CN110083912A - The optimal waterpower permanent magnet generator optimum design method of annual electricity generating capacity - Google Patents
The optimal waterpower permanent magnet generator optimum design method of annual electricity generating capacity Download PDFInfo
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- CN110083912A CN110083912A CN201910319211.4A CN201910319211A CN110083912A CN 110083912 A CN110083912 A CN 110083912A CN 201910319211 A CN201910319211 A CN 201910319211A CN 110083912 A CN110083912 A CN 110083912A
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Abstract
The present invention provides a kind of waterpower permanent magnet generator optimum design method that annual electricity generating capacity is optimal, this method establishes the hydrological data bank in target power station, and database includes the relevant statistics such as head, flow, rainfall and water level；Then based on database has been established, the variation of generator input terminal load information caused by hydrographic data distributional difference is analyzed, matching obtains the prerun operating condition of generator；More efficiency optimization systems are finally established based on prerun operating condition, is solved using objective function of the optimization algorithm to multiple efficiency and obtains key design parameter.This all multivariable for reasonably choosing and being utilized water power field using this method is designed the parameter of electric machine, the comprehensive operation efficiency for improving hydroelectric generator in multiple water level periods can be synchronized, and calculation amount is small, particularly suitable for hydroelectric field.
Description
Technical field
The present invention relates to hydroelectric generation optimisation technique fields, forever more particularly, to a kind of waterpower that annual electricity generating capacity is optimal
Magnetic generator optimum design method.
Background technique
Generator is the critical component of hydroelectric power system energy conversion, and the height of generator efficiency directly influences water power
The generated energy of system, therefore efficiency optimization is the important goal of design of electrical motor.Motor optimized design is to utilize fast development
Computer technology be that design of electrical motor personnel fast and accurately find a kind of design of electrical motor method of best design parameter, motor
Optimal Design Method be widely used in design of electrical motor field and the hot spot of people's research.
The efficiency optimization of existing generator designs usually using the rated efficiency model of motor as objective function, with motor
It can be constrained to constraint condition, using optimization algorithm, obtain the optimal solution of objective function by constantly iterating to calculate to solve, if
It is as shown in Figure 1 to count process.Theoretically, obtained optimal solution is substituted into the specified fortune that can make motor in the design parameter of motor
Line efficiency reaches maximum value.
In water power field, since water-power plant is by many factors shadows such as regional rainfall, upper and lower basin water flows
It rings, the water level distributional difference in reservoir or river is fairly obvious among 1 year, and the difference of water level directly results in the input of generator
End load variation is very big.In this case, motor runs on less time under declared working condition.Traditional efficiency optimization is set
Meter method assumes that generator continuous service is optimized in rated condition and only to the rated efficiency of generator, defeated without considering
Enter the great influence that the variation of end load generates operational efficiency, this may cause operation effect of the generator under off rating
Rate substantially reduces, further the serious overall operation efficiency for reducing generator, the annual electricity generating capacity in reduction power station.
Existing hydroelectric generation does not carry out operating condition preanalysis to target power station with generator design method, does not carry out
Load matched, it is assumed that generator by long-term work in declared working condition, do not account in the annual period in reservoir or river water level point
Cloth changes the influence of the operating condition variation to generator, and has only carried out optimized design to the specified operational efficiency of generator,
It is difficult to adapt to the actual multi-state running environment in power station, will lead to generator in the decline of non-rated load operation efficiency, into
One step influences the annual electricity generating capacity output in power station.
Summary of the invention
In order to overcome the problems, such as this, the present invention provides a kind of optimizations of waterpower permanent magnet generator that annual electricity generating capacity is optimal to set
Meter method, this method, which can synchronize, improves comprehensive operation efficiency of the hydroelectric generator in multiple water level periods, and particularly suitable for
Hydroelectric field.
The present invention provides a kind of waterpower permanent magnet generator optimum design method that annual electricity generating capacity is optimal, this method includes such as
Lower step:
S1: establishing hydrological data bank, and the hydrological data bank includes head, flow, rainfall and water level in annual period；
S2: data analysis is carried out to the hydrological data bank, by hydrographic data discretization to distinguish hydrology period, the water
Literary period includes dry season, the period when a river is at its normal level and wet season, and establishes the input work of each hydrology period mapped permanent magnet generator
Rate P1~P3, input speed ω1~ω3, time accounting factor alpha1~α3, and then establish the parsing of hydrological variation and generator efficiency
Relationship, to obtain corresponding efficiency Model η of each hydrology period1(X)~η3(X)；
Wherein, cumulative time T shared by each hydrology period1~T3Determine corresponding time accounting factor alpha1~α3, α1
~α3Calculation formula (1) are as follows:
Subscript i is different water level period coefficients, and value is 1,2,3 respectively；
S3: establishing the analytic modell analytical model of magneto alternator, to obtain optimal set using optimization algorithm come iteration
Count variable X；The analytic modell analytical model includes multiple objective function Fi(X), constraint condition Gj(X) and the design variable X of generator,
The multiple objective function Fi(X) time accounting factor alpha is utilized in foundation1~α3With efficiency Model η1(X)~η3(X)。
In another embodiment, the hydrological data bank established in the step S1 is specifically purpose reservoir or river water
The hydrological data bank of literary information.
In another embodiment, the multiple objective function Fi(X) specifically meet following formula (2):
Fi(X)=αi*ηi(X) (2)
Wherein, subscript i corresponds to different water level period coefficients, respectively 1,2,3.
In another embodiment, the design variable X is motor axial length L1, rotor internal diameter Rr, permanent magnet thickness hm、
The wide δ of air gap, the high h of stator slots, the high h of stator yokey, stator facewidth wt, pole embrace αpOne of or it is a variety of.
In another embodiment, the constraint condition GjIt (X) include stator winding current density J1(X), stator slot is full
Rate Sf(X), air gap flux density Bg(X), stator teeth flux density Bt(X) and/or stator yoke flux density By(X) constraint function.
In another embodiment, the optimization algorithm in the step S3 is genetic algorithm.
It is an advantage of the current invention that the foundation of hydrological data bank can help design of electrical motor personnel more complete with analysis method
The operating condition distribution for proposing meter generator is understood to face, so that design scheme has more specific aim.Furthermore optimization algorithm is with multiple water
The generator operational efficiency in position period is objective function, the operational efficiency of the multi-load state of energy Synchronous fluorimetry generator.Therefore,
The present invention has the advantages that improve the annual electricity generating capacity of water-power plant.
Detailed description of the invention
Fig. 1 is a kind of design flow diagram of design of electrical motor parameter；
Fig. 2 is the load matched flow diagram of hydroelectric system of the invention；
Fig. 3 is the optimal optimization design flow diagram of annual electricity generating capacity of the invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
As shown in Fig. 2, it illustrates a kind of load matched flow diagrams of hydroelectric system, and as seen from the figure, load
Matching process includes:
1) hydrological data bank is established: establishing the hydrological data bank to purpose reservoir or River Hydrology information；Wherein, described
It is the hydrographic information progress data statistics and analysis for being directed to target power station that hydrological data bank, which is established, specially establishes target water power
The integrated database of the information such as head, flow, rainfall and the water level of reservoir or river within annual period where standing.
2) generator loading match: to established hydrological data bank carry out data analysis, according to water level, flow, head,
The data separations dry season such as rainfall, the period when a river is at its normal level, wet season, by hydrographic data discretization, (discretization meets hydroelectric generation neck
Operation characteristic of the motor by water level differentia influence in domain), and establish each hydrology period mapped power input to a machine P1
~P3, input speed ω1~ω3, time accounting factor alpha1~α3, and then the parsing for establishing hydrological variation and generator efficiency is closed
System, to obtain corresponding efficiency Model η of each hydrology period1(X)~η3(X)；
3) optimum procedure: establishing the analytic modell analytical model of magneto alternator, thus using optimal in optimum procedure
Algorithm iteration obtains design of electrical motor parameter, so as to be suitable for hydroelectric field, and improves hydroelectric generator multiple
The comprehensive operation efficiency in water level period.
In one embodiment, the hydrographic data is discrete turns to three important hydrology periods, is respectively as follows:
S1, dry season: dry season refers mainly to reservoir level HiIn lower segment (H0-H1), cumulative time T1。
Dry season can input power P to generator1, input speed ω1It has an impact, and will further influence motor electromagnetic performance
With efficiency Model η1(X).Cumulative time in dry season T1Determine time accounting factor alpha1, calculation formula is such as shown in (1).
S2, the period when a river is at its normal level: the period when a river is at its normal level refers mainly to reservoir level HiIn more gentle segment (H1-H2), the cumulative time is
T2.Dry season can input power P to generator2, input speed ω2It has an impact, and will further influence motor electromagnetic
It can be with efficiency Model η2(X).Cumulative time in dry season T2Determine time accounting factor alpha2, calculation formula is such as shown in (1).
S3, wet season: dry season refers mainly to reservoir level HiIn higher segment (H2-H3), cumulative time T3。
Dry season can input power P to generator3, input speed ω3It has an impact, and will further influence motor electromagnetic performance
With efficiency Model η3(X).Cumulative time in dry season T3Determine time accounting factor alpha3, calculation formula is such as shown in (1).
Wherein, subscript i is different water level period coefficients, successively represents dry season, the period when a river is at its normal level and wet season, respectively 1,2,
3。
Hydrographic information can have an important influence on motor performance, and the foundation of hydrological data bank is matched with generator loading
The more efficiency optimized designs of generator to next step are generated into key effect.
Fig. 3 shows a kind of optimization design flow diagram that annual electricity generating capacity is optimal, and as figure shows, annual electricity generating capacity is optimal
Optimization design is multi-objective optimization design of power, and design method includes:
Analytic modell analytical model and selected optimal algorithm based on load matched model foundation magneto alternator (such as are lost
Propagation algorithm) multiple-objection optimization is carried out to analytic modell analytical model, thus obtain the optimal solution of model, i.e., optimal magneto alternator
Design variable.The analytic modell analytical model includes multiple objective function Fi(X), constraint condition Gj(X), design variable X.
In a case study on implementation, intelligent algorithm selects genetic algorithm, and generator uses durface mounted permanent magnet synchronous motor knot
Structure, analytic modell analytical model include design variable X, constraint condition Gj(X), multiple objective function Fi(X) with time accounting factor alphai, in which:
S1, design variable X: electric machine structure parameter can be had an important influence on to electric efficiency by referring to, the design that case is selected becomes
Amount includes: motor axial length L1, rotor internal diameter Rr, permanent magnet thickness hm, the wide δ of air gap, the high h of stator slots, the high h of stator yokey, stator
Facewidth wt, pole embrace αp。
S2, constraint condition Gj(X): be the performance constraints such as electricity, magnetic, heat, the power of motor be presented as in analytic modell analytical model because become
Flow function.Constraint condition can make the calculated value of algorithm be constantly in reasonable range, since the application belongs to water power field, therefore originally
The constraint condition considered in case specifically includes that stator winding current density J1(X), stator copper factor Sf(X), air gap flux density Bg
(X), stator teeth flux density Bt(X), stator yoke flux density By(X) constraint function.
S3, multiple objective function Fi(X) with time accounting factor alphai, optimization program is multiple-objection optimization, more mesh in case
Scalar functions Fi(X) time accounting factor alpha is utilized in foundation1~α3With efficiency Model η1(X)~η3(X).Specifically, such as formula
(2) shown in, multiple objective function Fi(X) be respectively power station be in dry season, the period when a river is at its normal level, the wet season efficiency Model η i (X) with
Corresponding time accounting factor alphaiProduct, time accounting factor alphaiFor the time accounting coefficient in dry season, the period when a river is at its normal level, wet season
αi, the time accounting coefficient the big, and the weight optimized is higher.
Wherein multiple objective function Fi(X) are as follows:
Fi(X)=αi*ηi(X) (2)
Wherein, subscript i corresponds to different water level period coefficients, respectively 1,2,3.
Finally, genetic algorithm is to the more efficiency optimization bodies set up by design variable, constraint condition and objective function
System is iterated calculating and solves.When meeting the decision condition that algorithm terminates, terminates optimization program and export optimal solution.
Method of the invention initially sets up the hydrological data bank in target power station, and database includes head, flow, rainfall
And the relevant statistics such as water level；Then based on database has been established, to generator caused by hydrographic data distributional difference
The variation of input terminal load information is analyzed, and matching obtains the prerun operating condition of generator；Finally established based on prerun operating condition
More efficiency optimization systems are solved using objective function of the optimization algorithm to multiple efficiency and obtain key design parameter.
The all multivariables for reasonably choosing and being utilized hydrology field using this method are designed the parameter of electric machine, can effectively improve
The annual electricity generating capacity in power station exports, and calculation amount is small.
Technical solution between each embodiment of the present invention can be combined with each other, but must be with ordinary skill
Based on personnel can be realized, this technical side will be understood that when the combination of technical solution appearance is conflicting or cannot achieve
The combination of case is not present, also not the present invention claims protection scope within.
Finally, method of the invention is only preferable embodiment, it is not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent replacement, improvement and so on should be included in protection of the invention
Within the scope of.
Claims (6)
1. a kind of waterpower permanent magnet generator optimum design method that annual electricity generating capacity is optimal, which is characterized in that this method includes as follows
Step:
S1: establishing hydrological data bank, and the hydrological data bank includes head, flow, rainfall and water level in annual period；
S2: carrying out data analysis to the hydrological data bank, by hydrographic data discretization to distinguish hydrology period, when the hydrology
Phase includes dry season, the period when a river is at its normal level and wet season, and establishes the input power P of each hydrology period mapped permanent magnet generator1
~P3, input speed ω1~ω3, time accounting factor alpha1~α3, and then the parsing for establishing hydrological variation and generator efficiency is closed
System, to obtain corresponding efficiency Model η of each hydrology period1(X)~η3(X)；
Wherein, cumulative time T shared by each hydrology period1~T3Determine corresponding time accounting factor alpha1~α3, α1~α3
Calculation formula (1) are as follows:
Subscript i is different water level period coefficients, and value is 1,2,3 respectively.
S3: establishing the analytic modell analytical model of magneto alternator, becomes to obtain optimal design using optimization algorithm come iteration
Measure X；The analytic modell analytical model includes multiple objective function Fi(X), constraint condition Gj(X) and the design variable X of generator, described
Objective function Fi(X) time accounting factor alpha is utilized in foundation1~α3With efficiency Model η1(X)~η3(X)。
2. optimum design method according to claim 1, which is characterized in that the hydrological data bank established in the step S1
Specifically purpose reservoir or the hydrological data bank of River Hydrology information.
3. optimum design method according to claim 1, which is characterized in that the multiple objective function Fi(X) under specific satisfaction
Formula (2):
Fi(X)=αi*ηi(X) (2)
Wherein, subscript i corresponds to different water level period coefficients, respectively 1,2,3.
4. optimum design method according to claim 1 or 3, which is characterized in that the design variable X is motor axial length L1、
Rotor internal diameter Rr, permanent magnet thickness hm, the wide δ of air gap, the high h of stator slots, the high h of stator yokey, stator facewidth wt, pole embrace αpIn
It is one or more.
5. optimum design method according to claim 1, which is characterized in that the constraint condition GjIt (X) include stator winding
Current density, J1(X), stator copper factor Sf(X), air gap flux density Bg(X), stator teeth flux density Bt (X) and/or stator yoke flux density
By(X) constraint function.
6. optimum design method according to claim 1, which is characterized in that the optimization algorithm in the step S3 is to lose
Propagation algorithm.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110555249A (en) * | 2019-08-20 | 2019-12-10 | 湖南大学 | motor parameter design method based on global optimal water pump load annual loss power consumption |
| CN111001632A (en) * | 2019-12-02 | 2020-04-14 | 平庆义 | Ash removal method for coating equipment |
| CN111859668A (en) * | 2020-07-21 | 2020-10-30 | 河南郑大水利科技有限公司 | Radial flow type hydropower station optimized operation method based on big data |
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| CN103470442A (en) * | 2013-09-17 | 2013-12-25 | 郑程遥 | Method for selecting rotation speed of double-speed salient-pole synchronous water-turbine generator set |
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| CN103470442A (en) * | 2013-09-17 | 2013-12-25 | 郑程遥 | Method for selecting rotation speed of double-speed salient-pole synchronous water-turbine generator set |
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| AMIR NIKBAKHSH等: "Design and optimization of permanent magnet synchronous generator for use in hydrodynamic renewable energy by appling ACO and FEA", 《IIUM ENGINEERING JOURNAL》 * |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110555249A (en) * | 2019-08-20 | 2019-12-10 | 湖南大学 | motor parameter design method based on global optimal water pump load annual loss power consumption |
| CN111001632A (en) * | 2019-12-02 | 2020-04-14 | 平庆义 | Ash removal method for coating equipment |
| CN111859668A (en) * | 2020-07-21 | 2020-10-30 | 河南郑大水利科技有限公司 | Radial flow type hydropower station optimized operation method based on big data |
| CN111859668B (en) * | 2020-07-21 | 2023-11-17 | 河南郑大水利科技有限公司 | Runoff hydropower station optimal operation method based on big data |
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