KR20120109211A - Optimal design method of direct-driven pm wind generator for decreasing pm using rsm - Google Patents

Abstract

PURPOSE: An optimal design method of a wind power generator is provided to shorten optimal performance time by using a reaction surface method for applying internet distribution computing. CONSTITUTION: An axis direction length, a height, and a magnetic pole angle are selected as a design variable for a permanent magnet wind generator rotor. Driving efficiency and permanent magnet surface are set up as an object function. The calculated result is selected after interpretation. The selected result is used as an initial model. The selected result is inputted to the entire processing. Final design variable is determined.

Description

Optimal Design Method of Direct-driven PM Wind Generator for Decreasing PM using RSM}

The present invention relates to an optimal design method of a permanent magnet wind power generator using a response surface method, and more particularly, to a response surface method based on an experimental design method to reduce the size of a permanent magnet by applying an optimization technique of a permanent magnet wind power generator. The optimum design method of permanent magnet wind power generator using the response surface method for minimizing the maximum annual energy production and magnet amount by using.

Entering the modern society, natural resources are depleted, and the use of electric appliances is rapidly increasing due to the increase in the use of home appliances. Along with this phenomenon, the wind power generation system is rapidly developing with advantages of reducing energy production costs and eco-friendly characteristics. In particular, in order to solve the power shortage phenomenon, MW-class medium and large sized wind power generators are in the spotlight, and in reality, customized generators considering geographical characteristics and wind speed are being installed worldwide. In particular, the wind power generation system is classified into a direct drive type with or without a gear box according to a driving method. In the case of a wind power generation system having a speed increaser, many mechanical losses are generated due to the speed increaser, and thus, the lifespan of the wind power generation system is shortened. Recently, the wind power generation system has been adopted a lot of direct drive type in terms of high efficiency and high performance. In addition, since the generator of the direct-driven wind power generation system must operate at a low speed and have a relatively high torque density, a multipole permanent magnet synchronous generator having high torque density and high efficiency is mainly used.

In general, for the purpose of designing a wind power generator, output characteristics and material cost at rated wind speed have been selected. However, wind power generation systems are not always operated at rated wind speeds, so the full range of operating wind speeds should be considered. As a method to consider the operating wind speed range, Grauers uses the wind speed distribution method to calculate the loss ratio coefficient at each wind speed based on the generator loss at the rated wind speed. However, the calculation is complicated and not practical. Inoue, on the other hand, uses the Weibull function as the wind speed probability distribution function. However, in this case, detailed data shape factor and scale factor of the wind speed distribution in the region where the generator is installed are needed.

Conventionally, when performing the wind turbine design, the characteristic analysis is performed in the entire wind speed range using the annual energy production (AEP) as the objective function. In order to carry out the design, there was a problem that a lot of permanent magnets have to be used, which is relatively high in the manufacture of a wind generator.

 [1] J. F. Manwell, J. G. McGowan and A. L. Rogers, "Wind Energy Theory, Design and Application," John Wiley & Sons, 1st Ed .. 2002.  [2] Y. Chen, P. Pillay and M. A. Khan, "PM wind generator comparison of different topologies," Proc. of 39th IAS Annual Meeting Conference, Vol. 3, No. 3-7, pp. 1405-1412. October, 2004.  [3] M. A. Khan, P. Pillay and M. Malengret, "Impact of direct-drive WEC Systems on the design of a small PM wind generator," Proc of IEEE Power Tech Conference, Vol. 2, pp. 23-26, June, 2003.  4 W. Wu, V. S. Ramsden and T. Crawford, "A Low-speed, High-torque, Direct-drive Permanent Magnet Generator for Wind Turbines," Conference Record of Industry Applications, Vol. 1, pp. 147-154, October, 2000.  [5] Donald S. Zinger et al, "Annualized Wind Energy Improvement Using Variable Speeds," IEEE Transactions on Industry Applications, Vol. 33, pp. 1444-1447, November, 1997.  [6] Anders Grauers, "Design of Direct-driven Permanent-magnet Generators for Wind Turbines," Ph. D Thesis Chalmers University, October 1996.  [7] Inoue, A., Hasan Ali Mohd., Takahashi, R., Murata, T., Tamura, J., Ichinose and M., Kazumasa Ide, "A Calculation Method of the Total Efficiency of Wind Generator," Proc. of PEDS 2005, Vol. 2, No. 28-01, pp. 1595-1600, November, 2005.  D. E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning," Addison-Wesley Publishing Co. Inc., N. Y., 1989.  [9] A. Esposito and L. Tarricone, "Grid computing for electromagnetics: a beginner's guide with applications," IEEE Antennas and Propagation Magazine, Vol. 45, No. 2, pp. 92-99, April 2003.  [10] M. Milenkovic et al., "Toward Internet distributed computing," Computer, pp. 38-46, May 2003.  [11] H. S. Choi et al., "A Distributed Computing Technique for Analysis of Electric Machines Using Internet Web Services," Digest of CEFC2004, pp. 124, June 6-9, 2004.  Literature 12, et al., “Optimal Design of Permanent Magnet DC Motor Using Parallel Computing,” The Korean Institute of Electrical Engineers Summer Conference, 649-650, 2006.  [Document 13] Yoo, Youn-Soo, Kyung Nam-Ho, “Development of 750kW Wind Power Generator”, Kangwon National University Invention Report, 2004.  [Document 14] Won-Soo Won, “University Design in College,” pp.18-24, 1994.  C. Audet, J. E. Dennis JR, "Mesh adaptive direct search algorithms for constrained optimization," SIAM J. Optim., Vol. 17, No. 1, pp. 188-217, 2006.  [16] Moon Byung-ro, “Genetic Algorithm”, 2003.  17. T. G. Kolda. R. M. Lewis, and V. Torczon, "Optimization by direct search: new perspectives on some classical and modern methods.SIAM Rev., 45 (3): 385-482 (electronic), 2003.  C. Audet, J. E. Dennis JR, "Mesh adaptive direct search algorithms for constrained optimization," SIAMJ. Optim., Vol. 17, No. 1, pp. 188-217, 2006.

The present invention has been made to solve the problems described above, by using an optimization algorithm to reduce the amount of magnets used in the design of wind power generators and at the same time utilizing the Internet distributed computing finite by repeating the calculation process or the execution part of the instruction It is an object of the present invention to provide an optimal design method of a permanent magnet wind turbine using the response surface method that solves the problem of excessive calculation time that occurs during the optimal design of the wind turbine using the element analysis method.

In addition, in order to make up for the shortcomings of the response surface method based on the use of computer and experimental design, which are a problem when applying the internet distributed computing, the simulation results according to the design variables are databased to optimize the optimal design execution time of the wind turbine. Another object of the present invention is to provide an optimal design method for a permanent magnet wind turbine using a response surface method for shortening and reducing the amount of magnets.

In order to achieve the above object, the optimum design method of the permanent magnet wind turbine using the reaction surface method according to the present invention is (a) the axial length, height, and pole angle of the permanent magnet in the rotor of the permanent magnet wind turbine. Selecting the design variables, operating efficiency and the area of permanent magnets as the objective function, performing analysis and selecting the calculated results; and (b) inputting the selected result as an initial model to the entire processing and determining final design variables therethrough.

In this case, it is preferable to divide the object group by the number of the main computer executing the optimization program and the plurality of server computers connected to the Internet and perform the objective function in parallel.

According to an embodiment of the present invention as described above, first, the response surface method is used as an optimization method for the optimum design of electric equipment, and the enormous optimization that is a problem of the optimal design using finite element analysis is applied by applying the Internet distributed computing. There is an advantageous effect that can shorten the time.

Second, the multimodal characteristics of the objective function are highly likely to converge with quasi-optimization, which results in a more satisfactory result by overlapping the optimal performance using the response surface method.

Third, the optimal design of the permanent magnet wind turbine can be realized by combining the finite element method and the response surface method, and shortening the optimization execution time, which is a problem of the optimal design using the finite element analysis, by making a database of simulation results according to the design variables. There is an advantage to this.

의 관계를 나타낸 도면,
도 2는 일반적인 풍력 발전 시스템의 출력 특성을 나타낸 도면,
도 3은 인터넷 분산 컴퓨팅과 최적화 수행의 결합 체계를 나타낸 도면,
도 4는 반응표면분석 흐름도를 나타낸 도면,
도 5는 최적화 설계된 표면부착형 영구자석 동기발전기의 정격(509.32kW, 13.5[m/s])조건에서의 자속분포를 비교한 도면,
도 6은 풍속별 효율 비교 결과를 나타낸 도면이다.1 is a power factor according to the general tip speed ratio of the propeller type

Drawing showing the relationship between,
2 is a view showing the output characteristics of a typical wind power generation system,
3 is a diagram showing a combination of Internet distributed computing and optimization performance;
4 is a flowchart illustrating a response surface analysis flow chart;
FIG. 5 is a comparison of magnetic flux distribution under rated (509.32kW, 13.5 [m / s]) condition of an optimized designed surface-mounted permanent magnet synchronous generator.
6 is a view illustrating a result of comparing wind speed and efficiency.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, which are intended to describe in detail enough to be able to easily carry out the invention by those skilled in the art to which the present invention belongs, This does not mean that the technical spirit and scope of the present invention is limited.

Wind power generation system

1. Wind power generation system output characteristics

The optimal design of the wind generators applied to the wind power generation system should be considered together with the output characteristics of the wind power generation system. Therefore, the characteristics of the wind power generation system will be discussed before the optimal design of the Surface-Mounted Permanent Magnet Synchronous Generator (SPMSG).

end. Characteristics of Wind Energy

When the mass m in the air moves at a velocity v, the kinetic energy is expressed in Equation 1 in SI units.

Therefore, the energy due to the flow of air is the amount of change in the kinetic energy per unit time, it can be expressed as Equation 2.

는 풍력에 에너지의 총량[W],

는 공기밀도 [kg/m³], A는 블레이드 통과면적 [m²], v는 풍속 [m/s]라 한다면, 단위시간에 대한 공기의 부피변화는

이며, 질량의 변화는

가 되므로, 풍력 에너지는 다음과 같다.
here,

Is the total amount of energy in wind [W],

Where is the air density [kg / m ³ ], A is the blade passage area [m ² ], and v is the wind speed [m / s],

, The change in mass is

Since the wind energy is as follows.

의 에너지는 터빈에 의해 기계적인 동력으로 변환되는데, 터빈의 이상적인 회전의 경우 즉, 각 운동량의 변화가 없고 터빈 끝의 간섭흐름이 없는 경우 변환효율을 표시하는 출력계수

는 이론적으로 0.593임을 Betz가 밝힌 바 있으나, 공기의 점성과 회전자 끝의 간섭 흐름 등 여러 가지 원인에 의해서 설계와 운전 상태에 따라 달라진다. 따라서 풍력터빈에 따른 출력계수

를 적용하면 풍속이 갖는 에너지는 수학식 4와 같다.

The energy of is converted into mechanical power by the turbine. The output coefficient indicates the conversion efficiency in the case of ideal rotation of the turbine, that is, when there is no change in the angular momentum and no interference flow at the end of the turbine.

Betz stated that the theory is 0.593, but it depends on the design and operating conditions due to various factors such as air viscosity and interference flow at the rotor end. Therefore, output coefficient according to wind turbine

If you apply the energy of the wind speed is shown in Equation 4.

의 관계를 보여준다.1 is a power factor according to the general tip speed ratio of the propeller type

Shows the relationship.

이상의 풍속에서만 시스템의 출력이 생산되기 시작하고, 정격풍속

에서의 정격출력

까지는

에 비례하여 출력을 나타내며, 그 이상의 풍속에서는 시스템의 출력이 일정하게 유지된다. 이러한 정격 상태에서 정격출력의 일정한 유지는 터빈의 날개 각도를 변경하는 즉, 피치제어(pitch control)시스템을 통하여 이루어지며, 결국 날개 각도의 변경을 통하여 터빈의 출력계수

를 조정함으로써 이루어진다. 풍속이 정지풍속

이상이 되면 터빈의 파손방지나 내부시스템의 안정을 위하여 공회전상태가 되어 더 이상 발전을 하지 않는다.
In addition, during the operation of the actual wind power generation system, the starting wind speed may be caused by mechanical inertia, friction, and electrical losses.

Only above this wind speed, the output of the system starts to be produced.

Rated output at

by

The output is proportional to, and at higher wind speeds the system output remains constant. In this rated state, constant maintenance of the rated output is made by changing the blade angle of the turbine, that is, through a pitch control system, and finally, the output coefficient of the turbine by changing the blade angle.

By adjusting it. Wind speed

If it is abnormal, in order to prevent damage to the turbine or stabilize the internal system, the engine is idle and no further power generation is performed.

2 shows output characteristics of a general wind power generation system. Based on the output characteristics of such a wind power generation system, the actual generator design is to be performed.

2. Composition of Wind Power System

In the present invention, the annual energy production (AEP) of the SPMSG will be calculated in consideration of the calculated output and annual operating time by applying the statistical wind speed probability density in the entire operating range of the wind turbine.

Table 1 below shows the configuration and specifications of a wind power generation system using a permanent magnet wind power generator to be designed in the present invention.

Specification of Wind Power Generation System Rated Power output (Ps) 500 kW
Design speed
Cut-in wind speed 3.5 m / s Rated wind speed 13.5 m / s Cut-out wind speed 26 m / s
Generator
Type Surface-mouned
Permanent magent
Synchronous Generator
Turbine rotor
Diameter 39 m Ratational speed range 0 to 32 rpm Swet area 1207m ² Control system Pitch control

3. Loss Calculation of Medium and Large Wind Generators

In the design of the SPMSG, the calculation of each loss must be performed first of all to improve the efficiency by predicting the output. Therefore, in this study, we will examine each loss calculation method of SPMSG.

end. Stator Dongson

는 다음과 같이 표시된다.
Copper is mainly used in electrical circuits of electrical equipment, and in rare cases aluminum or brass is also used. The winding temperature is 75 ° C (the winding temperature, and the characteristics of the device are calculated with the windings at this temperature. However, this is the case for Class A, E, and B Class F and H class insulation machines). Is assumed to be 115 ° C), and the electrical resistance of the copper wire with a cross-sectional area q [mm2] and length 1 [m]

Is displayed as follows.

는 다음과 같이 정의된다.
The current density when current I [A] flows through the copper wire is Δ = I / q [A / mm2], and the copper loss

Is defined as

Where ql is the volume of the copper wire [cm ² ]

이며, 1[kg]당 동손

는 다음과 같이 계산된다.
Since copper specific gravity is approximately 8.9, the weight of copper wire

Copper loss per kilogram

Is calculated as follows.

배(

)가 되어 다음과 같이 근사적으로 나타낼 수 있다.
In addition, the resistance of equation (17) is a resistance to direct current, and when alternating current flows in a copper wire, there is no uniform current distribution in the cross section due to the skin effect, so that the apparent resistance increases, resulting in a copper loss per 1 [kg].

ship(

) Can be approximated as

의 값은 동선 단면의 형태, 전류의 주파수 등에 따라서 변하지만, 실제 기기에서는

=1.0~1.3이다. 따라서 사전에

의 값을 추정하여 이것에 맞는 형상과 치수를 가지는 도선을 선정하며, 결과적으로 동손을 원하는 범위 내로 들어오게 하는 것이 필요하다.

The value of depends on the shape of the copper cross section, the frequency of the current, etc.

= 1.0 to 1.3. So in advance

It is necessary to estimate the value of, select the conductor with the shape and dimensions that fit it, and consequently bring the copper loss into the desired range.

I. Iron loss

In the case of SPMSG, the pore magnetic flux density is expressed as the sum of the magnetic flux densities caused by the permanent magnet and stator current. Thus, unlike induction motors, stator iron losses are expressed as a function of stator current. In general, the value is small enough to be ignored. However, when the proportion of the total loss is large, such as in high-speed rotation, and when the characteristics of the device need to be accurately interpreted, it is necessary to more accurately predict the stator iron loss due to the change in the stator current. Therefore, the magnetic flux density distribution of the device must be accurately known for accurate iron loss calculations and finally obtained in connection with the finite element analysis.

can do.

All. Dongson

)을 계산하여 정격전류(

)와 해당풍속에서의 전류(

)의 제곱비로 구성된 동손의 g-fuction을 곱하여 전 운전영역에서의 동손(

)을 아래와 같이 구할 수 있다.
By using the loss calculation method suggested in the previous section,

Calculate the rated current (

) And the current at the wind speed (

By multiplying the g-fuction of the copper loss by the square ratio of),

) Can be obtained as

: 출력전류(정격 풍속시)here

: Output current (at rated wind speed)

: 출력전류(특정 풍속시)

: Output current (at specific wind speed)

la. Hysteresis

)을 계산하여 정격주파수(

)와 해당풍속에서의 주파수(

)의 비로 구성된 히스테리시스손의 g-fuction을 곱하여 전 운전영역에서의 히스테리시스손 (

)을 아래와 같이 구할 수 있다.
Hysteresis loss at nominal rating using the thread calculation method proposed in the previous section

) To calculate the rated frequency (

) And the frequency at that wind speed (

The hysteresis loss over the entire operating range by multiplying the g-fuction of the hysteresis loss

) Can be obtained as

: 주파수(정격 풍속시)here

: Frequency (at rated wind speed)

: 주파수(특정 풍속시)

: Frequency (at specific wind speed)

: 쇄교자속(정격 풍속시)

: Flux bridge flux (at rated wind speed)

: 쇄교자속(특정 풍속시)

: Fluctuation flux (at certain wind speed)

hemp. Eddy current loss

)을 계산하여 정격주파수()와 해당풍속에서의 주파수(

)의 제곱비로 구성된 와전류손의 g-fuction을 곱하여 전 운전영역에서의 와전류손(

)을 아래와 같이 구할 수 있다.
Eddy current loss at nominal rating using the loss calculation method proposed in the previous section.

Calculate the rated frequency () and the frequency at the corresponding wind speed (

By multiplying the g-fuction of the eddy current loss composed of the square ratio of

) Can be obtained as

: 주파수(정격 풍속시) here

: Frequency (at rated wind speed)

: 주파수(특정 풍속시)

: Frequency (at specific wind speed)

: 쇄교자속(정격 풍속시)

: Flux bridge flux (at rated wind speed)

: 쇄교자속(특정 풍속시)

: Fluctuation flux (at certain wind speed)

bar. Mechanical hand

)을 계산하여 마찰계수와 손실계수로 구성된 와전류손의 g-fuction을 곱하여 전 운전영역에서의 기계손(

)을 아래와 같이 구할 수 있다.
Machine loss at nominal rate using loss calculation method suggested in the previous section.

) And multiply the g-fuction of the eddy current loss, which consists of the friction coefficient and the loss coefficient,

) Can be obtained as

here

: 마찰계수(속도에 비례)

: Coefficient of friction (proportional to speed)

: 손실계수(속도에 세제곱에 비례)

= Loss factor (proportional to speed)

: 속도(정격 풍속시)

: Speed (at rated wind speed)

: 속도(특정 풍속시)

: Speed (at specific wind speed)

four. Total loss calculation

To calculate the output and efficiency of a wind turbine, it is necessary to calculate the total losses that take into account the entire operating area of the generator. When calculating the total loss, there is a problem that the loss calculation needs to be performed at each wind speed. To solve the problem, the total loss to calculate the annual maximum energy production using g-fuction is calculated as follows.

: 동손here

Dongson

: 히스테리시스손

Hysteresis

: 와전류손

: Eddy current loss

: 기계손

Machine hand

4. Annual energy output of wind power generation system ( AEP )

)을 통해서 예측된다. 특정 풍속

가 1년 동안 부는 시간과 연간 에너지 생산량을 구하는 식은 다음과 같다.
The annual energy output (AEP), which is selected as the objective function when optimizing a wind generator, is based on the probability of each wind speed and the output of the generator.

Is predicted through). Specific wind speed

The formula for calculating the time spent in a year and the annual energy output is:

는 365×12(=8760시간),

특정 풍속

가 1년간 부는 시간,

특정 풍속

의 확률here,

Is 365 × 12 (= 8760 hours),

Specific wind speed

A year blowing time,

Specific wind speed

Probability

일 때 연간 에너지 생산량은 아래의 수학식 17과 같다.
Wind speed

When the annual energy production is expressed by Equation 17 below.

Therefore, the total annual energy production by the entire wind speed range is expressed by Equation 18 below.

가 작을 수록 AEP의 계산은 정확하게 되나 최적화 시에 계산시간이 증가하기 때문에 절충해서 선정할 필요가 있다. 본 발명에서는 2.5[m/s]로 선정하였다.
In the present invention, the annual energy production (AEP total) was selected as the objective function for the optimal design of the generator, and through this, the maximum energy was obtained in the entire operating region of the wind speed. If you divide a lot of wind speed intervals,

The smaller the value is, the more accurate the calculation of the AEP is, but the calculation time increases during optimization. In the present invention, 2.5 [m / s] was selected.

Internet Distributed Computing and Optimization Techniques

Most existing wind turbine designs were designed based on the designer's experience. However, there are difficulties in ensuring the accuracy of analysis and designing for the purpose when carrying out the design. Therefore, in the present invention, in order to solve this problem, the wind turbine design was performed to satisfy the accuracy and purpose of the analysis by optimizing the response surface method when designing the wind generator.

In particular, in the present invention, the optimum design of the wind power generator was performed by converging the objective function according to the design variable in the optimal design problem of the wind power generator which generates excessive computational time in the magnetic field analysis. Hereinafter, the features of the Internet distributed computing method and the response surface method will be described.

1. Internet Distributed Computing

As shown in FIG. 3, data exchange between a main computer and a server computer is implemented through an internet web service. The parallel computation of the objective function using the internet distributed computing solves the problem of excessive computation time in the optimal design of the wind power generator using the finite element analysis method. For reference, in the present invention, 10 server computers are connected to the main computer to reduce the calculation time. Here, we propose a technique that combines the optimization algorithm with Internet distributed computing and discuss the process for applying it to real devices. 3 shows a combination of internet distributed computing and optimization.

The main components for combining Internet distributed computing and optimization algorithms are divided into three parts: MAIN, CLIENT, and SERVER. Specifically, the main is a part where the optimization program is performed and plays a role of generating design variables (x1 .... xn) during the optimal design of the power device. The client distributes design variables created in the main to each server. In addition, each server receives the distributed design variables and performs the finite element analysis of the characteristic of the power equipment.

After the characteristic analysis of the power equipment applying the finite element analysis, the result of the calculation, that is, the objective function (F (x1 .... xn)) is returned to the main through the client, and the main optimizes the optimal candidate solution through the optimization operation. Determine. This process is repeated until the optimal convergence condition is satisfied to derive the optimal solution.

2. Response surface method

Recently, as the design problem becomes more complex and the analysis time increases exponentially, the approximate optimal design method has started to attract attention. The approximate optimal design replaces the original problem with the approximate function problem and performs the optimal design using the transformed approximation function problem, thereby reducing the cost of analysis. The generation of the approximation function is largely divided into localapproximation and global approximation. Local approximation generates an approximation function near the current design point using sensitivity information. The approximation function is very accurate near the current design point, but the accuracy decreases rapidly as it moves away from the current design point. However, proper use of local approximation can significantly reduce the number of computational calculations compared to the optimal design without the approximation function, which has been used in many studies. On the other hand, global approximation generates an approximation function for a certain design area, which may be inaccurate for one design point, but has the advantage of showing the general tendency of the analysis to approximate the whole design area. In recent years, it has been actively studied.

4 is a flowchart illustrating a response surface analysis. First, the first-order prediction equation is determined, and then the fundamental satisfaction of the design is determined, and the next experiment is performed according to the satisfaction. Finally, depending on whether or not the final experimental goal is achieved, finish the design or consider experiments in the next step. As shown in the figure, the response surface equation is estimated more than two times.

end. Response surface method  concept

It is a method of mathematically and statistically analyzing a problem in which a response is caused by a plurality of variable actions, and a response surface of a change of the response when a plurality of design variables are influencing an objective function by performing a complex action. Statistical analysis method for.

In addition, the response surface method can estimate not only which factors affect the most, but also what combinations can have the greatest effect. In a typical experimental design, a measure of the effects of a combination of factors would give a response surface method to estimate the equation when a factor affects and the factors show the greatest effect, and that equation is the most The reaction surface method has the following characteristics in that it becomes a desired value.

(1) When there are several independent variables, find out which combination of independent variables will give the best response.

2) Determine if there is an interaction between independent variables.

3) The response surface cannot be made by the two-level batch method. In other words, more than 3 levels are used for the response surface.

4) Three or more factors may be performed with fewer experiments than the three-level complete batch method.

I. Response surface method  apply

The response surface method applied in the present invention performs optimization by experiment of 3 factor 3 level. At this time, the design variables are selected as the length, width, and width of the permanent magnet, and the response surface method is implemented with the objective function as the volume of the permanent magnet and the annual energy production.

Optimal Design Results of Permanent Magnet Wind Generators

1. Internet Distributed Computing Response surface method  Optimal Design Result

In order to perform accurate characteristics analysis and optimal design of generators, it is based on finite element method and response surface method and optimized for surface-mounted permanent magnet synchronous generator aiming to minimize the volume of permanent magnet and maximum annual energy output through parallel distributed computing. The design results are shown in Table 2. For reference, the objective function is presented by comparing the AEP model with the permanent magnet volume minimization model which is mainly applied to general design. The maximum AEP design model improved 2.2% over the previous model and 1.5% for the minimizing magnet volume in the annual energy output (1234.5MWh).

Comparison of results of the optimization model (rated wind velocity = 13.5 [m / s])
Objective function Design variables

AEP [MWh]
Per pole
Permanent Magnet Volume
[㎣]
Axial length
(X1)

Permanent Magnet Height
(X2)
Stimulation angle
(X3)
existing
Model

550
12.5
2.50
1193.4
17187.5
Magnetic volume
minimization

545
10.1
1.92
1218.3
10568.64
AEP
Maximize

548
12.2
2.3
1234.5
15376.88

5 is a magnetic volume minimization model of an optimally designed permanent magnet wind turbine and a magnetic flux density distribution at a rated speed of an AEP maximization model. As shown in Table 2, the magnet volume minimization model compared to an AEP maximization model has an axial length and height of a permanent magnet. However, due to the reduction of the angle, the density of the magnetic flux density distribution is different.

In FIG. 6, the efficiency characteristics according to the wind speed of each design model were compared. In particular, the AEP maximization model had the maximum efficiency (96.66%) near the high wind speed (6 [m / s]). Although the permanent magnet minimization model slightly outperforms the AEP maximization model only at rated wind speed, the efficiency of the AEP maximization model is improved compared to other models in all operating areas. In addition, the AEP maximized model can reduce the amount of magnets close to 11% of the existing model, resulting in maximum efficiency with minimum manufacturing cost. In particular, the present invention has a great meaning in that the ultimate purpose of the wind generator is to maximize the total output of the annual energy and to produce the production cost.

Preferred embodiments of the present invention described above are disclosed to solve the technical problem, and various modifications, changes, additions, etc. within the spirit and scope of the present invention may be made by those skilled in the art to which the present invention pertains. It will be possible, and such modifications and changes should be considered to be within the scope of the following claims.

Claims (2)

(a) In the rotor of the permanent magnet wind turbine, the results were calculated after selecting the design variables for the axial length, height, and magnetic pole angle of the permanent magnet, placing the operating efficiency and the area of the permanent magnet as the objective function, and performing the analysis. Selecting a step;
(b) inputting the selected result as an initial model to the entire processing and determining final design variables therethrough; and an optimum design method of the permanent magnet wind turbine using the response surface method.
The method of claim 1,
An optimal design method of a permanent magnet wind power generator using a response surface method characterized in that the object function is performed in parallel by dividing the population by the number of the main computer performing the optimization program and the plurality of server computers connected to the Internet.

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