CN110083912A - The optimal waterpower permanent magnet generator optimum design method of annual electricity generating capacity - Google Patents

Abstract

The present invention provides a method for optimal design of hydraulic permanent magnet generators with optimal annual power generation. The method establishes the hydrological database of the target hydropower station, and the database includes relevant statistical data such as water head, flow, rainfall and water level; then based on the established The database analyzes the changes in the load information of the generator input end caused by the difference in the distribution of hydrological data, and matches the pre-operating conditions of the generator; finally, a multi-efficiency optimization system is established based on the pre-operating conditions, and the optimization algorithm is used to optimize the efficiency of multiple efficiencies. The objective function is solved and the key design parameters are obtained. Using this method to reasonably select and utilize many variables in the field of hydropower to design motor parameters can simultaneously improve the comprehensive operating efficiency of hydroelectric generators in multiple water level periods, and the amount of calculation is small, which is especially suitable for the field of hydropower.

Description

Optimization design method of hydraulic permanent magnet generator with optimal annual power generation

technical field

The invention relates to the technical field of hydraulic power generation optimization, and more specifically, relates to an optimal design method of a hydraulic permanent magnet generator with optimal annual power generation.

Background technique

Generators are key components of energy conversion in hydroelectric power generation systems. The efficiency of generators directly affects the power generation of hydropower systems. Therefore, efficiency optimization is an important goal of motor design. Motor optimization design is a motor design method that uses fast-growing computer technology to quickly and accurately find the best design parameters for motor designers. The optimal design method of motors is widely used in the field of motor design and is also a research topic. hotspot.

The efficiency optimization design of existing generators usually takes the rated efficiency model of the motor as the objective function, takes the performance constraints of the motor as the constraints, and uses the optimization algorithm to obtain the optimal solution of the objective function through continuous iterative calculations. The design process is as follows: Figure 1 shows. In theory, substituting the obtained optimal solution into the design parameters of the motor can make the rated operating efficiency of the motor reach the maximum value.

In the field of hydropower, since hydropower stations are affected by various factors such as regional rainfall, water flow in upper and lower basins, etc., the water level distribution of reservoirs or rivers varies significantly in a year, and the difference in water level directly leads to the input of the generator. The load varies greatly. In this case, the motor will run less time at rated conditions. The traditional efficiency optimization design method assumes that the generator continues to operate at the rated state and only optimizes the rated efficiency of the generator, without considering the important impact of the change of the input load on the operating efficiency, which may cause the generator to operate at non-rated The greatly reduced operating efficiency under working conditions will further seriously reduce the overall operating efficiency of the generator and reduce the annual power generation of the hydropower station.

The existing generator design methods for hydroelectric power generation do not carry out pre-analysis of the working conditions of the target hydropower station, do not carry out load matching, assume that the generator will work at the rated working condition for a long time, and do not take into account the impact of changes in the water level distribution of the reservoir or river in the annual cycle. However, it is difficult to adapt to the actual multi-working condition operating environment of the hydropower station if only the rated operating efficiency of the generator is optimized, which will lead to a decrease in the operating efficiency of the generator at non-rated load, further Affect the annual power output of the hydropower station.

SUMMARY OF THE INVENTION

In order to overcome this problem, the present invention provides an optimal design method of a hydraulic permanent magnet generator with the best annual power generation. This method can simultaneously improve the comprehensive operating efficiency of the hydraulic generator in multiple water level periods, and is especially suitable for field of hydropower.

The invention provides a method for optimal design of a hydraulic permanent magnet generator with optimal annual power generation, the method comprising the following steps:

S1: Establish a hydrological database, the hydrological database includes the head, flow, rainfall and water level in the annual cycle;

S2: Perform data analysis on the hydrological database, discretize the hydrological data to distinguish the hydrological period, the hydrological period includes the dry season, the normal water period and the wet season, and establish the input power of the permanent magnet generator mapped to each hydrological period P ₁ ～ P ₃ , input speed ω ₁ ～ω ₃ , and time proportion coefficient α ₁ ～α ₃ , and then establish the analytical relationship between hydrological changes and generator efficiency, so as to obtain the efficiency model η ₁ (X) corresponding to each hydrological period ~η ₃ (X);

Among them, the cumulative time T ₁ to T ₃ occupied by each hydrological period determines the corresponding time ratio coefficients α ₁ to α ₃ , and the calculation formula (1) of α ₁ to α ₃ is:

The subscript i is the coefficient of different water level periods, and the values are 1, 2, and 3 respectively;

S3: Establish an analytical model of the permanent magnet synchronous generator, thereby using an optimization algorithm to iteratively obtain the optimal design variable X; the analytical model includes a multi-objective function F _i (X), constraint conditions G _j (X) and the design variable X of the generator, the establishment of the multi-objective function F _i (X) utilizes the time ratio coefficients α ₁ ~ α ₃ and the efficiency models η ₁ (X) ~ η ₃ (X).

In another embodiment, the hydrological database established in step S1 is specifically a hydrological database of hydrological information of the target reservoir or river.

In another embodiment, the multi-objective function F _i (X) specifically satisfies the following formula (2):

F _i (X) = α _i *η _i (X) (2)

Among them, the subscript i corresponds to the coefficients of different water level periods, which are 1, 2, and 3 respectively.

In another embodiment, the design variable X is motor shaft length L ₁ , rotor inner diameter R _r , permanent magnet thickness h _m , air gap width δ, stator slot height h _s , stator yoke height h _y , stator teeth One or more of width w _t and polar arc coefficient α _p .

In another embodiment, the constraints G _j (X) include stator winding current density J ₁ (X), stator slot fullness S _f (X), air gap magnetic density B _g (X), stator tooth Constraint function of flux density B _t (X) and/or stator yoke flux density By _y (X).

In another embodiment, the optimization algorithm in step S3 is a genetic algorithm.

The invention has the advantage that the establishment and analysis method of the hydrological database can help motor designers to more comprehensively understand the working condition distribution of the generator to be designed, so that the design scheme is more targeted. Furthermore, the optimization algorithm takes the operating efficiency of the generator in multiple water level periods as the objective function, and can simultaneously optimize the operating efficiency of the generator in multiple load states. Therefore, the present invention has the advantage of increasing the annual power generation of hydroelectric power plants.

Description of drawings

Fig. 1 is a design flow chart of a kind of motor design parameter;

Fig. 2 is a schematic diagram of the load matching flow chart of the hydroelectric power generation system of the present invention;

Fig. 3 is a schematic diagram of an optimal design process for optimal annual power generation in the present invention.

Detailed ways

The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are intended to illustrate the present invention, but not to limit the scope of the present invention.

As shown in Figure 2, it shows a schematic diagram of a load matching process of a hydroelectric power generation system. It can be seen from the figure that the load matching process includes:

1) Establishment of a hydrological database: establish a hydrological database for the hydrological information of the target reservoir or river; wherein, the establishment of the hydrological database is to carry out data statistics and analysis for the hydrological information of the target hydropower station, specifically to establish the reservoir or river where the target hydropower station is located. A comprehensive database of information such as head, flow, rainfall and water level during the cycle.

2) Generator load matching: analyze the data of the established hydrological database, distinguish dry season, normal water season, and wet season according to data such as water level, flow, water head, and rainfall, and discretize the hydrological data (this discretization is in line with hydroelectric power generation). The operating characteristics of motors affected by water level differences in the field), and establish the input power P ₁ ~ P ₃ , input speed ω ₁ ~ ω ₃ , and time proportion coefficient α ₁ ~ α ₃ of the motor mapped in each hydrological period, and then establish Analytical relationship between hydrological changes and generator efficiency to obtain efficiency models η ₁ (X) to η ₃ (X) corresponding to each hydrological period;

3) Optimization program: establish the analytical model of the permanent magnet synchronous generator, so as to use the optimal algorithm in the optimization program to iteratively obtain the motor design parameters, so that it can be applied to the field of hydropower generation, and improve the performance of the hydroelectric generator at multiple water levels. The comprehensive operating efficiency of the period.

In one embodiment, the hydrological data are discretized into three important hydrological periods, which are:

S1. Dry season: The dry season mainly refers to the low water level H _i of the reservoir (H ₀ -H ₁ ), and its cumulative time is T ₁ . The dry season will affect the input power P ₁ and input speed ω ₁ of the generator, and will further affect the electromagnetic performance of the motor and the efficiency model η ₁ (X). The cumulative time T ₁ in the dry season determines the time proportion coefficient α ₁ , and the calculation formula is shown in (1).

S2. Flat water period: The flat water period mainly refers to that the reservoir water level H _i is in a relatively gentle interval (H ₁ -H ₂ ), and its cumulative time is T ₂ . The dry season will affect the input power P ₂ and input speed ω ₂ of the generator, and will further affect the electromagnetic performance of the motor and the efficiency model η ₂ (X). The cumulative time T ₂ in the dry season determines the time proportion coefficient α ₂ , and the calculation formula is shown in (1).

S3. Wet water season: the dry season mainly refers to the reservoir water level H _i being in a relatively high interval (H ₂ -H ₃ ), and its cumulative time is T ₃ . The dry season will affect the input power P ₃ and input speed ω ₃ of the generator, and will further affect the electromagnetic performance of the motor and the efficiency model η ₃ (X). The cumulative time T ₃ in the dry season determines the time proportion coefficient α ₃ , and the calculation formula is shown in (1).

Among them, the subscript i is the coefficient of different water level periods, representing the dry season, normal water season and wet season in turn, which are 1, 2, and 3, respectively.

Hydrological information will have an important impact on the performance of the motor. The establishment of the hydrological database and the matching of the generator load will play a key role in the next step of the multi-efficiency optimization design of the generator.

Figure 3 shows a schematic diagram of an optimal design process for optimal annual power generation. As can be seen from the figure, the optimal design for optimal annual power generation is a multi-objective optimization design, and the design method includes:

Establish the analytical model of the permanent magnet synchronous generator based on the load matching model, and select the optimal algorithm (such as genetic algorithm) to perform multi-objective optimization on the analytical model, so as to obtain the optimal solution of the model, that is, the optimal permanent magnet synchronous generator design variables. The analytical model includes a multi-objective function F _i (X), a constraint condition G _j (X), and a design variable X.

In an implementation case, the intelligent algorithm uses the genetic algorithm, the generator adopts the structure of the surface-mounted permanent magnet synchronous motor, and the analytical model includes the design variable X, the constraint condition G _j (X), the multi-objective function F _i (X) and the time occupation Ratio coefficient α _i , where:

S1. Design variable X: Refers to the structural parameters of the motor that can have an important impact on the efficiency of the motor. The design variables selected in the case include: motor shaft length L ₁ , rotor inner diameter R _r , permanent magnet thickness h _m , air gap width δ, and stator slot height h _s , stator yoke height h _y , stator tooth width w _t , pole arc coefficient α _p .

S2. Constraint condition G _j (X): It is the electric, magnetic, thermal, force and other performance constraints of the motor reflected in the dependent variable function in the analytical model. Constraint conditions can keep the calculated value of the algorithm within a reasonable range. Since this application belongs to the field of hydropower, the constraints considered in this case mainly include: stator winding current density J ₁ (X), stator slot fullness rate S _f (X ), air gap flux density B _g (X), stator tooth flux density B _t (X), and stator yoke flux density B _y (X) constraint functions.

S3. Multi-objective function F _i (X) and time proportion coefficient α _i , the optimization program in the case is multi-objective optimization, and the establishment of the multi-objective function F _i (X) utilizes time proportion coefficient α ₁ ~ α ₃ and efficiency models η ₁ (X) to η ₃ (X). Specifically, as shown in formula (2), the multi-objective function F _i (X) is the product of the efficiency model ηi(X) of the hydropower station in the dry season, normal water season, and wet season and the corresponding time proportion coefficient α _i , respectively, The time proportion coefficient α _i is the time proportion coefficient α _i of the dry season, normal water season, and wet season. The larger the time proportion coefficient, the higher the weight of optimization.

Among them, the multi-objective function F _i (X) is:

F _i (X) = α _i* η _i (X) (2)

Among them, the subscript i corresponds to the coefficients of different water level periods, which are 1, 2, and 3 respectively.

Finally, the genetic algorithm iteratively calculates and solves the multi-efficiency optimization system established by the design variables, constraints and objective functions. When the judgment condition for the end of the algorithm is met, the optimization program ends and the optimal solution is output.

The method of the present invention first establishes the hydrological database of the target hydropower station, and the database includes related statistical data such as water head, flow, rainfall, and water level; then based on the established database, the change of the load information at the input end of the generator caused by the difference in the distribution of hydrological data is analyzed. , to match the pre-operating conditions of the generator; finally, a multi-efficiency optimization system is established based on the pre-operating conditions, and the optimization algorithm is used to solve the objective functions of multiple efficiencies and obtain key design parameters. Using this method to reasonably select and utilize many variables in the hydrological field to design the motor parameters can effectively improve the annual power output of the hydropower station with a small amount of calculation.

The technical solutions of the various embodiments of the present invention can be combined with each other, but it must be based on the realization of those skilled in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of such technical solutions does not exist , nor within the scope of protection required by the present invention.

Finally, the method of the present invention is only a preferred embodiment, and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

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 F_i(X), constraint condition G_j(X) and the design variable X of generator, described Objective function F_i(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 F_i(X) under specific satisfaction Formula (2):

F_i(X)=α_i*η_i(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 R_r, permanent magnet thickness h_m, the wide δ of air gap, the high h of stator slot_s, the high h of stator yoke_y, stator facewidth w_t, pole embrace α_pIn It is one or more.

5. optimum design method according to claim 1, which is characterized in that the constraint condition G_jIt (X) include stator winding Current density, J₁(X), stator copper factor S_f(X), air gap flux density B_g(X), stator teeth flux density Bt (X) and/or stator yoke flux density B_y(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.