving each change rate Δ P according to the change trend of the plant operation parameter PiCorresponding weight ωi；
The change rate prediction unit is used for calculating the predicted change rate delta P under different change trends based on the change rate and the weight;
and the operation parameter prediction unit is used for calculating the change of the operation parameter P in the future delta t time by using the predicted change rates of different change trends.
8. The system of claim 7, wherein the trend determining unit determines a trend of the operating parameter, the trend including a continuous upward trend, a continuous downward trend, and a fluctuation trend.
CN202110195530.6A 2021-02-20 2021-02-20 Equipment operation parameter short-time prediction method and system based on change rate Pending CN112990552A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110195530.6A CN112990552A (en) 2021-02-20 2021-02-20 Equipment operation parameter short-time prediction method and system based on change rate

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110195530.6A CN112990552A (en) 2021-02-20 2021-02-20 Equipment operation parameter short-time prediction method and system based on change rate

Publications (1)

Publication Number Publication Date
CN112990552A true CN112990552A (en) 2021-06-18

Family

ID=76394170

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110195530.6A Pending CN112990552A (en) 2021-02-20 2021-02-20 Equipment operation parameter short-time prediction method and system based on change rate

Country Status (1)

Country Link
CN (1) CN112990552A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023000530A1 (en) * 2021-07-19 2023-01-26 苏州百孝医疗科技有限公司 Method for improving accuracy of real-time concentration change trend during continuous monitoring of analyte concentration in animal body
CN116186109A (en) * 2022-12-26 2023-05-30 中国长江电力股份有限公司 Method for inquiring time sequence data with value changed by information system
CN116388111A (en) * 2023-04-18 2023-07-04 杭州欣美成套电器制造有限公司 In-situ measurement and control protection integrated device of electric micro-grid
CN117536691A (en) * 2024-01-09 2024-02-09 枣庄矿业集团新安煤业有限公司 Fully-mechanized coal mining face equipment parameter monitoring method and system

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020087258A1 (en) * 2000-12-29 2002-07-04 Johnson Daniel P. Prognostics monitor for systems that are subject to failure
US6442511B1 (en) * 1999-09-03 2002-08-27 Caterpillar Inc. Method and apparatus for determining the severity of a trend toward an impending machine failure and responding to the same
CN103364669A (en) * 2013-07-31 2013-10-23 广州供电局有限公司 Online detecting method and system for GIS (Gas Insulated Switchgear) device operating state
CN104848885A (en) * 2015-06-04 2015-08-19 北京金控自动化技术有限公司 Method for predicting time of future failure of equipment
CN105139091A (en) * 2015-08-26 2015-12-09 国家电网公司 Capacitor capacitance value and change trend forecasting method based on time series method
CN106059661A (en) * 2015-12-25 2016-10-26 国家电网公司 Time sequence analysis based optical transmission network trend prediction method
CN106600076A (en) * 2017-01-10 2017-04-26 西安交通大学 Regenerative thermal oxidizer (RTO) waste gas treatment device monitoring data analysis and early warning method
CN106600064A (en) * 2016-12-16 2017-04-26 东软集团股份有限公司 Data prediction method and device
CN106908674A (en) * 2017-02-17 2017-06-30 国网上海市电力公司 A kind of Transformer condition evaluation based on the prediction of multimode amount
CN107357764A (en) * 2017-06-23 2017-11-17 联想(北京)有限公司 Data analysing method, electronic equipment and computer-readable storage medium
CN108089078A (en) * 2017-12-07 2018-05-29 北京能源集团有限责任公司 Equipment deteriorates method for early warning and system
CN110705780A (en) * 2019-09-27 2020-01-17 科大国创软件股份有限公司 IT performance index prediction method based on intelligent algorithm
CN110763929A (en) * 2019-08-08 2020-02-07 浙江大学 Intelligent monitoring and early warning system and method for convertor station equipment
CN111409572A (en) * 2020-03-30 2020-07-14 北京经纬恒润科技有限公司 Control method and device for vehicle body closing system
CN111416353A (en) * 2020-05-11 2020-07-14 华北电力大学 Standby configuration method considering wind power continuous time period fluctuation trend

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6442511B1 (en) * 1999-09-03 2002-08-27 Caterpillar Inc. Method and apparatus for determining the severity of a trend toward an impending machine failure and responding to the same
US20020087258A1 (en) * 2000-12-29 2002-07-04 Johnson Daniel P. Prognostics monitor for systems that are subject to failure
CN103364669A (en) * 2013-07-31 2013-10-23 广州供电局有限公司 Online detecting method and system for GIS (Gas Insulated Switchgear) device operating state
CN104848885A (en) * 2015-06-04 2015-08-19 北京金控自动化技术有限公司 Method for predicting time of future failure of equipment
CN105139091A (en) * 2015-08-26 2015-12-09 国家电网公司 Capacitor capacitance value and change trend forecasting method based on time series method
CN106059661A (en) * 2015-12-25 2016-10-26 国家电网公司 Time sequence analysis based optical transmission network trend prediction method
CN106600064A (en) * 2016-12-16 2017-04-26 东软集团股份有限公司 Data prediction method and device
CN106600076A (en) * 2017-01-10 2017-04-26 西安交通大学 Regenerative thermal oxidizer (RTO) waste gas treatment device monitoring data analysis and early warning method
CN106908674A (en) * 2017-02-17 2017-06-30 国网上海市电力公司 A kind of Transformer condition evaluation based on the prediction of multimode amount
CN107357764A (en) * 2017-06-23 2017-11-17 联想(北京)有限公司 Data analysing method, electronic equipment and computer-readable storage medium
CN108089078A (en) * 2017-12-07 2018-05-29 北京能源集团有限责任公司 Equipment deteriorates method for early warning and system
CN110763929A (en) * 2019-08-08 2020-02-07 浙江大学 Intelligent monitoring and early warning system and method for convertor station equipment
CN110705780A (en) * 2019-09-27 2020-01-17 科大国创软件股份有限公司 IT performance index prediction method based on intelligent algorithm
CN111409572A (en) * 2020-03-30 2020-07-14 北京经纬恒润科技有限公司 Control method and device for vehicle body closing system
CN111416353A (en) * 2020-05-11 2020-07-14 华北电力大学 Standby configuration method considering wind power continuous time period fluctuation trend

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
刘吉臻;高萌;吕游;杨婷婷;: "过程运行数据的稳态检测方法综述", 仪器仪表学报, no. 08, 15 August 2013 (2013-08-15) *
孙博;康锐;张叔农;: "基于特征参数趋势进化的故障诊断和预测方法", 航空学报, no. 02, 15 March 2008 (2008-03-15) *
徐铬;李正天;程建;万松;曹长冲;: "油浸绝缘电气主设备状态量发展趋势预测模型研究", 水电站机电技术, no. 03, 15 March 2018 (2018-03-15) *
王颖华;张鑫;: "西宁市降水量特征及变化趋势分析", 水土保持研究, no. 05, 15 October 2011 (2011-10-15) *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023000530A1 (en) * 2021-07-19 2023-01-26 苏州百孝医疗科技有限公司 Method for improving accuracy of real-time concentration change trend during continuous monitoring of analyte concentration in animal body
CN116186109A (en) * 2022-12-26 2023-05-30 中国长江电力股份有限公司 Method for inquiring time sequence data with value changed by information system
CN116186109B (en) * 2022-12-26 2024-01-05 中国长江电力股份有限公司 Method for inquiring time sequence data with value changed by information system
CN116388111A (en) * 2023-04-18 2023-07-04 杭州欣美成套电器制造有限公司 In-situ measurement and control protection integrated device of electric micro-grid
CN116388111B (en) * 2023-04-18 2024-02-20 杭州欣美成套电器制造有限公司 In-situ measurement and control protection integrated device of electric micro-grid
CN117536691A (en) * 2024-01-09 2024-02-09 枣庄矿业集团新安煤业有限公司 Fully-mechanized coal mining face equipment parameter monitoring method and system
CN117536691B (en) * 2024-01-09 2024-04-05 枣庄矿业集团新安煤业有限公司 Fully-mechanized coal mining face equipment parameter monitoring method and system

Similar Documents

Publication Publication Date Title
CN112990552A (en) Equipment operation parameter short-time prediction method and system based on change rate
Ma et al. Data-fusion prognostics of proton exchange membrane fuel cell degradation
US20160223600A1 (en) Power generation performance evaluation method and apparatus for power generator set
TWI797418B (en) Method and system for correcting component failures in ion implant semiconductor manufacturing tool
US8577822B2 (en) Data-driven approach to modeling sensors wherein optimal time delays are determined for a first set of predictors and stored as a second set of predictors
RU2626780C1 (en) Method and system of remote monitoring energy installations
US8405239B2 (en) Wind-turbine-dynamic-characteristics monitoring apparatus and method therefor
US20210232104A1 (en) Method and system for identifying and forecasting the development of faults in equipment
CN112884199B (en) Hydropower station equipment fault prediction method, hydropower station equipment fault prediction device, computer equipment and storage medium
US20170316325A1 (en) Device for probability forecasts, energy transmission and/or energy distribution network
KR20150054974A (en) Method for the computer-assisted monitoring of the operation of a technical system, particularly of an electrical energy-generating installation
Amayri et al. Condition based maintenance of wind turbine systems considering different turbine types
JP6086875B2 (en) Power generation amount prediction device and power generation amount prediction method
CN116960941A (en) Power climbing event prediction method, device, equipment and storage medium
CN111049159B (en) Power system transient stability prevention control method embedded in deep belief network
Hailu et al. Techniques of power system static security assessment and improvement: A literature survey
Khadidos et al. Integrating industrial appliances for security enhancement in data point using SCADA networks with learning algorithm
US20230147470A1 (en) Failure prediction system
Elnour et al. A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems
Kusiak et al. Virtual wind speed sensor for wind turbines
CN110454322B (en) Water turbine speed regulation control method, device and system based on multivariable dynamic matrix
Mohamed et al. Modelling of inter‐area angular dynamics and proactive defence strategy in deregulated power grids with large‐scale integration of renewable energy sources
Al-Dahidi et al. A novel fault detection system taking into account uncertainties in the reconstructed signals
Wei et al. A lite cellular generalized neuron network for frequency prediction of synchronous generators in a multimachine power system
Deng et al. Research on fault diagnosis of flexible material R2R manufacturing system based on quality control chart and SoV

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination