Wind power plant remote control system

Abstract

The invention provides a wind power plant remote control system, and relates to the technical field of wind power generation, and the system comprises a sensor network module which is used for collecting the operation state data of each wind turbine generator in real time; the communication module is used for packaging and transmitting the currently collected running state data; the central analysis platform is used for predicting the total output power of the wind power plant, analyzing the optimal pitch angle of each wind turbine generator and predicting the wind energy utilization rate of the wind power plant; the remote control terminal is used for displaying the prediction information to a manager and remotely controlling the action of the wind turbine generator based on the optimal pitch angle; by analyzing and predicting the optimal pitch angle, the total output power and the wind energy utilization rate, the action of the wind turbine generator is remotely controlled, accurate and efficient monitoring and control over the wind power plant are achieved, the wind energy utilization rate and the power generation efficiency are improved, and the power generation cost is reduced. The problems that in the prior art, monitoring data are not comprehensive and not accurate, and a control strategy lacks accuracy and adaptability are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a remote control system for a wind farm. Background Art

[0002] With the growing global demand for clean energy and the emphasis on environmental protection, wind power generation has developed rapidly as a renewable and pollution-free way of obtaining energy. From the early small wind turbines to today's large wind farms, the scale and technical level have made a qualitative leap. In the design and manufacturing of wind turbines, the diameter of the wind rotor is constantly increasing, and the blade material and design are constantly optimized, which improves the efficiency of wind energy capture. At the same time, the generator technology is also constantly improving, from the traditional asynchronous generator to the application of new generators such as direct-drive permanent magnet generators, which improves the power generation efficiency and reliability.

[0003] In terms of wind farm layout and management, through detailed evaluation and analysis of wind energy resources, the site selection of wind farms is more scientific and reasonable to obtain more stable and efficient wind energy resources; and with the integration of information technology and automation technology, the monitoring and management of wind farms has gradually become intelligent and remote;

[0004] However, some existing wind farm monitoring systems only monitor some key parameters, and are not comprehensive enough to monitor some parameters that have a significant impact on the performance and life of wind turbines. At the same time, traditional wind turbine control strategies are often based on fixed parameters and models, and do not fully consider the complex and changeable environmental factors and the dynamic characteristics of the wind turbines themselves in the actual operation of wind farms. When a wind turbine in a wind farm fails or its performance deteriorates, the existing control strategies are difficult to quickly and effectively identify the faulty unit, which can easily lead to a reduction in the power generation efficiency of the entire wind farm.

[0005] Therefore, there is an urgent need in the art for a wind farm remote control system to solve the above problems. Summary of the invention

[0006] The present invention provides a remote control system for a wind farm, which can remotely control the operation of wind turbines by analyzing and predicting the optimal pitch angle, total output power and wind energy utilization rate, thereby realizing accurate and efficient monitoring and control of the wind farm, improving wind energy utilization rate and power generation efficiency, and solving the problems in the prior art of incomplete and inaccurate monitoring data and lack of accuracy and adaptability of control strategies.

[0007] The present invention provides a wind farm remote control system, comprising:

[0008] The sensor network module is installed in key locations of each wind turbine in the wind farm to collect the operating status data of each wind turbine in real time;

[0009] A communication module, which is wirelessly connected to the sensor network module and is used to package and transmit the currently collected operating status data;

[0010] A central analysis platform, which is connected to the communication module and receives the current operating status data of each wind turbine, is used to predict the total output power of the wind farm, analyze the optimal pitch angle of each wind turbine, and predict the wind energy utilization rate of the wind farm;

[0011] A remote control terminal is connected to the central analysis platform and the wind turbine control terminal, and is used to display the prediction information to the manager and remotely control the action of the wind turbine based on the optimal pitch angle.

[0012] According to a wind farm remote control system provided by the present invention, the sensor network module comprises:

[0013] A wind rotor sensor unit is provided at the wind rotor of each wind turbine generator set and is used to obtain the current wind rotor speed, wind rotor radius and pitch angle of each wind turbine generator set in real time;

[0014] An environmental sensing unit is provided on the top of the cabin of each wind turbine generator set and is used to obtain the humidity data, air pressure data, air density data and wind speed data of the environment in which each wind turbine generator set is currently located in real time;

[0015] The summary front-end unit is connected to the wind wheel sensor unit and the environment sensor unit, and is used to code the corresponding parameters according to the wind turbine group number to which each sensor unit belongs, and generate operating status data carrying each identification code.

[0016] According to a wind farm remote control system provided by the present invention, the communication module comprises:

[0017] A data packaging unit, which is connected to the summary front-end unit and is used to sort out the current operating status data according to the identification codes carried by them, and generate a data set corresponding to each wind turbine group;

[0018] A data transmission unit is connected to the data packaging unit and is used to send each data set to the central analysis platform through wireless transmission technology.

[0019] According to a wind farm remote control system provided by the present invention, the central analysis platform comprises:

[0020] a total output power prediction unit, which is connected to the data transmission unit and is used to analyze the tip speed ratio of each wind turbine according to the parameters in the data set, predict the output power of the wind turbine based on the tip speed ratio, and summarize the output power of each wind turbine to obtain a total output power prediction result of the wind farm;

[0021] An optimal pitch angle analysis unit, connected to the data transmission unit, for calculating the optimal pitch angle of each wind turbine according to the parameters in the data set and a preset pitch angle analysis model;

[0022] A wind energy utilization rate prediction unit, which is connected to the data transmission unit and the total output power prediction unit, and calculates a wind energy utilization rate prediction result of the current wind farm based on a wind energy utilization rate prediction model;

[0023] A summary output unit is connected to the total output power prediction unit, the optimal pitch angle analysis unit and the wind energy utilization prediction unit, and is used to summarize the total output power prediction results and wind energy utilization prediction results of the wind farm into prediction information, and convert the optimal pitch angle of each wind turbine group into a corresponding control instruction.

[0024] According to a wind farm remote control system provided by the present invention, the prediction process of the total output power of the wind farm specifically includes:

[0025] Calculate the tip speed ratio λ of each wind turbine i ; The calculation formula is:

[0026]

[0027] Among them, λ i is the current tip speed ratio of the i-th wind turbine, ω r and R are the rotor speed and rotor radius corresponding to the i-th wind turbine, respectively, and V is the wind speed data;

[0028] Calculate the output power prediction value P of each wind turbine i ; The calculation formula is:

[0029] P i =0.5*ρ*A*V 3 *C p (λ, β, α, θ)

[0030] Among them, P i is the predicted output power of the i-th wind turbine; ρ is the air density; A is the rotor swept area of ​​the i-th wind turbine, which is determined based on the rotor radius R; C p (λ, β, α, θ) is the wind energy utilization coefficient, and its calculation formula is:

[0031]

[0032] in, is the maximum coefficient corresponding to the Betz limit case; λ i is the current tip speed ratio of the i-th wind turbine, λ0 is the historical lower limit of the tip speed ratio of the i-th wind turbine, and λ ris the historical limit of the tip speed ratio of the i-th wind turbine; β i is the current pitch angle of the i-th wind turbine, β0 is the historical lower limit of the pitch angle of the i-th wind turbine, β r is the historical limit of the pitch angle of the i-th wind turbine; α i is the current humidity data of the environment where the i-th wind turbine is located, α0 is the lower limit of the historical humidity data of the environment where the i-th wind turbine is located, and α r is the upper limit of the historical humidity data of the environment where the i-th wind turbine is located; θ i is the current air pressure data of the environment where the i-th wind turbine is located, θ0 is the lower limit of the historical air pressure data of the environment where the i-th wind turbine is located, θ r is the upper limit of the historical air pressure data of the environment where the i-th wind turbine is located;

[0033] The output power prediction value P of each wind turbine i Summarize and combine historical humidity, air pressure and aging coefficient to calculate the total output power forecast result P of the wind farm total ; The calculation formula is:

[0034]

[0035] Among them, P total is the total output power prediction result of the current wind farm; n is the number of wind turbines; θ i0 is the initial air pressure data of the environment where the i-th wind turbine is located, α i0 is the initial humidity data of the environment where the i-th wind turbine is located; the initial air pressure data θ i0 and initial humidity data α i0 Determined by the records during the installation of the wind turbine; ∈ is the equipment aging coefficient, which is set based on the operating time and maintenance records of the wind turbine.

[0036] According to a wind farm remote control system provided by the present invention, the pitch angle analysis model is:

[0037]

[0038] in, is the optimal pitch angle of the i-th wind turbine; i is the current tip speed ratio of the i-th wind turbine, ω r is the rotor speed corresponding to the i-th wind turbine, V is the wind speed data, α i is the current humidity data of the environment where the i-th wind turbine is located.

[0039] According to a wind farm remote control system provided by the present invention, the wind energy utilization rate prediction model is:

[0040]

[0041] Where, η is the wind energy utilization rate of the current wind farm; P total is the total output power prediction result of the current wind farm; ρ is the air density, V is the wind speed data, A total is the total area of ​​the wind farm.

[0042] According to a wind farm remote control system provided by the present invention, the remote control terminal comprises:

[0043] A display unit connected to the summary output unit, used to display the current forecast information on a display device, and to combine the historical forecast information and the current forecast information to form a trend chart;

[0044] The signal sending unit is connected to the summary output unit and is used to convert the generated control instruction into a control signal to control the adjustment action of the wind turbine control end so that the current pitch angle of the wind turbine reaches the optimal pitch angle.

[0045] Compared with the prior art, the beneficial effects of this application are:

[0046] This application comprehensively collects data to accurately reflect the operating status of the wind turbine set, and dynamically adjusts the control parameters according to the real-time operating status and environmental conditions of the wind turbine set, so that the wind turbine set can maintain the optimal operating state under different working conditions, thereby improving the wind energy utilization rate and power generation efficiency; when a wind turbine set in a wind farm fails or its performance degrades, the wind energy utilization rate can be quickly used as an evaluation indicator of the operating status of the entire wind farm, providing a basis for adjusting the control parameters of other wind turbine sets to optimize the power generation efficiency of the entire wind farm.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 It is a structural schematic diagram of a wind farm remote control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] Embodiment 1:

[0053] The present invention provides a remote control system for a wind farm. Figure 1 ,include:

[0054] The sensor network module is installed in key locations of each wind turbine in the wind farm to collect the operating status data of each wind turbine in real time;

[0055] A communication module, which is wirelessly connected to the sensor network module and is used to package and transmit the currently collected operating status data;

[0056] A central analysis platform, which is connected to the communication module and receives the current operating status data of each wind turbine, is used to predict the total output power of the wind farm, analyze the optimal pitch angle of each wind turbine, and predict the wind energy utilization rate of the wind farm;

[0057] The remote control terminal is connected to the central analysis platform and the wind turbine control terminal to display the forecast information to the manager and remotely control the operation of the wind turbine based on the optimal pitch angle.

[0058] The principle of the above embodiment is that the sensor network modules are distributed in the key parts of each wind turbine in the wind farm, and can accurately and real-time collect the operating status data of each wind turbine. The communication module receives these operating status data through wireless connection and packages them for transmission to ensure that the data can be transmitted quickly. The central analysis platform receives the data from the communication module and processes the data using advanced analysis algorithms to predict the total output power of the wind farm. This is achieved by integrating the operating status data of each wind turbine; at the same time, the optimal pitch angle of each wind turbine is analyzed, which is based on a specific analysis model and data characteristics; it can also predict the wind energy utilization rate of the wind farm, which is also calculated based on the overall operating data and related physical principles. On the one hand, the remote control terminal displays the information predicted by the central analysis platform to the manager, so that the manager can clearly understand the operating status and future trends of the wind farm; on the other hand, the action of the wind turbine is remotely controlled according to the optimal pitch angle, so that the wind turbine can operate according to the optimized pitch angle to achieve the purpose of improving power generation efficiency and optimizing wind energy utilization.

[0059] The beneficial effects of the above-mentioned embodiments are: avoiding the lags and errors that may exist in traditional monitoring methods, and providing a reliable data basis for subsequent analysis and control; helping managers to plan and arrange production in advance, such as reasonably arranging grid dispatching, etc.; the remote control terminal remotely controls the wind turbine set based on the optimal pitch angle, so that the wind turbine set always operates in the optimal state, can maximize the use of wind energy, improve power generation efficiency, and increase the economic benefits of the wind farm; the prediction information is displayed to the manager, and the manager can view and understand the operation status of the wind farm at any time through the remote control terminal, which is convenient for timely decision-making and adjustment, greatly improving management efficiency, and reducing the cost and risk of manual inspections and on-site operations.

[0060] In order to further optimize the above embodiment, the sensor network module includes:

[0061] A wind rotor sensor unit is provided at the wind rotor of each wind turbine generator set and is used to obtain the current wind rotor speed, wind rotor radius and pitch angle of each wind turbine generator set in real time;

[0062] An environmental sensing unit is provided on the top of the cabin of each wind turbine generator set and is used to obtain the humidity data, air pressure data, air density data and wind speed data of the environment in which each wind turbine generator set is currently located in real time;

[0063] The summary front-end unit is connected to the wind wheel sensor unit and the environmental sensor unit, and is used to code the corresponding parameters according to the wind turbine group number to which each sensor unit belongs, and generate operating status data carrying each identification code.

[0064] It should be noted that the wind rotor speed is one of the key factors affecting the utilization of wind energy. By measuring the wind rotor speed, important parameters such as the tip speed ratio can be calculated, which can then be used to evaluate the operating efficiency of the wind turbine and determine the optimal control strategy. For the measurement of the wind rotor speed, a rotary encoder and other equipment can be used to convert the rotation angle of the wind rotor into an electrical signal, and then the speed can be calculated.

[0065] The rotor radius is an inherent parameter of the wind turbine, but real-time acquisition of this parameter can be used to more accurately calculate related quantities such as the rotor swept area; the rotor radius can be accurately measured and recorded during the design and installation of the wind turbine, and a corresponding parameter storage module is set in the sensor unit so as to obtain it in real time;

[0066] The pitch angle directly affects the ability of the wind rotor to capture wind energy. Different pitch angle settings under different wind speeds and operating conditions will cause changes in the output power of the wind turbine. Real-time monitoring of the pitch angle helps to achieve precise control. The pitch angle can be measured using an angle sensor installed near the rotation axis of the blade to directly measure the change in the pitch angle.

[0067] Humidity data will affect air density and the physical properties of the blade surface, which in turn will affect the wind energy utilization coefficient and the performance of the wind turbine. For example, a high humidity environment may cause condensation on the blade surface, increase air resistance, and reduce wind energy utilization efficiency. Humidity sensors can use capacitive humidity sensors or resistive humidity sensors to obtain humidity data by measuring the capacitance or resistance changes caused by air humidity.

[0068] Air pressure data is closely related to air density, which is an important parameter for calculating wind energy utilization coefficient and wind turbine output power. Under different air pressure conditions, the energy content and flow characteristics of air will be different, affecting the power generation efficiency of wind turbines. Air pressure sensors can use piezoresistive air pressure sensors or capacitive air pressure sensors to measure air pressure data based on the changes in resistance or capacitance caused by air pressure changes. Air density data itself is a key parameter for calculating wind energy-related formulas, which directly affects the energy content of wind energy and the capture and conversion efficiency of wind turbines. Air density can be calculated by indirectly measuring temperature and air pressure data and then using the ideal gas state equation.

[0069] Wind speed data is one of the core parameters for wind farm operation. The size and change of wind speed determines the wind energy resources available to wind turbines and is an important basis for power prediction and control strategy formulation. Wind speed sensors can use three-cup wind speed sensors or ultrasonic wind speed sensors to obtain wind speed data by measuring the impact of air flow on the sensor.

[0070] In order to accurately distinguish the parameters of different wind turbines in the subsequent data transmission and processing, it is necessary to code each parameter; by coding according to the wind turbine number to which it belongs, the accuracy and identifiability of the data can be ensured, and the central analysis platform can effectively analyze and process large amounts of data; it can use devices such as microcontrollers or field programmable gate arrays (FPGAs) to implement the coding function; in terms of hardware, these devices have sufficient processing power and storage units, and can associate the data transmitted by the wind wheel sensor unit and the environmental sensor unit with the corresponding wind turbine number according to pre-set rules, and add an identification code; in terms of software, the corresponding encoding program can be written to realize data packaging and identification processing.

[0071] In order to further optimize the above embodiment, the communication module includes:

[0072] A data packaging unit, which is connected to the summary front-end unit and is used to sort out the current operating status data according to the identification codes carried by them, and generate a data set corresponding to each wind turbine group;

[0073] The data transmission unit is connected to the data packaging unit and is used to send each data set to the central analysis platform through wireless transmission technology.

[0074] It should be noted that the data packaging unit can use data processing software or programming language to implement the data packaging function; for example, define the data structure in the software, classify and store the data into the corresponding structure according to the identification code, and form a data set corresponding to each wind turbine; or use a database management system to store the data in different tables according to the wind turbine number, and implement data sorting and packaging through query operations;

[0075] The data transmission unit can choose a variety of methods; for example, for areas with short distances and good communication environments, short-range wireless communication technologies such as ZigBee and Bluetooth can be used; for longer distances within the wind farm and higher communication quality requirements, 5G communication technology or wireless local area network (WLAN) technology based on the IEEE802.11 standard can be used; if the wind farm is located in a remote area and the communication infrastructure is poor, satellite communication technology can also be considered to ensure data transmission; at the same time, in order to ensure the stability and reliability of data transmission, redundant design can be adopted, such as using multiple wireless transmission methods at the same time or setting up multiple communication base stations to avoid data transmission interruption due to failure of a certain communication method.

[0076] In order to further optimize the above embodiment, the central analysis platform includes:

[0077] A total output power prediction unit, which is connected to the data transmission unit, is used to analyze the tip speed ratio of each wind turbine according to the parameters in the data set, predict the output power of the wind turbine based on the tip speed ratio, and summarize the output power of each wind turbine to obtain a total output power prediction result of the wind farm;

[0078] An optimal pitch angle analysis unit, which is connected to the data transmission unit and is used to calculate the optimal pitch angle of each wind turbine according to the parameters in the data set and a preset pitch angle analysis model;

[0079] A wind energy utilization rate prediction unit, which is connected to the data transmission unit and the total output power prediction unit, and calculates the wind energy utilization rate prediction result of the current wind farm based on the wind energy utilization rate prediction model;

[0080] A summary output unit, which is connected to the total output power prediction unit, the optimal pitch angle analysis unit and the wind energy utilization rate prediction unit, and is used to summarize the total output power prediction result and the wind energy utilization rate prediction result of the wind farm into prediction information, and convert the optimal pitch angle of each wind turbine into a corresponding control instruction;

[0081] Specifically, the prediction process of the total output power of a wind farm includes:

[0082] Calculate the tip speed ratio λ of each wind turbine i ; The calculation formula is:

[0083]

[0084] Among them, λ i is the current tip speed ratio of the i-th wind turbine, ω r and R are the rotor speed and rotor radius corresponding to the i-th wind turbine, respectively, and V is the wind speed data;

[0085] Calculate the output power prediction value P of each wind turbine i ; The calculation formula is:

[0086] P i =0.5*ρ*A*V 3 *C p (λ, β, α, θ)

[0087] Among them, P i is the predicted output power of the i-th wind turbine; ρ is the air density; A is the rotor swept area of ​​the i-th wind turbine, which is determined based on the rotor radius R; C p (λ, β, α, θ) is the wind energy utilization coefficient, and its calculation formula is:

[0088]

[0089] in, is the maximum coefficient corresponding to the Betz limit case; λ i is the current tip speed ratio of the i-th wind turbine, λ0 is the historical lower limit of the tip speed ratio of the i-th wind turbine, and λ r is the historical limit of the tip speed ratio of the i-th wind turbine; β i is the current pitch angle of the i-th wind turbine, β0 is the historical lower limit of the pitch angle of the i-th wind turbine, β r is the historical limit of the pitch angle of the i-th wind turbine; α i is the current humidity data of the environment where the i-th wind turbine is located, α0 is the lower limit of the historical humidity data of the environment where the i-th wind turbine is located, and α r is the upper limit of the historical humidity data of the environment where the i-th wind turbine is located; θ i is the current air pressure data of the environment where the i-th wind turbine is located, θ0 is the lower limit of the historical air pressure data of the environment where the i-th wind turbine is located, θ r is the upper limit of the historical air pressure data of the environment where the i-th wind turbine is located;

[0090] The output power prediction value P of each wind turbine i Summarize and combine historical humidity, air pressure and aging coefficient to calculate the total output power forecast result P of the wind farm total ; The calculation formula is:

[0091]

[0092] Among them, P total is the total output power prediction result of the current wind farm; n is the number of wind turbines; θ i0 is the initial air pressure data of the environment where the i-th wind turbine is located, α i0 is the initial humidity data of the environment where the i-th wind turbine is located; the initial air pressure data θ i0 and initial humidity data α i0 Determined by the records during the installation of the wind turbine; s is the equipment aging coefficient, which is set based on the operating time and maintenance records of the wind turbine.

[0093] It should be noted that for the ideal Betz limit case, the maximum value of the wind energy utilization coefficient is This is obtained through theoretical analysis of the energy changes of the airflow before and after the wind rotor. When the wind rotor intercepts energy from the wind, in an ideal situation, the wind rotor can only transfer at most 10% of the energy passing through its swept area. The conversion of wind energy into mechanical energy. Although actual wind turbines cannot reach the Betz limit due to various practical factors (such as blade shape, complexity of air flow, mechanical loss, etc.), this theoretical value provides a reference basis for the calculation of wind energy utilization coefficient;

[0094] The tip speed ratio is the ratio of the linear velocity of the rotor tip to the wind speed; the linear velocity of the rotor tip is determined by the rotor speed and radius. By calculating the tip speed ratio, we can understand the matching relationship between the rotor and the wind speed. Different tip speed ratios will cause changes in the wind energy utilization coefficient, which will in turn affect the output power of the wind turbine; the output power of the wind turbine is related to the wind energy utilization coefficient, wind speed, air density and rotor swept area; the wind energy utilization coefficient takes into account multiple factors such as the tip speed ratio, pitch angle, ambient humidity and atmospheric pressure, and reflects the efficiency of the wind turbine in converting wind energy into electrical energy; when calculating the total output power of a wind farm, it is necessary to consider the output power prediction value of each wind turbine as well as factors such as ambient humidity, air pressure changes and equipment aging; since changes in ambient humidity and air pressure will affect air density and wind energy utilization coefficient, and thus affect the output power of the wind turbine, these factors need to be corrected; equipment aging will also cause the performance of wind turbines to decline. By introducing the equipment aging coefficient, the total output power of the wind farm can be predicted more accurately.

[0095] In order to further optimize the above embodiment, the pitch angle analysis model is:

[0096]

[0097] in, is the optimal pitch angle of the i-th wind turbine; iis the current tip speed ratio of the i-th wind turbine, ω r is the rotor speed corresponding to the i-th wind turbine, V is the wind speed data, α i is the current humidity data of the environment where the i-th wind turbine is located.

[0098] It should be noted that the optimal pitch angle is the pitch angle setting that enables the wind turbine to achieve the best operating state while considering multiple factors; the tip speed ratio reflects the relationship between the rotor speed and the wind speed. When the tip speed ratio changes, in order to maintain the best wind energy utilization efficiency, the pitch angle needs to be adjusted accordingly; the rotor speed and wind speed are the key factors that determine the tip speed ratio, and also directly affect the operating state of the wind turbine; different combinations of rotor speeds and wind speeds require different pitch angles to optimize wind energy utilization; environmental humidity affects air density and the physical properties of the blade surface, and thus affects the performance of the wind turbine; when considering the optimal pitch angle, the influence of environmental humidity needs to be included to ensure optimal operation under current environmental conditions;

[0099] By comprehensively considering these factors and using the pitch angle analysis model to determine the optimal pitch angle, the wind turbine can achieve the best operating effect under the current operating environment and its own operating parameters, such as maximizing wind energy utilization efficiency or output power.

[0100] In order to further optimize the above embodiment, the wind energy utilization rate prediction model is:

[0101]

[0102] Where, η is the wind energy utilization rate of the current wind farm; P total is the total output power prediction result of the current wind farm; ρ is the air density, V is the wind speed data, A total is the total area of ​​the wind farm.

[0103] It should be noted that wind energy utilization rate refers to the ratio of the actual output of electric energy of a wind farm to the total amount of available wind energy. The air density and wind speed determine the amount of wind energy contained in a unit volume of air, and the total area of ​​the wind farm determines the range of available wind energy. The wind energy utilization rate η of the current wind farm can intuitively understand the degree of utilization of wind energy resources by the wind farm, and provide a basis for evaluating the operating efficiency of the wind farm and optimizing the operating strategy.

[0104] In order to further optimize the above embodiment, the remote control terminal includes:

[0105] A display unit connected to the summary output unit, used to display the current forecast information on the display device, and to combine the historical forecast information and the current forecast information to form a trend chart;

[0106] The signal sending unit is connected to the summary output unit and is used to convert the generated control instruction into a control signal to control the adjustment action of the wind turbine control end so that the current pitch angle of the wind turbine reaches the optimal pitch angle.

[0107] It should be noted that the display unit can be implemented using graphical user interface (GUI) technology. By developing a special software application, a visual interface is created on the display device to display forecast information and trend charts. Various chart drawing tools, such as bar charts, line charts, pie charts, etc., can be used to intuitively present different types of data. At the same time, the database management system is used to store historical forecast information and current forecast information so that they can be queried and analyzed when needed. The signal sending unit can be implemented using communication protocol conversion technology. According to the communication protocol supported by the wind turbine control end, the control instructions generated by the summary output unit are formatted to meet the requirements of the wind turbine control end. For example, if the wind turbine control end supports the Modbus protocol, and the control instructions generated by the summary output unit are based on other protocols, protocol conversion is required. A special communication protocol conversion module or software can be used to implement this function.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote control system for a wind farm, characterized in that: include: The sensor network module is installed in key locations of each wind turbine in the wind farm to collect the operating status data of each wind turbine in real time; A communication module, which is wirelessly connected to the sensor network module and is used to package and transmit the currently collected operating status data; A central analysis platform, which is connected to the communication module and receives the current operating status data of each wind turbine, is used to predict the total output power of the wind farm, analyze the optimal pitch angle of each wind turbine, and predict the wind energy utilization rate of the wind farm; A remote control terminal is connected to the central analysis platform and the wind turbine control terminal, and is used to display the prediction information to the manager and remotely control the action of the wind turbine based on the optimal pitch angle.

2. A wind farm remote control system according to claim 1, characterized in that: The sensor network module comprises: A wind rotor sensor unit is provided at the wind rotor of each wind turbine generator set and is used to obtain the current wind rotor speed, wind rotor radius and pitch angle of each wind turbine generator set in real time; An environmental sensing unit is provided on the top of the cabin of each wind turbine generator set and is used to obtain the humidity data, air pressure data, air density data and wind speed data of the environment in which each wind turbine generator set is currently located in real time; The summary front-end unit is connected to the wind wheel sensor unit and the environment sensor unit, and is used to code the corresponding parameters according to the wind turbine group number to which each sensor unit belongs, and generate operating status data carrying each identification code.

3. A wind farm remote control system according to claim 2, characterized in that: The communication module comprises: A data packaging unit, which is connected to the summary front-end unit and is used to sort out the current operating status data according to the identification codes carried by them, and generate a data set corresponding to each wind turbine group; A data transmission unit is connected to the data packaging unit and is used to send each data set to the central analysis platform through wireless transmission technology.

4. A wind farm remote control system according to claim 3, characterized in that: The central analysis platform includes: a total output power prediction unit, which is connected to the data transmission unit and is used to analyze the tip speed ratio of each wind turbine according to the parameters in the data set, predict the output power of the wind turbine based on the tip speed ratio, and summarize the output power of each wind turbine to obtain a total output power prediction result of the wind farm; An optimal pitch angle analysis unit, connected to the data transmission unit, for calculating the optimal pitch angle of each wind turbine according to the parameters in the data set and a preset pitch angle analysis model; A wind energy utilization rate prediction unit, which is connected to the data transmission unit and the total output power prediction unit, and calculates a wind energy utilization rate prediction result of the current wind farm based on a wind energy utilization rate prediction model; A summary output unit is connected to the total output power prediction unit, the optimal pitch angle analysis unit and the wind energy utilization prediction unit, and is used to summarize the total output power prediction results and wind energy utilization prediction results of the wind farm into prediction information, and convert the optimal pitch angle of each wind turbine group into a corresponding control instruction.

5. A wind farm remote control system according to claim 4, characterized in that: The prediction process of the total output power of the wind farm specifically includes: Calculate the tip speed ratio λ of each wind turbine i ; The calculation formula is: Among them, λ i is the current tip speed ratio of the i-th wind turbine, ω r and R are the rotor speed and rotor radius corresponding to the i-th wind turbine, respectively, and V is the wind speed data; Calculate the output power prediction value P of each wind turbine i ; The calculation formula is: P i =0.5*ρ*A*V 3 *C p (λ, β, α, θ) Among them, P i is the predicted output power of the i-th wind turbine; ρ is the air density; A is the rotor swept area of ​​the i-th wind turbine, which is determined based on the rotor radius R; C p (λ, β, α, θ) is the wind energy utilization coefficient, and its calculation formula is: in, is the maximum coefficient corresponding to the Betz limit case; λ i is the current tip speed ratio of the i-th wind turbine, λ0 is the historical lower limit of the tip speed ratio of the i-th wind turbine, and λ r is the historical limit of the tip speed ratio of the i-th wind turbine; β i is the current pitch angle of the i-th wind turbine, β0 is the historical lower limit of the pitch angle of the i-th wind turbine, β r is the historical limit of the pitch angle of the i-th wind turbine; α i is the current humidity data of the environment where the i-th wind turbine is located, α0 is the lower limit of the historical humidity data of the environment where the i-th wind turbine is located, and α r is the upper limit of the historical humidity data of the environment where the i-th wind turbine is located; θ i is the current air pressure data of the environment where the i-th wind turbine is located, θ0 is the lower limit of the historical air pressure data of the environment where the i-th wind turbine is located, θ r is the upper limit of the historical air pressure data of the environment where the i-th wind turbine is located; The output power prediction value P of each wind turbine i Summarize and combine historical humidity, air pressure and aging coefficient to calculate the total output power forecast result P of the wind farm total ; The calculation formula is: Among them, P total is the total output power prediction result of the current wind farm; n is the number of wind turbines; θ i0 is the initial air pressure data of the environment where the i-th wind turbine is located, α i0 is the initial humidity data of the environment where the i-th wind turbine is located; the initial air pressure data θ i0 and initial humidity data α i0 Determined by the records during the installation of the wind turbine; ∈ is the equipment aging coefficient, which is set based on the operating time and maintenance records of the wind turbine.

6. A wind farm remote control system according to claim 5, characterized in that: The pitch angle analysis model is: in, is the optimal pitch angle of the i-th wind turbine; i is the current tip speed ratio of the i-th wind turbine, ω r is the rotor speed corresponding to the i-th wind turbine, V is the wind speed data, α i is the current humidity data of the environment where the i-th wind turbine is located.

7. A wind farm remote control system according to claim 6, characterized in that: The wind energy utilization rate prediction model is: Where, η is the wind energy utilization rate of the current wind farm; P total is the total output power prediction result of the current wind farm; ρ is the air density, V is the wind speed data, A total is the total area of ​​the wind farm.

8. A wind farm remote control system according to claim 7, characterized in that: The remote control terminal comprises: A display unit connected to the summary output unit, used to display the current forecast information on a display device, and to combine the historical forecast information and the current forecast information to form a trend chart; The signal sending unit is connected to the summary output unit and is used to convert the generated control instruction into a control signal to control the adjustment action of the wind turbine control end so that the current pitch angle of the wind turbine reaches the optimal pitch angle.

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