Flexible power optimization method based on wind farm equipment

Abstract

This invention discloses a flexible power optimization method based on wind farm equipment, comprising: acquiring the operating constraint factors and operating sequence of the wind turbine to construct an optimization model; solving the optimization model using an iterative algorithm to obtain an optimal parameter set; and sending the optimal parameter set to the wind turbine management system in the form of instructions for adaptive parameter adjustment of the wind turbine: when the rated power of the wind turbine exceeds a set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and gearbox in the wind turbine through instructions to reduce the power generation to a steady state; when the rated power of the wind turbine is lower than the set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and gearbox in the wind turbine through instructions to increase the power generation to a steady state. This invention effectively reduces the power loss of wind turbines caused by wind dynamics, achieving low energy consumption and energy saving.

Description

Technical Field

[0001] This invention relates to the technical fields of new energy and power optimization, and in particular to a flexible power optimization method based on wind farm equipment. Background Art

[0002] With the increasing severity of the energy crisis, wind power, as a clean and renewable new energy source, has received widespread attention and research. However, in practical applications, wind power also has some problems, such as unstable wind speed and noise pollution. Due to changes in meteorological conditions, wind speed will sometimes increase and sometimes decrease, resulting in unstable wind turbine speed, which in turn affects the fluctuating output power of the generator and thus affects the power generation efficiency. Due to the air vibration and mechanical friction generated when the turbine rotates, the wind turbine will generate a lot of noise, which will have a certain impact on the surrounding environment and human health.

[0003] Wind farm equipment mainly consists of three parts: a wind turbine, a gearbox, and a generator. The wind turbine is the core component of the wind farm equipment; it is a rotor driven by wind energy. Its power output is greatly affected by the wind and airflow. Because the rotational speed of the mechanical energy generated by the wind turbine is inconsistent with the frequency of the AC power required by the power grid, a gearbox is needed to regulate the speed. The gearbox is an important part of the wind farm equipment, enabling the conversion between the rotational speed output by the wind turbine and the rotational speed required by the generator. The generator is an important device for converting mechanical energy into electrical energy. In a wind farm, due to the limitations of the wind turbine and gearbox, the rotational speed and torque received by the generator will change; therefore, the generator needs to have a certain degree of adaptability. Through the cooperation of the wind turbine, gearbox, and generator, the purpose of converting wind energy into electrical energy is achieved.

[0004] Flexible DC transmission technology has strong technical advantages in large-scale wind farm grid connection. In recent years, transmitting electricity from different locations through long-distance, multi-terminal high-voltage direct current (MTDC) systems has become a trend in offshore wind farm grid integration. However, with the adoption of MTDC systems, the penetration rate of wind power generation is constantly increasing, which brings challenges to grid operation, such as low inertia and transient stability issues. In the event of a fault on the DC side, due to the uncontrollable diode path in the converter of the flexible DC transmission system, the flexible DC transmission system cannot block the fault current during a short-circuit fault on the DC side. After the fault occurs, the fault can only be cleared by disconnecting the AC side circuit breaker, which results in significant switching losses.

[0005] Flexible DC transmission technology can be used to centralize multiple wind turbines in offshore wind farms to form a large-scale wind power collection and transmission station, reducing voltage loss and interconnecting the DC wind farm with the AC power grid. This allows wind energy to be transmitted to load centers to provide stable power and solve the problem of unstable wind power generation caused by wind speed fluctuations. However, in practical applications, current flexible DC transmission technology still cannot completely avoid the power loss problem of wind turbines, which is a problem that continues to be solved in the development and research of wind farm equipment. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: obtaining the operating constraint factors and operating sequence of wind turbine units to construct an optimization model;

[0008] The optimal parameter set is obtained by solving the optimization model using an iterative algorithm.

[0009] The optimal parameter set is sent to the wind turbine management system in the form of instructions for operation, and the wind turbine parameters are adaptively adjusted:

[0010] When the rated power of the wind turbine exceeds the set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and the gearbox in the wind turbine through instructions, so as to reduce the power generation to a steady state.

[0011] When the rated power of the wind turbine is lower than the set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and the gearbox in the wind turbine through instructions, so as to improve the power generation to a steady state.

[0012] As a preferred embodiment of the flexible power optimization method based on wind farm equipment described in this invention, before obtaining the operating constraint factor and operating sequence of the wind turbine, it is necessary to collect historical operating data of the wind turbine and preprocess it to obtain a sample dataset. The operating constraint factor and operating sequence are obtained by screening the sample dataset.

[0013] As a preferred embodiment of the flexible power optimization method based on wind farm equipment described in this invention, the preprocessing includes:

[0014] Filter out duplicate, useless, and irrelevant parameters from the historical operating data of the wind turbine units;

[0015] The filtered information parameters are converted into unified dummy variables using an encoding strategy;

[0016] The dummy variable is standardized by subtracting its mean and dividing by its standard deviation, so that the different configuration values ​​of the parameter are replaced by mathematical values, forming a numerical parameter;

[0017] The set of numerical parameters is the sample dataset.

[0018] As a preferred embodiment of the flexible power optimization method based on wind farm equipment described in this invention, the operating constraint factors include power balance constraints, energy storage output constraints, and supply-demand balance constraints.

[0019] The runtime sequence includes active power output sequence, load sequence, and voltage sequence.

[0020] As a preferred embodiment of the flexible power optimization method based on wind farm equipment described in this invention, solving the optimization model includes:

[0021] Select initial point X 0 k = 0;

[0022] Find a suitable direction P k P k This represents the search direction at step k+1.

[0023] Calculate along P k Step length γ in the direction of movement k A new point X is obtained. k+1 ;

[0024] X k+1 =X k +γ k P k Verify X k+1 Is this the optimal solution? If so, the iteration ends.

[0025] If not, return to the search direction P. k Repeat the steps iteratively until the optimal solution is found;

[0026] The process involves iteratively generating a point sequence {X(k)} that gradually approaches the optimal point.

[0027] As a preferred embodiment of the flexible power optimization method based on wind farm equipment described in this invention, the rated power of the wind turbine is related to the wind turbine speed, the number and length of blades, the wind turbine area, wind direction, and climatic conditions, and its mathematical calculation formula is as follows:

[0028] Rated power of wind turbine = 0.5 × S × F 3 ×n

[0029] Where S is the wind shear area, F is the wind speed, and n is a constant.

[0030] The beneficial effects of the present invention are as follows: By obtaining the optimal parameter set for the operation of wind turbine equipment, the present invention can adaptively adjust and control the parameters during equipment operation, effectively reducing the power loss of wind turbine equipment caused by wind dynamics, and achieving low energy consumption and energy saving. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0032] Figure 1 This is a flowchart illustrating the flexible power optimization method based on wind farm equipment as shown in this invention. Detailed Implementation

[0033] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0034] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this invention.

[0035] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0036] Example 1

[0037] According to an embodiment of the present invention, in combination Figure 1 The flowchart shown illustrates a flexible power optimization method based on wind farm equipment, which specifically includes the following steps:

[0038] S1: Obtain the operating constraint factors and operating sequence of the wind turbine to build an optimization model. It should be noted that before obtaining the operating constraint factors and operating sequence of the wind turbine, historical operating data of the wind turbine needs to be collected and preprocessed to obtain a sample dataset. The operating constraint factors and operating sequence are then obtained by filtering the sample dataset.

[0039] Further preprocessing includes:

[0040] Remove duplicate, useless, and irrelevant parameters from the historical operating data of wind turbine units;

[0041] The filtered information parameters are converted into unified dummy variables using an encoding strategy;

[0042] Standardize dummy variables, subtract their mean and divide by their standard deviation, so that different configuration values ​​of the parameters are replaced by mathematical values, forming numerical parameters;

[0043] The set of numerical parameters constitutes the sample dataset.

[0044] As an example, operational constraints include power balance constraints, energy storage output constraints, and supply-demand balance constraints.

[0045] As an example, the runtime timing includes active power output timing, load timing, and voltage timing.

[0046] As an example, the rated power of a wind turbine is related to the turbine speed, the number and length of blades, the turbine area, wind direction, and climatic conditions. The mathematical formula for its calculation is as follows:

[0047] Rated power of wind turbine = 0.5 × S × F 3 ×n

[0048] Where S is the wind shear area, F is the wind speed, and n is a constant.

[0049] S2: Solve the optimization model using an iterative algorithm to obtain the optimal parameter set. It should be noted in this step that the optimization model constructs an objective function with the goal of minimizing power loss, and its mathematical expression is as follows:

[0050]

[0051] Where, ||r|| is the L2 norm of r, r is the weight vector, a is the runtime order matrix, i = 1, 2, 3, k is a constant, and e k λ is the operating constraint factor, and λ is the penalty coefficient with λ > 0.

[0052] As an example, when i = 1, a i To select the active power output timing matrix.

[0053] As an example, when i = 2, a i To select the load time series matrix.

[0054] As an example, when i = 3, a i To select the voltage timing.

[0055] Specifically, solving the optimization model includes:

[0056] (1) Select the initial point X 0 k = 0;

[0057] (2) Find a suitable direction P k P k This represents the search direction at step k+1.

[0058] (3) Calculate along P k Step length γ in the direction of movement k A new point X is obtained. k+1 ;

[0059] (4)X k+1 =X k +γ k P k Verify X k+1 Is this the optimal solution? If so, the iteration ends.

[0060] (5) If not, return to step (2) and iterate again until the optimal solution is found;

[0061] The process involves iteratively generating a point sequence {X(k)} that gradually approaches the optimal point.

[0062] It should be noted that the embodiments of the present invention solve the optimization model in an iterative manner, which improves the accuracy and efficiency of solving the optimization model while avoiding the complex computation of genetic algorithms. In this embodiment, new points are obtained by calculating the step size of the search direction, and then replaced in the best way, and each point is identified one by one to further reduce the impact of errors.

[0063] It should also be noted that the letters involved in steps (1) to (5) above are for illustrative purposes only and have no special meaning. They can also be replaced by other letters or expressions.

[0064] S3: Send the optimal parameter set to the wind turbine management system as a command for adaptive parameter adjustment of the wind turbine. Note that this step also requires further explanation:

[0065] When the rated power of the wind turbine exceeds the set threshold range, the optimal parameter set corrects the speed of the wind turbine and gearbox in the wind turbine through instructions to reduce the power generation to a steady state.

[0066] When the rated power of the wind turbine is lower than the set threshold range, the optimal parameter set corrects the speed of the wind turbine and the gearbox in the wind turbine through instructions in order to improve the power generation to a steady state.

[0067] It should also be noted in this embodiment that the above threshold range can be adaptively set according to the actual needs on site, and the embodiment of the present invention does not impose a unique range limitation with a fixed value.

[0068] Preferably, in this embodiment of the invention, by limiting the operating constraint factors and the operating sequence, the optimal parameter set for the operation of the wind turbine is obtained with the goal of minimizing the operating power consumption of the wind turbine. This allows for adaptive parameter adjustment of the operating system, solving the problem of excessive power loss caused by wind speed instability due to wind dynamics.

[0069] The aforementioned active power output timing matrix, load timing matrix, and voltage timing matrix can all be generated using existing technologies (computers), and will not be elaborated upon in this implementation.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A flexible power optimization method based on wind farm equipment, characterized in that, include: Obtain the operating constraint factors and operating sequence of the wind turbine to construct an optimization model; The operational constraint factors include power balance constraints, energy storage output constraints, and supply-demand balance constraints. The runtime sequence includes active power output sequence, load sequence, and voltage sequence; The optimal parameter set is obtained by solving the optimization model using an iterative algorithm. The optimization model constructs an objective function with the goal of minimizing power loss, and its mathematical expression is as follows: in, Let r be the L2 norm of r, r be the weight vector, a be the runtime order matrix, i = 1, 2, 3, and k be a constant. For the running constraint factor, The penalty coefficient and >0; When i=1, To select the active power output timing matrix; When i=2 To select the load time series matrix; When i=3 To select the voltage timing; Solving the optimization model includes: Select initial point k=0; Find a suitable direction , This represents the search direction at step k+1. Calculate along Step length in the direction of movement , obtain new points ; = ,check Is this the optimal solution? If so, the iteration ends. If not, return to the search direction. Repeat the steps iteratively until the optimal solution is found; Here, point sequences are generated through iteration. To gradually approach the optimal point; The optimal parameter set is sent to the wind turbine management system in the form of instructions for operation, and the wind turbine parameters are adaptively adjusted: When the rated power of the wind turbine exceeds the set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and the gearbox in the wind turbine through instructions, so as to reduce the power generation to a steady state. When the rated power of the wind turbine is lower than the set threshold range, the optimal parameter set corrects the rotational speed of the wind turbine and the gearbox in the wind turbine through instructions, so as to improve the power generation to a steady state.

2. The flexible power optimization method based on wind farm equipment according to claim 1, characterized in that, Before obtaining the operating constraint factors and operating sequence of the wind turbine, it is necessary to collect historical operating data of the wind turbine and preprocess it to obtain a sample dataset. The operating constraint factors and operating sequence are obtained by screening the sample dataset.

3. The flexible power optimization method based on wind farm equipment according to claim 2, characterized in that, The preprocessing includes: Filter out duplicate, useless, and irrelevant parameters from the historical operating data of the wind turbine units; The filtered information parameters are converted into unified dummy variables using an encoding strategy; The dummy variable is standardized by subtracting its mean and dividing by its standard deviation, so that the different configuration values ​​of the parameter are replaced by mathematical values, forming a numerical parameter; The set of numerical parameters is the sample dataset.

4. The flexible power optimization method based on wind farm equipment according to claim 1, characterized in that, The rated power of the wind turbine is related to the turbine speed, the number and length of blades, the turbine area, wind direction, and climatic conditions. The mathematical calculation formula is as follows: The rated power of the wind turbine is 0.

5. S n Where S is the wind shear area, F is the wind speed, and n is a constant.

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