CN110083912B - Hydraulic permanent magnet generator optimal design method with optimal annual energy production - Google Patents
Hydraulic permanent magnet generator optimal design method with optimal annual energy production Download PDFInfo
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Abstract
The invention provides an optimal design method of a hydroelectric permanent magnet generator with optimal annual energy production, which establishes a hydrological database of a target hydropower station, wherein the database comprises relevant statistical data such as water head, flow, rainfall, water level and the like; then, based on the established database, analyzing the load information change of the input end of the generator caused by the hydrological data distribution difference, and matching to obtain the pre-operation working condition of the generator; and finally, establishing a multi-efficiency optimization system based on the pre-operation working condition, and solving objective functions of a plurality of efficiencies by utilizing an optimization algorithm to obtain key design parameters. The method reasonably selects and utilizes a plurality of variables in the hydropower field to design the motor parameters, can synchronously improve the comprehensive operation efficiency of the hydroelectric generator in a plurality of water level periods, has small calculated amount, and is particularly suitable for the hydropower field.
Description
Technical Field
The invention relates to the technical field of hydroelectric power generation optimization, in particular to an optimal design method of a hydroelectric permanent magnet generator with optimal annual energy production.
Background
The generator is a key component for energy conversion of the hydroelectric power generation system, the efficiency of the generator directly influences the generated energy of the hydroelectric power system, and therefore, the efficiency optimization is an important target of motor design. The motor optimal design is a motor design method for rapidly and accurately finding out optimal design parameters for motor designers by using a rapidly developed computer technology, and the motor optimal design method is widely applied to the field of motor design and is a hot spot for people to study.
The existing efficiency optimization design of the generator generally uses a rated efficiency model of the motor as an objective function, uses performance constraint of the motor as a constraint condition, and utilizes an optimization algorithm to obtain an optimal solution of the objective function through continuous iterative calculation and solution, wherein the design flow is shown in figure 1. Theoretically, substituting the obtained optimal solution into the design parameters of the motor can make the rated operation efficiency of the motor reach the maximum value.
In the field of hydropower stations, as the hydropower stations are affected by regional rainfall, water flow in up and down flow fields and other factors, the water level distribution difference of reservoirs or rivers is quite obvious in one year, and the water level difference directly causes the great load change of the input end of the generator. In this case, the motor will run for less time at rated conditions. The traditional efficiency optimization design method assumes that the generator continuously operates in a rated state and only optimizes the rated efficiency of the generator, and does not consider the important influence of the change of the load at the input end on the operation efficiency, which may cause the operation efficiency of the generator under the non-rated working condition to be greatly reduced, further seriously reduce the overall operation efficiency of the generator and reduce the annual energy production of the hydropower station.
The existing design method of the generator for hydroelectric power generation does not conduct working condition pre-analysis on a target hydropower station and does not conduct load matching, the generator is supposed to work in a rated working condition for a long time, the influence of water level distribution change in the annual period of a reservoir or a river on the working condition change of the generator is not considered, the rated operation efficiency of the generator is only optimally designed, the actual multi-working condition operation environment of the hydropower station is difficult to adapt, the operation efficiency of the generator in the non-rated load is reduced, and annual generating capacity output of the hydropower station is further influenced.
Disclosure of Invention
In order to overcome the problem, the invention provides an optimal design method of the hydroelectric permanent magnet generator with optimal annual energy production, which can synchronously improve the comprehensive operation efficiency of the hydroelectric generator in a plurality of water level periods and is particularly suitable for the field of hydroelectric generation.
The invention provides an optimal design method of a hydroelectric permanent magnet generator with optimal annual energy production, which comprises the following steps:
s1: establishing a hydrological database, wherein the hydrological database comprises a water head, a flow, a rainfall and a water level in a year period;
s2: performing data analysis on the hydrologic database, discretizing hydrologic data to distinguish hydrologic periods, wherein the hydrologic periods comprise a dead water period, a flat water period and a rich water period, and establishing the input power P of the permanent magnet generator mapped by each hydrologic period 1 ～P 3 Input rotational speed omega 1 ～ω 3 Time duty factor alpha 1 ～α 3 Further establishing an analytic relationship between the hydrologic variation and the generator efficiency, thereby obtaining an efficiency model eta corresponding to each hydrologic period 1 (X)～η 3 (X)；
Wherein the accumulated time T occupied by each hydrologic period 1 ～T 3 Determines the corresponding time duty factor alpha 1 ～α 3 ，α 1 ～α 3 The calculation formula (1) of (2) is:
subscript i is a coefficient of different water level periods, and the values are 1, 2 and 3 respectively;
s3: establishing an analytic model of the permanent magnet synchronous generator, and thus iteratively acquiring an optimal design variable X by utilizing an optimization algorithm; the analytical model comprises multiple objective functions F i (X) constraint G j (X) and a design variable X of the generator, said multiple objective function F i The creation of (X) uses the time duty factor alpha 1 ～α 3 And efficiency model eta 1 (X)～η 3 (X)。
In another embodiment, the hydrological database established in the step S1 is specifically a hydrological database of hydrological information of the target reservoir or river.
In another embodiment, the multiple objective function F i (X) specifically satisfies the following formula (2):
F i (X)＝α i *η i (X) (2)
the subscript i corresponds to different water level period coefficients and is 1, 2 and 3 respectively.
In another embodiment, the design variable X is the motor shaft length L 1 Inner diameter R of rotor r Thickness h of permanent magnet m Air gap width delta, stator slot height h s Stator yoke height h y Stator tooth width w t Coefficient of polar arc alpha p One or more of the following.
In another embodiment, the constraint G j (X) includes stator winding current density J 1 (X) stator slot filling ratio S f (X) air gap flux density B g (X) magnetic density of stator tooth B t (X) and/or stator yoke magnetic density B y Constraint function of (X).
In another embodiment, the optimization algorithm in step S3 is a genetic algorithm.
The method has the advantages that the method for establishing and analyzing the hydrological database can help motor designers to more comprehensively know the working condition distribution of the power generator to be designed, so that the design scheme is more targeted. Furthermore, the optimization algorithm uses the generator operation efficiency of a plurality of water level periods as an objective function, and can synchronously optimize the operation efficiency of the generator in a multi-load state. The invention thus has the advantage of increasing the annual energy production of a hydroelectric power plant.
Drawings
FIG. 1 is a design flow chart of a motor design parameter;
FIG. 2 is a schematic diagram of a load matching flow of the hydro-power generation system of the present invention;
FIG. 3 is a schematic diagram of an optimal design flow for annual energy production.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
As shown in fig. 2, a load matching flow diagram of a hydro-power generation system is shown, and as can be seen from the figure, the load matching flow includes:
1) And (3) establishing a hydrologic database: establishing a hydrological database of hydrological information of a target reservoir or river; the hydrologic database is established by carrying out data statistics and analysis on hydrologic information of a target hydropower station, and particularly is an integrated database for establishing information such as water head, flow, rainfall, water level and the like of a reservoir or river where the target hydropower station is located in a annual period.
2) Load matching of the generator: the established hydrologic database is subjected to data analysis, the dead water period, the flat water period and the high water period are distinguished according to the data such as water level, flow rate, water head and rainfall, the hydrologic data are discretized (the discretization accords with the operation characteristics of the motor influenced by the water level difference in the hydroelectric power generation field), and the input power P of the motor mapped in each hydrologic period is established 1 ～P 3 Input rotational speed omega 1 ～ω 3 Time duty factor alpha 1 ～α 3 Further establishing an analytic relationship between the hydrologic variation and the generator efficiency, thereby obtaining an efficiency model eta corresponding to each hydrologic period 1 (X)～η 3 (X)；
3) Optimization procedure: and establishing an analysis model of the permanent magnet synchronous generator, so that the design parameters of the motor are obtained by iteration through an optimal algorithm in an optimization program, the method can be applied to the field of hydroelectric generation, and the comprehensive operation efficiency of the hydroelectric generator in a plurality of water level periods is improved.
In one embodiment, the hydrographic data is discretized into three important hydrographic periods, respectively:
s1, withered water period: the dry period mainly refers to the reservoir level H i In the lower interval (H) 0 -H 1 ) The accumulated time is T 1 . The input power P of the generator can be controlled in the dry period 1 Input rotational speed omega 1 Producing influence and further influencing the electromagnetic performance and efficiency model eta of the motor 1 (X). Accumulated time T of dead water period 1 Determining the time duty factor alpha 1 The calculation formula is shown as (1).
S2, horizontal period: the level period mainly refers to the reservoir level H i In the flatter interval (H) 1 -H 2 ) The accumulated time is T 2 . The input power P of the generator can be controlled in the dry period 2 Input rotational speed omega 2 Producing influence and further influencing the electromagnetic performance and efficiency model eta of the motor 2 (X). Accumulated time T of dead water period 2 Determining the time duty factor alpha 2 The calculation formula is shown as (1).
S3, water-enlarging period: the dry period mainly refers to the reservoir level H i In the upper interval (H) 2 -H 3 ) The accumulated time is T 3 . The input power P of the generator can be controlled in the dry period 3 Input rotational speed omega 3 Producing influence and further influencing the electromagnetic performance and efficiency model eta of the motor 3 (X). Accumulated time T of dead water period 3 Determining the time duty factor alpha 3 The calculation formula is shown as (1).
The subscript i is the coefficient of different water level periods, and sequentially represents a dead water period, a flat water period and a rich water period, which are respectively 1, 2 and 3.
The hydrologic information can have an important influence on the running performance of the motor, and the establishment of a hydrologic database and the load matching of the generator can have a key effect on the optimal design of the multiple-effect rate of the generator in the next step.
FIG. 3 shows a schematic diagram of an annual energy production optimizing design flow, as can be seen, the annual energy production optimizing design is a multi-objective optimizing design, and the design method comprises the following steps:
and establishing an analytic model of the permanent magnet synchronous generator based on the load matching model, and selecting an optimal algorithm (such as a genetic algorithm) to perform multi-objective optimization on the analytic model so as to obtain an optimal solution of the model, namely an optimal design variable of the permanent magnet synchronous generator. The analytical model comprises multiple objective functions F i (X), constraint G j (X) design variable X.
In one embodiment, the intelligent algorithmThe method selects genetic algorithm, the generator adopts a surface-mounted permanent magnet synchronous motor structure, and the analytical model comprises a design variable X and a constraint condition G j (X) multiple objective function F i (X) and time ratio coefficient alpha i Wherein:
s1, designing a variable X: the design variables that can have an important influence on motor efficiency include: motor shaft length L 1 Inner diameter R of rotor r Thickness h of permanent magnet m Air gap width delta, stator slot height h s Stator yoke height h y Stator tooth width w t Coefficient of polar arc alpha p 。
S2, constraint condition G j (X): the performance constraints of electricity, magnetism, heat, force and the like of the motor are embodied as dependent variable functions in an analytical model. The constraint condition can enable the calculated value of the algorithm to be in a reasonable range all the time, and as the application belongs to the field of hydropower, the constraint condition considered in the case mainly comprises: stator winding current density J 1 (X) stator slot filling ratio S f (X) air gap flux density B g (X) magnetic density of stator tooth B t (X) magnetic density B of stator yoke y Constraint function of (X).
S3, multiple objective functions F i (X) and time ratio coefficient alpha i The optimization program in the case is multi-objective optimization, and the multi-objective function F i The creation of (X) uses the time duty factor alpha 1 ～α 3 And efficiency model eta 1 (X)～η 3 (X). Specifically, as shown in formula (2), the multiple objective function F i (X) is an efficiency model eta i (X) of the hydropower station in the dead water period, the flat water period and the rich water period and a corresponding time duty ratio coefficient alpha i The product of (a) and the time duty factor alpha i The time duty ratio coefficient alpha of the dry period, the flat period and the rich period i The larger the time-to-duty factor, the higher the optimized weight.
Wherein the multiple objective functions F i (X) is:
F i (X)＝α i* η i (X) (2)
the subscript i corresponds to different water level period coefficients and is 1, 2 and 3 respectively.
Finally, the genetic algorithm carries out iterative computation and solution on a multi-effect rate optimization system established by the design variables, the constraint conditions and the objective function. And when the judgment condition of algorithm ending is met, ending the optimization program and outputting the optimal solution.
Firstly, establishing a hydrological database of a target hydropower station, wherein the database comprises relevant statistical data such as water head, flow, rainfall, water level and the like; then, based on the established database, analyzing the load information change of the input end of the generator caused by the hydrological data distribution difference, and matching to obtain the pre-operation working condition of the generator; and finally, establishing a multi-efficiency optimization system based on the pre-operation working condition, and solving objective functions of a plurality of efficiencies by utilizing an optimization algorithm to obtain key design parameters. The method is used for reasonably selecting and designing motor parameters by utilizing various variables in the hydrologic field, so that annual energy generation output of the hydropower station can be effectively improved, and the calculated amount is small.
The technical solutions of the embodiments of the present invention may be combined with each other, but it is necessary to base the implementation of those skilled in the art, and when the technical solutions are contradictory or cannot be implemented, it should be considered that the combination of the technical solutions does not exist and is not within the scope of protection claimed by the present invention.
Finally, the method of the present invention is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An optimal design method of a hydroelectric permanent magnet generator with optimal annual energy production is characterized by comprising the following steps:
s1: establishing a hydrological database, wherein the hydrological database comprises a water head, a flow, a rainfall and a water level in a year period;
s2: data analysis is performed on the hydrologic database, hydrologic data are discretized to distinguish hydrologic periods, and the hydrologic periods comprise dead water periodsThe water leveling period and the water enlarging period, and establishes the input power P of the permanent magnet generator mapped in each hydrologic period 1 ～P 3 Input rotational speed omega 1 ～ω 3 Time duty factor alpha 1 ～α 3 Further establishing an analytic relationship between the hydrologic variation and the generator efficiency, thereby obtaining an efficiency model eta corresponding to each hydrologic period 1 (X)～η 3 (X)；
Wherein the accumulated time T occupied by each hydrologic period 1 ～T 3 Determines the corresponding time duty factor alpha 1 ～α 3 ，α 1 ～α 3 The calculation formula (1) of (2) is:
subscript i is a coefficient of different water level periods, and the values are 1, 2 and 3 respectively;
s3: establishing an analytic model of the permanent magnet synchronous generator, and thus iteratively acquiring an optimal design variable X by utilizing an optimization algorithm; the analytical model comprises multiple objective functions F i (X) constraint G j (X) and a design variable X of the generator, said objective function F i The creation of (X) uses the time duty factor alpha 1 ～α 3 And efficiency model eta 1 (X)～η 3 (X) wherein F i (X)＝α i* η i (X)。
2. The optimization design method according to claim 1, wherein the hydrologic database established in step S1 is specifically a hydrologic database of hydrologic information of a target reservoir or river.
3. The optimization design method according to claim 1, wherein the design variable X is a motor shaft length L 1 Inner diameter R of rotor r Thickness h of permanent magnet m Air gap width delta, stator slot height h s Stator yoke height h y Stator tooth width w t Coefficient of polar arcα p One or more of the following.
4. The optimization design method according to claim 1, wherein the constraint condition G j (X) includes stator winding current density J 1 (X) stator slot filling ratio S f (X) air gap flux density B g (X) magnetic density of stator tooth B t (X) and/or stator yoke magnetic density B y Constraint function of (X).
5. The optimization design method according to claim 1, wherein the optimization algorithm in the step S3 is a genetic algorithm.
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| Design and optimization of permanent magnet synchronous generator for use in hydrodynamic renewable energy by appling ACO and FEA;Amir Nikbakhsh等;《IIUM Engineering Journal》;20170228;第18卷(第2期);158-176 * |
| 农村小水电站增效扩容水能复核及技术改造方案;谭剑波等;《人民黄河》;20180831;第40卷(第8期);123-126 * |
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