CN105631152B - Variable speed variable frequency pneumatic electric system Wind energy extraction method based on particle cluster algorithm - Google Patents

Abstract

The invention discloses a kind of variable speed variable frequency pneumatic electric system maximal wind-energy capture method based on particle cluster algorithm, the variable speed variable frequency pneumatic electric system constituted for the permanent-magnet synchronous Wind turbines by variable frequency transformer and Duo Tai parallel runnings, collaboration optimization is carried out to the rotating speed of variable frequency transformer and the propeller pitch angle of all permanent-magnet synchronous Wind turbines by particle cluster algorithm, so as to realize the maximal wind-energy capture of variable speed variable frequency pneumatic electric system.Compared with for the single argument optimization method of variable frequency transformer rotating speed, the Multi-variables optimum design method based on particle cluster algorithm proposed can obtain more wind energies.

Description

Wind energy capturing method of variable-speed variable-frequency wind power system based on particle swarm optimization

Technical Field

The invention relates to the technical field of wind power generation, in particular to a wind energy capturing method of a variable-speed variable-frequency wind power system based on a particle swarm algorithm.

Background

Offshore wind power has the advantages of high wind speed, small turbulence intensity, stable wind speed and direction and the like, and is a main trend of the development of the wind power industry. However, the difficulty of offshore operation causes high maintenance cost of the offshore wind turbine, and the limitation of weather and marine environment makes it difficult to repair the wind turbine in time after the failure, which leads to reduction of effective power generation time, so that the reliability of the offshore wind turbine has a great influence on the economic benefit of the wind power plant.

The variable-speed variable-frequency wind power system formed by adopting the variable-frequency transformer to control a plurality of permanent magnet synchronous wind power sets running in parallel has the following advantages: firstly, a permanent magnet synchronous wind turbine is adopted at sea, no slip ring or electric brush is used, and the failure rate is low; secondly, a core device variable frequency transformer of the system is arranged on the land, so that the maintenance is convenient; active power mainly flows into a power grid through the variable frequency transformer, the capacity and the cost of the power electronic device are obviously reduced, and the overload capacity of the system is strong.

Aiming at the variable-speed variable-frequency wind power system, the existing maximum wind energy capturing method adopts a single variable optimization algorithm, calculates the optimal variable-frequency transformer rotating speed according to the wind speed of each wind turbine, and has low wind energy utilization rate. In order to improve the wind energy utilization rate, the rotating speed of the variable frequency transformer needs to be optimized, and simultaneously, the pitch angle of each wind turbine generator set needs to be optimized. However, there is currently no co-optimization method for variable frequency transformer speed and pitch angle of each wind turbine.

Disclosure of Invention

In view of the above defects in the prior art, the technical problem to be solved by the present invention is to provide a method for capturing wind energy of a variable speed and variable frequency wind power system based on a particle swarm algorithm, which can perform cooperative optimization on the pitch angle of each permanent magnet synchronous wind turbine generator and the rotation speed of a variable frequency transformer, so as to achieve maximum wind energy capture of the variable speed and variable frequency wind power system.

In order to achieve the above object, the present invention provides a maximum wind energy capturing method for a variable speed and variable frequency wind power system based on a particle swarm optimization, wherein the variable speed and variable frequency wind power system comprises a plurality of permanent magnet synchronous wind power sets and a variable frequency transformer connected with the permanent magnet synchronous wind power sets, the variable frequency transformer is connected in a power frequency grid, the variable frequency transformer comprises a double-fed motor and a direct current motor mechanically connected with the double-fed motor, the direct current motor is electrically connected with a driving circuit, and the driving circuit is connected with a transformer, and the method is characterized in that the method comprises the following steps:

step 1: the method comprises the steps of collecting real-time wind speeds of all permanent magnet synchronous wind turbine generators in a variable-speed variable-frequency wind power system and giving M-dimensional wind speed vectors

Step 2: randomly initializing a particle swarm, setting an initial position and a speed of each particle, wherein the particle swarm is composed of N particles, and the position of each particle in a multidimensional space is expressed as a vector of the following form:

wherein, β_i,jIs the pitch angle, omega, of the jth permanent magnet synchronous wind turbine generator in the ith particle_i,VFTThe rotating speed of the variable frequency transformer in the ith particle is obtained;

and step 3: the fitness value of each particle in the particle swarm is calculated according to the following formula:

wherein rho is air density, A is swept area of the blade, and the power coefficient C of the kth permanent magnet synchronous wind turbine generator_k(V_k,β_k,ω_VFT) The following were used:

wherein R is the blade radius of the permanent magnet synchronous wind turbine generator system, omega_gridFor synchronizing the angular speed, p, of the grid_WTAnd p_VFTThe number of pole pairs, K, of the permanent magnet synchronous wind turbine generator and the variable frequency transformer respectively₁To K₆Is a coefficient determined by the wing profile of the wind turbine;

and 4, step 4: initial position of each particleAs its historical best positionCalculating the fitness value of the particle swarm, selecting the particle with the maximum fitness value from the particle swarm as the current global optimal particle, and recording the position of the particle as the current global optimal particle

And 5: the velocity and position of each particle is updated according to

Wherein,c₁、c₂is a constant number r₁And r₂Is a random number;

step 6: if a particle is present in step 5One dimension of which exceeds the adjustment range of the pitch angle of the wind turbine or the rotational speed of the variable-frequency transformer, e.g.(k is 1,2, …, M) orIt is set at the boundary of the adjustment range;

and 7: calculating each updated particle by adopting the method of the step 3And compares it with its historical optimum positionAnd global historical optimal positionThe corresponding fitness value is compared, if the fitness value of the current particle is higher, the corresponding particle is replaced by the corresponding fitness valueOr

And 8: repeating the steps 5-7 until the iteration converges, and obtaining the globally optimal particles as follows:

β therein_g1,β_g2,…,β_gMFor the optimum pitch angle, omega, of each permanent magnet synchronous wind turbine_gVFTThe optimal rotating speed of the variable frequency transformer is obtained;

step 9, β is added_g1,β_g2,…,β_gMA pitch angle controller for the corresponding wind turbine_gVFTA rotation speed controller for the variable frequency transformer.

The invention has the beneficial effects that:

the invention improves the wind energy utilization efficiency of the variable speed variable frequency wind power system which is composed of the variable frequency transformer and a plurality of permanent magnet synchronous wind power units which are operated in parallel by the cooperative optimization of the pitch angle of the wind power unit and the rotating speed of the variable frequency transformer.

The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.

Drawings

FIG. 1 is a topological structure diagram of a variable speed and variable frequency wind power system composed of a variable frequency transformer and a plurality of permanent magnet synchronous wind power sets running in parallel, which is suitable for the invention;

FIG. 2 is a set of time varying wind velocity plots used to verify the effectiveness of the control method of the present invention;

FIG. 3 is a graph showing the convergence of the optimal fitness value of a particle swarm under the control method of the present invention;

FIG. 4 is a pitch angle optimization result diagram of each permanent magnet synchronous wind turbine generator set under the control method of the invention;

FIG. 5 is a graph comparing the optimized results of the variable frequency transformer rotating speed under the control method and the single variable optimizing method of the present invention;

FIG. 6 is a comparison graph of the output power of the variable speed and variable frequency wind power system under the control method and the single variable optimization method of the present invention.

Detailed Description

As shown in fig. 1, a method for capturing maximum wind energy of a variable-speed variable-frequency wind power system based on a particle swarm optimization, where the variable-speed variable-frequency wind power system includes multiple permanent magnet synchronous wind power sets and a variable-frequency transformer connected to the multiple permanent magnet synchronous wind power sets, the variable-frequency transformer is connected to a power frequency grid, the variable-frequency transformer includes a double-fed motor and a direct current motor mechanically connected to the double-fed motor, the direct current motor is electrically connected to a driving circuit, and the driving circuit is connected to a transformer, where the method includes the following steps:

step 1: the method comprises the steps of collecting real-time wind speeds of all permanent magnet synchronous wind turbine generators in a variable-speed variable-frequency wind power system and giving M-dimensional wind speed vectors

Step 2: randomly initializing a particle swarm, setting an initial position and a speed of each particle, wherein the particle swarm is composed of N particles, and the position of each particle in a multidimensional space is expressed as a vector of the following form:

in this embodiment, β_i,jIs the pitch angle, omega, of the jth permanent magnet synchronous wind turbine generator in the ith particle_i,VFTThe rotating speed of the variable frequency transformer in the ith particle is obtained;

and step 3: the fitness value of each particle in the particle swarm is calculated according to the following formula:

in this embodiment, ρ is the air density, a is the swept area of the blade, and the power coefficient C of the kth permanent magnet synchronous wind turbine generator_k(V_k,β_k,ω_VFT) The following were used:

in this embodiment, R is the blade radius of the permanent magnet synchronous wind turbine generator, ω_gridFor synchronizing the angular speed, p, of the grid_WTAnd p_VFTThe number of pole pairs, K, of the permanent magnet synchronous wind turbine generator and the variable frequency transformer respectively₁To K₆Is a coefficient determined by the wing profile of the wind turbine;

and 4, step 4: initial position of each particleAs its historical best positionCalculating the fitness value of the particle swarm, selecting the particle with the maximum fitness value from the particle swarm as the current global optimal particle, and recording the position of the particle as the current global optimal particle

And 5: the velocity and position of each particle is updated according to

In the present embodiment, the first and second electrodes are,c₁、c₂is a constant number r₁And r₂Is a random number;

step 6: if a particle is present in step 5One dimension of which exceeds the adjustment range of the pitch angle of the wind turbine or the rotational speed of the variable-frequency transformer, e.g.(k is 1,2, …, M) orIt is set at the boundary of the adjustment range;

and 7: calculating each updated particle by adopting the method of the step 3And compares it with its historical optimum positionAnd global historical optimal positionThe corresponding fitness value is compared, if the fitness value of the current particle is higher, the corresponding particle is replaced by the corresponding fitness valueOr

And 8: repeating the steps 5-7 until the iteration converges, and obtaining the globally optimal particles as follows:

in this embodiment, β_g1,β_g2,…,β_gMFor the optimum pitch angle, omega, of each permanent magnet synchronous wind turbine_gVFTThe optimal rotating speed of the variable frequency transformer is obtained;

step 9, β is added_g1,β_g2,…,β_gMA pitch angle controller for the corresponding wind turbine_gVFTA rotation speed controller for the variable frequency transformer.

For the above conditions, simulation verification is performed at the wind speed shown in FIG. 2, and the simulation waveforms are shown in FIGS. 3 to 6. from FIG. 3, it can be seen that the particle swarm optimization can be converged quickly, ensuring that the controller obtains the optimum pitch angle β in a short time_g1,β_g2,β_g3,β_g4And the optimal rotation speed omega of the variable frequency transformer_gVFTOptimum pitch angle β_g1,β_g2,β_g3,β_g4As shown in FIG. 4, the result ω of the optimization of the rotation speed of the variable frequency transformer_gVFTAs shown in fig. 5; as can be seen from fig. 6, compared with the univariate optimization method for the rotating speed of the variable frequency transformer, the multivariate optimization method based on the particle swarm can obviously improve the wind energy utilization efficiency under various wind speed conditions.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (1)

1. The method for capturing the maximum wind energy of the variable-speed variable-frequency wind power system based on the particle swarm optimization comprises a plurality of permanent magnet synchronous wind power sets and variable-frequency transformers connected with the permanent magnet synchronous wind power sets, wherein each variable-frequency transformer is connected in a power frequency power grid and comprises a double-fed motor, a transformer, a driving circuit and a direct current motor mechanically connected with the double-fed motor, the direct current motor is electrically connected with the driving circuit, and the driving circuit is connected with the transformer, and the method is characterized by comprising the following steps of:

step 1: collectingThe real-time wind speed of each permanent magnet synchronous wind turbine generator in the variable-speed variable-frequency wind power system is assigned to an M-dimensional wind speed vector

Step 2: randomly initializing a particle swarm, setting an initial position and a speed of each particle, wherein the particle swarm is composed of N particles, and the position of each particle in a multidimensional space is expressed as a vector of the following form:

${\stackrel{\→}{x}}_i={\[{\mathrm{\β}}_{i,1},{\mathrm{\β}}_{i,2},...,{\mathrm{\β}}_{i,M},{\mathrm{\ω}}_{i,VFT}\]}^T,(i=1,2,...,N)$

wherein, β_i,jIs the pitch angle, omega, of the jth permanent magnet synchronous wind turbine generator in the ith particle_i,VFTFor variable frequency transformation in the ith particleThe rotation speed of the device;

and step 3: the fitness value of each particle in the particle swarm is calculated according to the following formula:

$P_i=\frac{1}{2}\mathrm{\ρ}A\underset{k=1}{\overset{M}{\mathrm{\Σ}}}\[C_k(V_k,{\mathrm{\β}}_{i,k},{\mathrm{\ω}}_{i,VFT}){V_k}^3\],(i=1,2,...,N)$

wherein rho is air density, A is swept area of the blade, and the power coefficient C of the kth permanent magnet synchronous wind turbine generator_k(V_k,β_k,ω_VFT) The following were used:

$\begin{array}{c}{C}_k(V_k,{\mathrm{\β}}_k,{\mathrm{\ω}}_{VFT})=K_6\frac{{\mathrm{\ω}}_{grid}-p_{VFT}{\mathrm{\ω}}_{VFT}}{p_{WT}}\frac{R}{V_k}\\ +K_1(\frac{K_2}{\frac{{\mathrm{\ω}}_{grid}-p_{VFT}{\mathrm{\ω}}_{VFT}}{p_{WT}}\frac{R}{V_k}+0.08{\mathrm{\β}}_k}-\frac{0.035K_2}{{{\mathrm{\β}}_k}^3+1}-K_3{\mathrm{\β}}_k-K_4)e^{\frac{-K_5}{\frac{{\mathrm{\ω}}_{grid}-p_{VFT}{\mathrm{\ω}}_{VFT}}{p_{WT}}\frac{R}{V_k}+0.008{\mathrm{\β}}_k}+\frac{0.035K_5}{{{\mathrm{\β}}_k}^3+1}}\end{array}$

wherein R is the blade radius of the permanent magnet synchronous wind turbine generator system, omega_gridFor synchronizing the angular speed, p, of the grid_WTAnd p_VFTThe number of pole pairs, K, of the permanent magnet synchronous wind turbine generator and the variable frequency transformer respectively₁To K₆Is a coefficient determined by the wing profile of the wind turbine;

and 4, step 4: initial position of each particleAs its historical best positionCalculating the fitness value of the particle swarm, selecting the particle with the maximum fitness value from the particle swarm as the current global optimal particle, and recording the position of the particle as the current global optimal particle

And 5: the velocity and position of each particle is updated according to

Wherein,c₁、c₂is a constant number r₁And r₂Is a random number;

step 6: if a particle is present in step 5One dimension of which exceeds the adjustment range of the pitch angle of the wind turbine or the rotational speed of the variable-frequency transformer, e.g.OrIt is set at the boundary of the adjustment range;

and 7: calculating each updated particle by adopting the method of the step 3And compares it with its historical optimum positionAnd global historical optimal positionThe corresponding fitness value is compared, if the fitness value of the current particle is higher, the corresponding particle is replaced by the corresponding fitness valueOr

And 8: repeating the steps 5-7 until the iteration converges, and obtaining the globally optimal particles as follows:

${\stackrel{\→}{p}}_g={\[{\mathrm{\β}}_{g1},{\mathrm{\β}}_{g2},...,{\mathrm{\β}}_{gM},{\mathrm{\ω}}_{gVFT}\]}^T$

β therein_g1,β_g2,…,β_gMFor the optimum pitch angle, omega, of each permanent magnet synchronous wind turbine_gVFTThe optimal rotating speed of the variable frequency transformer is obtained;

step 9, β is added_g1,β_g2,…,β_gMA pitch angle controller for the corresponding wind turbine_gVFTIs given toA rotating speed controller of the variable frequency transformer.