CN111594478B - Magnetic suspension centrifugal blower anti-surge control method based on big data - Google Patents
Magnetic suspension centrifugal blower anti-surge control method based on big data Download PDFInfo
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- CN111594478B CN111594478B CN202010499799.9A CN202010499799A CN111594478B CN 111594478 B CN111594478 B CN 111594478B CN 202010499799 A CN202010499799 A CN 202010499799A CN 111594478 B CN111594478 B CN 111594478B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/02—Surge control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/01—Purpose of the control system
- F05D2270/10—Purpose of the control system to cope with, or avoid, compressor flow instabilities
- F05D2270/101—Compressor surge or stall
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/301—Pressure
- F05D2270/3013—Outlet pressure
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/303—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/304—Spool rotational speed
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/313—Air temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
Abstract
The invention provides a magnetic suspension centrifugal blower anti-surge control method based on big data, which comprises the steps of constructing a data model by a deletion principle-non-inclusion principle of a large amount of actual operation data, then forming multidimensional data by data analysis, and converging surge points represented by the multidimensional data into a surge area. According to the anti-surge control method of the magnetic suspension centrifugal blower based on the big data, a complex mathematical model does not need to be established, multidimensional data are continuously collected, recorded and analyzed in-plant test and field operation of a large number of magnetic suspension blowers of the same type, and a blower surge area with the data as a core is drawn, so that anti-surge control can be realized; in the initial stage of the anti-surge model construction, a rough surge area can be drawn by a small amount of three-dimensional data, and the surge area is more and more accurate along with the increase of the data volume and the dimension in the later stage; the anti-surge model is a growing and trainable anti-surge model.
Description
Technical Field
The invention belongs to the field of magnetic suspension centrifugal fans, and particularly relates to a magnetic suspension centrifugal fan anti-surge control method based on big data.
Background
As one of the centrifugal blower families, surge is an inherent characteristic of the magnetic suspension centrifugal blower, and a general anti-surge method is to collect limited data of pressure, temperature, power and the like of the magnetic suspension centrifugal blower, calculate an anti-surge control area by applying some programmable control algorithms (such as a variable limit method, a fuzzy control method and the like), and realize the anti-surge control of the magnetic suspension centrifugal blower. The control algorithms need to establish a determined mathematical model, such as a flow-pressure binary linear anti-surge model, and a defined working area needs to be far enough away from a surge line to ensure reliable operation of magnetic suspension, so that the working range of the magnetic suspension blower is reduced, and the control algorithm is a relatively extensive control strategy; the mathematical model of the complex multivariate model or the more advanced control algorithm is difficult to establish, and generally needs a professional team to obtain the model through a large amount of experiments and calculations, and the cost is also expensive.
Disclosure of Invention
In view of the above, the present invention is directed to a magnetic suspension centrifugal blower anti-surge control method based on big data, so as to provide a blower anti-surge control method that can be grown and trained without building a complex mathematical model.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a magnetic suspension centrifugal blower anti-surge control method based on big data comprises the steps of constructing a data model by a large amount of actual operation data through a deletion principle-mutual exclusion principle, then forming multidimensional data through data analysis, and merging surge points represented by the multidimensional data into a surge area.
Further, the actual operation data comprises parameters of the magnetic suspension blower, which are affected by the operation of the magnetic suspension blower, sensitive parameters of the working state of the magnetic suspension blower and insensitive parameters of the working state of the magnetic suspension blower.
Further, the parameters of the magnetic suspension blower influenced by operation include a design point of the blower, an ambient temperature, air humidity, local atmospheric pressure, a working rotating speed, a pipe network characteristic, outlet pressure, water level depth, operation power, output current and an axis track.
Furthermore, the sensitive parameters of the working state of the magnetic suspension blower comprise the ambient temperature, the working rotating speed, the outlet pressure and the pipe network characteristics.
Furthermore, the insensitive parameters of the working state of the magnetic suspension blower comprise air humidity and an axis track.
Further, the specific method of the deletion principle-mutual exclusion principle is as follows: we construct a model using two operating parameters, fan operating speed X and outlet pressure Y, then (X-X0)2+(Y-Y0)2In the range of not more than R, othersAnd the data can not be selected for constructing a model, wherein X0 and Y0 represent independent parameters of the fan, and R represents the radius of a surge area.
Further, the specific method for analyzing and composing the multidimensional data is as follows: under specific working conditions, a large amount of operation data of the magnetic suspension blower is combined into a set, multidimensional data are formed by taking independent parameters of the fan as data dimensions, each multidimensional data represents a certain actual operation state of the fan and is represented by (X0, Y0, Z0 …), wherein X0, Y0 and Z0 all represent one of the independent parameters of the fan, R represents the radius of a surge area, and the set is represented by (X-X0)2+(Y-Y0)2+(Z-Z0)2+…≤R2The area formed by the N small multidimensional surging areas is used as the surging area of the fan, the surging area is defined, and the fan can be controlled to operate in a mode of avoiding the surging area.
Compared with the prior art, the magnetic suspension centrifugal blower anti-surge control method based on big data has the following advantages:
(1) according to the anti-surge control method of the magnetic suspension centrifugal blower based on the big data, a complex mathematical model does not need to be established, and multi-dimensional data are continuously collected, recorded and analyzed in-plant test and field operation of a large number of magnetic suspension blowers (the performance difference between individual blowers is very small) of the same type, and a blower surge area with the data as a core is drawn, so that anti-surge control can be realized; in the initial stage of the anti-surge model construction, a rough surge area can be drawn by a small amount of three-dimensional data, and the surge area is more and more accurate along with the increase of the data volume and the dimension in the later stage; the anti-surge model is a growing and trainable anti-surge model.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a diagram illustrating a big data-based anti-surge model according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The noun explains:
specific working conditions are as follows: under the condition that the environmental temperature and the pipe network characteristic parameters are not changed, the magnetic suspension blower controls the determined working state by three independent parameters of the working speed, the inlet flow and the outlet pressure.
Independent parameters: meaning that there is no correlation between any two parameters.
A magnetic suspension centrifugal blower anti-surge control method based on big data is disclosed, as shown in figure 1, and comprises the steps of constructing a data model by a deletion principle-non-inclusion principle of a large amount of actual operation data, then forming multidimensional data by data analysis, and converging surge points represented by the multidimensional data into a surge area.
The actual operation data comprises parameters influenced by the operation of the magnetic suspension blower, sensitive parameters of the working state of the magnetic suspension blower and insensitive parameters of the working state of the magnetic suspension blower.
Parameters influenced by the operation of the magnetic suspension blower comprise parameters such as a design point of the blower, ambient temperature, air humidity, local atmospheric pressure, working rotating speed, pipe network characteristics, outlet pressure, water level depth, operating power, output current, axis track and the like;
sensitive parameters of the working state of the magnetic suspension blower comprise parameters such as environment temperature, working rotating speed, outlet pressure, pipe network characteristics and the like;
insensitive parameters of the working state of the magnetic suspension blower comprise air humidity, axle center track and the like.
Deletion principle-mutually exclusive principle, for example: we use two operating parameters of the fan working speed X and the outlet pressure Y to build a model, if the fan generates surge (X0 and Y0 represent independent parameters of the fan) in actual operation (X0 and Y0), and we use the surge as data for building the model (X-X0)2+(Y-Y0)2In the range of R ≦ R, other data can not be selected for the data for constructing the model, and we use the simplest two-dimensional finite array for the principle description, as shown in FIG. 1, and FIG. 1 illustrates: the curves shown in fig. 1 or the dot matrixes represented by circles are drawn under specific working conditions; the dash-dotted line in the figure is a surge line, the solid line is a performance curve of 100%, 80%, 60% and 40% of rated rotation speed respectively, and the solid line is a pipe network characteristic curve 1 and a pipe network characteristic curve 2 respectively; the circle on the surge line (r) representsAnd a surge point, a solid line and a circle on the seventhly represent stable working points of the fan.
Under a specific working condition, for example, a large amount of operation data of the magnetic suspension blower is grouped together, a region formed by circles taking R as a radius is a surge region of the operation of the blower by taking each surge point data as a circle center (Q0, P0) (a solid circle at the intersection of a surge line (R) and a solid line (c)) and the surge line (R) in the figure 1 is smaller and smaller, and the surge region is more accurate as the surge line (R) in the figure 1. On the basis, if independent parameters such as inlet temperature, pipe network characteristics and the like are added, the area formed by converging the surge points represented by the multi-dimensional data is closer to the actual surge area of the fan.
The anti-surge method takes independent parameters of the fan as data dimensions to form multidimensional data, each multidimensional data represents a certain actual operating state of the fan and is represented by (X0, Y0, Z0 …), wherein X0, Y0 and Z0 represent one of the independent parameters of the fan and are represented by (X-X0)2+(Y-Y0)2+(Z-Z0)2+…≤R2A small multidimensional surge area is represented, and an area formed by the N small multidimensional surge areas is used as a surge area of the fan. It can be seen that the surge region of the wind turbine is closer to the true value for each additional dimension or additional multidimensional data. The surge area is defined, the fan can be controlled to avoid the operation of the surge area, if the conversion from the operation point (Q1, P1) formed by the coaction of the pipe network characteristic curve (r) and the fan performance curve (c) in the graph 1 to the operation point (Q2, P2) formed by the coaction of the pipe network characteristic curve (r) and the fan performance curve (c) is realized, the direct achievement can be realized, and the curved road is not needed.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A magnetic suspension centrifugal blower anti-surge control method based on big data is characterized in that: constructing a data model by a deletion principle-mutually exclusive principle of a large amount of actual operation data, then forming multidimensional data by data analysis, and converging surge points represented by the multidimensional data into a surge area;
the specific method of the deletion principle-mutual exclusion principle is as follows: the model is constructed by using two operation parameters of the working speed X of the fan and the outlet pressure Y, then (X-X0)2+(Y-Y0)2In the range of R ≦ R, other data can not be selected for the data for constructing the model, wherein X0 and Y0 represent independent parameters of the fan, and R represents the radius of a surge area;
the specific method for analyzing and forming the multidimensional data by the data comprises the following steps: under the condition that environmental temperature and pipe network characteristic parameters are not changed, the magnetic suspension blower is controlled by three independent parameters of working speed, inlet flow and outlet pressure to determine a working state, a large amount of operation data of the magnetic suspension blower are combined into a set, the independent parameters of the blower are taken as data dimensions to form multidimensional data, each multidimensional data represents a certain actual operation state of the blower and is represented by (X0, Y0 and Z0 …), wherein X0, Y0 and Z0 all represent one of the independent parameters of the blower, R represents the radius of a surge area, and (X-X0)2+(Y-Y0)2+(Z-Z0)2+…≤R2The area formed by the N small multidimensional surging areas is used as the surging area of the fan, the surging area is defined, and the fan can be controlled to operate in a mode of avoiding the surging area.
2. The anti-surge control method of the magnetic suspension centrifugal blower based on the big data as claimed in claim 1, characterized in that: the actual operating data comprises parameters of the magnetic suspension blower, the operation of which is influenced.
3. The anti-surge control method of the magnetic suspension centrifugal blower based on the big data as claimed in claim 2, characterized in that: parameters of the magnetic suspension blower, which are influenced by the operation, comprise a design point of the blower, the ambient temperature, the air humidity, the local atmospheric pressure, the working rotating speed, the pipe network characteristic, the outlet pressure, the water level depth, the operation power, the output current and the axle center track.
4. The anti-surge control method of the magnetic suspension centrifugal blower based on the big data as claimed in claim 3, characterized in that: the environmental temperature, the working rotating speed, the outlet pressure and the pipe network characteristics are sensitive parameters of the working state of the magnetic suspension blower.
5. The anti-surge control method of the magnetic suspension centrifugal blower based on the big data as claimed in claim 3, characterized in that: air humidity and axis track are insensitive parameters of the working state of the magnetic suspension blower.
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| CN114904665B (en) * | 2022-04-20 | 2023-06-13 | 珠海格力电器股份有限公司 | Anti-surge control method and device for magnetic suspension centrifuge and storage medium |
| CN116050030B (en) * | 2023-04-03 | 2023-07-28 | 亿昇(天津)科技有限公司 | Method, device and equipment for determining axial center position of blower rotor |
| CN117553027A (en) * | 2024-01-11 | 2024-02-13 | 山东天瑞重工有限公司 | Testing method and testing equipment for magnetic suspension blower |
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| DE19506790C2 (en) * | 1994-02-28 | 2000-11-16 | Kuehnle Kopp Kausch Ag | Process for the efficiency-optimized operation of a radial compressor |
| US6871498B1 (en) * | 2003-12-20 | 2005-03-29 | Honeywell International, Inc. | Compressor surge protector for electric assisted turbocharger |
| US7603237B2 (en) * | 2006-07-26 | 2009-10-13 | Schlumberger Technology Corporation | Method for analyzing data having shared and distinct properties |
| CN101545495B (en) * | 2008-03-26 | 2011-10-05 | 上海宝信软件股份有限公司 | Device and method for controlling anti-surging of a blast blower |
| CN106895022B (en) * | 2017-04-18 | 2018-04-06 | 沈阳鼓风机集团自动控制系统工程有限公司 | Anti-surge control method, control device and control system |
| CN107620729A (en) * | 2017-09-26 | 2018-01-23 | 亿昇(天津)科技有限公司 | A kind of magnetic suspension centrifugal blower anti-surge control method |
| CN110307144B (en) * | 2018-03-20 | 2021-05-11 | 恩尔赛思有限公司 | Method for analyzing, monitoring, optimizing and/or comparing energy efficiency in a multi-compressor system |
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