Architecture design method for data acquisition terminal of network construction type fan

Abstract

The invention relates to the technical field of new energy power generation, in particular to an architecture design method for a network construction type fan data acquisition terminal. The method comprises the following steps: S1, constructing an active frequency support evaluation data demand system covering multi-factor influence, and determining a frequency change rate and a maximum frequency deviation evaluation index; s2, designing a network construction type fan data acquisition terminal architecture comprising a sensing platform, a communication platform, a control platform and a monitoring platform, and realizing data multi-dimensional sensing and efficient transmission control; and S3, based on a PSCAD simulation platform, analyzing and constructing the active frequency support characteristics of the net type fan from different disturbance levels, inertia time constants and wind speed scenes. According to the method, in the aspects of performance evaluation and index construction, the constructed evaluation index system provides a comprehensive basis for quantitative evaluation of the frequency supporting capacity of the network construction type fan.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power generation technology, and in particular to a grid-forming wind turbine data acquisition terminal architecture design method. BACKGROUND

[0002] Under the strong driving of global energy transformation, large-scale new energy represented by wind power is connected to the power grid, which promotes the power system to accelerate transformation to a "double-high" form of high proportion of new energy and high proportion of power electronics. Relying solely on synchronous units for frequency regulation cannot effectively cope with the power fluctuations of load and new energy, leading to frequency stability crisis. Grid-forming (GFM) control technology can simulate the characteristics of synchronous generators, providing key services such as frequency support and voltage regulation, which is of great significance to improving the stability and reliability of the power system.

[0003] With the continuous increase of new energy penetration, grid-forming design has become a research hotspot. With the rapid development of the new energy industry, a multi-level grid-forming technology standard system has also been established, which clearly requires the frequency ride-through, dynamic response, and inertia simulation performance of grid-forming wind turbines.

[0004] However, new energy generation units are numerous, small in single capacity, have large differences in operating state, and are distributed in scattered locations, making it difficult to directly interact with the dispatching system. Therefore, new energy stations need to be managed as dispatching units. At this time, quantitatively evaluating the frequency support capability of new energy stations is a necessary prerequisite for efficient interaction between station frequency modulation resources and the power grid. At the same time, the expansion of distributed wind power stations has resulted in a variety of types of inverter devices being connected, with different communication protocols and data formats, posing challenges for heterogeneous data acquisition and sensing in the acquisition system. Accurate and real-time data is the basis for the operation monitoring and power generation decision-making of wind power stations.

[0005] Currently, research on control architecture for new energy station access scenarios requires designing a data acquisition terminal architecture that meets actual needs, and in-depth analysis of the active frequency support characteristics of grid-forming wind turbines, in order to provide targeted reference for the optimization design and engineering application of grid-forming wind turbines, and support for the efficient application of grid-forming wind turbines in power systems. SUMMARY

[0006] The present application proposes a grid-forming wind turbine data acquisition terminal architecture design method to solve the technical problems in the prior art.

[0007] To achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows: a grid-forming wind turbine data acquisition terminal architecture design method, comprising the following steps:

[0008] S1, construct an active frequency support evaluation data requirement system covering multi-factor influence, determine the frequency change rate and the maximum frequency deviation evaluation index;

[0009] S2, design a network-constructed wind turbine data acquisition terminal architecture including perception, communication, control and monitoring platform, realize multi-dimensional data perception and efficient transmission control;

[0010] S3, based on the PSCAD simulation platform, analyze the active frequency support characteristics of the network-constructed wind turbine from different disturbance levels, inertia time constant and wind speed scenarios.

[0011] Further, as a preferred technical solution of the application, the multi-factor influence in S1 is divided into wind turbine parameters, power grid operating state and weather conditions; the wind turbine parameters: the wind turbine parameters have a direct impact on the active frequency support capability of the wind turbine, including rated power, rotational inertia and converter control parameters; the power grid operating state: the power grid operating state is an important external factor affecting the active frequency support of the network-constructed wind turbine; the load level, short-circuit capacity and network structure data of the power grid need to be monitored; the weather conditions: the weather conditions have a non-negligible impact on the operation and active frequency support capability of the wind turbine, including wind speed, wind direction and air temperature data.

[0012] Further, as a preferred technical solution of the application, the frequency change rate RoCoF in S1 represents the change rate of the system frequency with time, reflects the transient frequency stability of the system, and the corresponding formula is:

[0013]

[0014] Wherein, f(t1), f(t2) are the system frequencies at time t1 and t2;

[0015] Maximum frequency change rate: in the process of system frequency disturbance, the maximum value of the frequency change rate is recorded;

[0016] The steady-state frequency SF is the frequency value when the frequency of the power system reaches the stable state again after a certain time of adjustment after experiencing disturbance; the steady-state frequency deviation: calculate the difference between the steady-state frequency and the rated frequency of the system, and the formula is:

[0017] Δf steady =f settling -f rated (2)

[0018] Wherein, Δf steady is the steady-state frequency deviation, f setting is the steady-state frequency, and f rated is the rated frequency of the system; the steady-state frequency deviation is used to evaluate the regulation accuracy of the network-constructed wind turbine in the frequency recovery stage;

[0019] Stabilization time T s : records the time required for the frequency to reach the stable frequency from the beginning of the disturbance to the system;

[0020] Absolute frequency minimum Abs_FN: directly records the minimum actual value to which the system frequency drops: the absolute frequency minimum is the most basic evaluation index, and the value size intuitively reflects the severity of the influence of the disturbance on the system frequency;

[0021] Relative frequency minimum Rel_FN: taking the rated frequency of the system as the benchmark, the difference between the frequency minimum and the rated frequency is calculated as a percentage of the rated frequency, and the formula is:

[0022]

[0023] Where, f rated is the rated frequency of the system, f nadir is the frequency minimum, and Δf nadir is the relative frequency minimum.

[0024] Further, as a preferred technical solution of the present application, the networked power supply data acquisition terminal technology architecture in S2 is based on a perception platform to obtain and preprocess real-time data of the controlled equipment of the new energy station; then relying on a communication platform, through a multi-channel communication architecture, data flow between the perception and control platforms is realized; the control platform realizes millisecond-level rapid active support of functions such as dynamic monitoring of the station grid connection point information and primary frequency modulation; the monitoring and management platform is responsible for monitoring of the wind and light equipment, multi-scene dispatching and coordination control, and management and recording of active support.

[0025] Compared with the prior art, the present application has the following advantages:

[0026] (1) In the performance evaluation and index construction aspect, the evaluation index system constructed by the present application provides a comprehensive basis for quantitatively evaluating the frequency support capability of the networked wind turbine; in the data acquisition terminal architecture design, the perception, communication, control and monitoring platform architecture proposed realizes accurate acquisition, reliable transmission and efficient control of the wind turbine operating state, grid parameters and environmental information, provides technical support for the "active network construction" of the wind turbine, and helps to improve the operation and maintenance efficiency and the grid connection stability.

[0027] (2) The present application method determines the influence mechanism of factors such as disturbance level, inertia time constant and wind speed on the active frequency support characteristics of the networked wind turbine. Among them, the disturbance intensifies, the inertia time constant decreases, and the wind speed increases, which will weaken the frequency support performance of the wind turbine, resulting in the decline of frequency stability and the reduction of regulation accuracy. These findings provide a direction for optimizing the control strategy of the networked wind turbine, helping to improve its frequency support capability under complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and are used to explain the application, but do not limit the application.

[0029] Figure 1 is a key factor affecting the active support capability of the wind turbine generator in the application;

[0030] Figure 2 is the meaning of the frequency support evaluation index in the application;

[0031] Figure 3 is a network type power supply data acquisition terminal technical architecture schematic diagram of the embodiment of the application;

[0032] Figure 4 is a terminal data acquisition schematic diagram of the embodiment of the application;

[0033] Figure 5 is a data preprocessing flowchart of the embodiment of the application;

[0034] Figure 6 is a new energy multi-level parallel communication network architecture diagram of the embodiment of the application;

[0035] Figure 7 is a multi-level closed-loop control block diagram in the embodiment of the application;

[0036] Figure 8 is a primary frequency modulation flowchart in the embodiment of the application;

[0037] Figure 9 is a wind power fast frequency response active-frequency droop characteristic schematic diagram in the embodiment of the application;

[0038] Figure 10 is a man-machine interface and information display schematic diagram in the embodiment of the application;

[0039] Figure 11 is a frequency response curve diagram under different disturbance levels in the embodiment of the application;

[0040] Figure 12 is a frequency response curve diagram under different inertia time constants in the embodiment of the application;

[0041] Figure 13 is a frequency response curve diagram under different wind speeds in the embodiment of the application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the application more clear, the application will be further described in detail below in combination with the drawings and embodiments. Of course, the specific embodiments described here are only used to explain the application, and do not limit the application.

[0043] A network-constructing type fan data acquisition terminal architecture design method, comprising the following steps:

[0044] S1, an active frequency support evaluation data requirement system covering multiple factor influences is constructed, and frequency change rate and maximum frequency deviation evaluation indexes are determined;

[0045] S2, a network-constructing type fan data acquisition terminal architecture including a perception, communication, control and monitoring platform is designed, and multi-dimensional data perception and efficient transmission control are realized;

[0046] S3, based on a PSCAD simulation platform, active frequency support characteristics of the network-constructing type fan are analyzed from different disturbance levels, inertia time constants and wind speed scenarios.

[0047] The network-constructing type wind turbine active frequency support evaluation data requirement: the new energy power generation unit is large in quantity, but the single machine capacity is limited, the running state is complex and changeable and is widely distributed, and it is difficult to directly interface with the dispatching system, so it is necessary to integrate and manage the new energy station as a dispatching unit. Quantitative evaluation of the fast frequency support capability (FFSC) of the new energy station is the key basis for realizing efficient interaction of the station frequency modulation resource and the power grid. For the station, real-time evaluation of the FFSC is helpful to optimize the frequency modulation control strategy and realize accurate regulation; for the power grid, accurate quantification of the FFSC can clearly define the adjustable range of the resource and provide a basis for dispatching decision-making to perfect the power grid frequency modulation system.

[0048] Wind farm fast frequency response characteristics: fast frequency support (FFS) is a new type of power grid support technology relying on the fast frequency response function of the converter of the non-synchronous power source, and plays a regulating role when the system frequency suddenly changes. Its definition is: when the power grid has serious active power imbalance, the FFS resource acts before the synchronous machine primary frequency modulation, adjusts the active power injection quickly, cooperates with the synchronous inertia and the primary frequency modulation mechanism, and suppresses the sharp change of the system frequency in the initial stage of the disturbance. The present application focuses on the suppression effect of the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the FFS resource in the transient frequency response stage after the system power is greatly disturbed.

[0049] The station FFS is the result of the active power-frequency response cooperation of the internal wind power support unit, and finally reflects at the common connection point of the station and the power grid, and has the following characteristics:

[0050] (1) Multiple factor coupling influence: the realization of the station FFS is subject to multiple constraints of wind resources, external power grid conditions and internal mechanical / electrical links of the power generation unit, and is the result of the comprehensive action of a large number of state and characteristic different control units. Frequency measurement, internal communication of the station, interaction mode of the station and the single machine in the FFS process, FFS control strategy and single machine response, etc. The internal wind speed, steady-state running state and various electrical and mechanical quantity limiting factors of the station will affect the FFS characteristics.

[0051] (2) Dynamic Time-Varying Characteristics: The FFS response characteristics of wind power generation units within the power station depend on their control strategies. Affected by resource fluctuations and changes in operating conditions, wind power generation units need to dynamically adjust the FFS control methods and parameters according to real-time resources and operating status, resulting in time-varying FFS response characteristics. In addition, the type, location, and intensity of power disturbances vary under different power grid operating scenarios. The superposition of multiple factors causes the power station's FFS to exhibit different response characteristics at different times, showing significant time-varying characteristics.

[0052] The main influencing factors of wind turbine FFSC can be divided into three categories: wind turbine parameters, grid operating status, and meteorological conditions. Figure 1 As shown.

[0053] Wind turbine intrinsic parameters: The intrinsic parameters of a wind turbine directly affect its active frequency support capability. These mainly include rated power, moment of inertia, and converter control parameters. Rated power data determines the maximum active power regulation that the wind turbine can provide during frequency support, and can be obtained from the wind turbine's design documents and nameplate information. Moment of inertia reflects the wind turbine's ability to store and release kinetic energy; its data is usually determined through a combination of theoretical calculations and actual testing, involving the wind turbine's mechanical structure parameters and operating characteristic data. Converter control parameters, such as power regulation speed and response delay time, can be obtained from the wind turbine's control system parameter setting files and operation and commissioning records. These parameters directly affect the wind turbine's response speed and accuracy to frequency changes.

[0054] Grid Operation Status: The grid operation status is a crucial external factor affecting the active frequency support of grid-connected wind turbines. It is necessary to monitor data such as grid load levels, short-circuit capacity, and grid structure. Load level data can be collected in real time through the grid load monitoring system to understand load changes at different times, analyze the impact of load fluctuations on frequency stability, and determine the frequency support requirements of wind turbines under different load conditions. Short-circuit capacity data reflects the strength of the grid and can be obtained through grid short-circuit current calculations and system parameter measurements; its magnitude affects power transmission and stability during wind turbine frequency support. Grid structure data, including line lengths, conductor types, and transformer parameters, can be obtained from grid planning and design data and geographic information systems (GIS) and is used to analyze the impact of grid topology on frequency propagation and regulation.

[0055] Meteorological conditions: Meteorological conditions also have a significant impact on the operation of wind turbines and their active frequency support capability. This mainly involves data such as wind speed, wind direction, and temperature. Wind speed and direction data can be obtained in real-time through anemometers at wind farms. These data directly relate to the output and power variation characteristics of wind turbines, thus affecting their frequency support capability. For example, rapid changes in wind speed can cause fluctuations in the active power of wind turbines, and the impact of these fluctuations needs to be considered during frequency regulation. Temperature data affects air density, thereby indirectly affecting the aerodynamic performance and output of wind turbines. This data can be obtained from meteorological monitoring stations or meteorological sensors within the wind farm to more accurately assess the effectiveness of active frequency support under different meteorological conditions.

[0056] The most direct effect of a power station's Fast Frequency Regulation (FFS) is reflected in the improvement of the system's frequency response characteristics after a severe active power imbalance occurs. The evaluation index system for FFS includes the rate of frequency change, maximum frequency deviation, and steady-state frequency deviation.

[0057] The rate of change of frequency (RoCoF) represents the rate at which the system frequency changes over time, reflecting the transient frequency stability of the system. The corresponding formula is:

[0058]

[0059] Where f(t1) and f(t2) are the system frequencies at times t1 and t2, respectively. Maximum frequency change rate: This is the maximum value of the frequency change rate recorded during the system frequency disturbance. This indicator directly reflects the response capability of the grid-type wind turbine when facing the worst frequency change conditions. The smaller the maximum frequency change rate, the stronger the grid-type wind turbine's ability to suppress frequency changes, and the more effectively it can slow down the rate of frequency change, providing better frequency support for the system.

[0060] Steady-state frequency (SF) refers to the frequency value of a power system when it returns to a steady state after a disturbance and a certain period of adjustment. Steady-state frequency deviation: calculated as the difference between the steady-state frequency and the system's rated frequency, using the formula:

[0061] Δf steady =f settling -f rated (2)

[0062] Where, Δf steady For steady-state frequency deviation, f setting For steady-state frequency, f rated This is the system's rated frequency.

[0063] Steady-state frequency deviation can be used to evaluate the regulation accuracy of grid-type wind turbines during the frequency recovery phase. The closer this value is to 0, the better the grid-type wind turbine can stabilize the system frequency near the rated frequency, the better the frequency support effect, and the higher the degree to which the system recovers to normal operating conditions.

[0064] Settling time (T) s This records the time required from the start of a disturbance in the system until the frequency reaches a stable level. The shorter the stabilization time, the faster the grid-type wind turbine can restore the system frequency to stability.

[0065] Absolute Frequency Minimum (Abs_FN): Directly records the lowest actual value to which the system frequency drops. The absolute frequency minimum is the most basic evaluation indicator, and its value directly reflects the severity of the impact of disturbances on the system frequency. Relative Frequency Minimum (Rel_FN): Calculates the percentage of the difference between the frequency minimum and the rated frequency relative to the rated frequency, using the system's rated frequency as a benchmark. The formula is:

[0066]

[0067] Among them, f rated f is the system's rated frequency. nadir At the point of lowest frequency, Δf nadir This is the point with the lowest relative frequency.

[0068] The meanings of various indicators are as follows: Figure 2 As shown, the evaluation index of frequency regulation effect is generally obtained through offline simulation. It is necessary to know the system frequency response model and analyze the degree of improvement of the system frequency response by the participation of new energy power plants in frequency regulation under the pre-accident scenario.

[0069] The grid-connected wind turbine data acquisition terminal architecture enables wind turbine units to shift from "passive grid connection" to "active grid connection" through multi-dimensional perception and in-depth processing of wind turbine operating status, grid parameters and environmental information, becoming a core technology and equipment for improving the capacity for renewable energy consumption and enhancing grid resilience.

[0070] The figure shows the technical architecture of the three-dimensional grid-type power data acquisition terminal. Based on the sensing platform, it acquires and preprocesses real-time data of the controlled equipment in the new energy power station. Then, relying on the communication platform, it realizes the data flow between the sensing and control platforms through a multi-channel communication architecture. The control platform realizes millisecond-level rapid active support for functions such as dynamic monitoring of the power station's grid connection point information and primary frequency regulation. The monitoring and management platform is responsible for the monitoring of wind and solar equipment, multi-scenario scheduling and coordination control, and active support management and recording.

[0071] The perception platform comprises a data acquisition module and a data preprocessing module. The data acquisition module acquires fan state and environmental data in real time through sensors. The data acquisition terminal should have multi-channel input function and support the access of various sensors. The terminal data acquisition structure is shown in Figure 4 For electrical quantity monitoring, voltage / current sensors acquire grid-connected electrical quantities such as fan outlet voltage, current, frequency and power factor to meet the dynamic response requirements of the grid, and power quality sensors monitor real-time indicators such as harmonic content, voltage sag / rise and flicker. For equipment state monitoring, speed / torque sensors precisely acquire main shaft speed and gearbox torque with the help of encoders. Temperature / humidity sensors are deployed at important locations such as motor windings to obtain temperature and humidity data. For environmental parameter monitoring, wind speed sensors use ultrasonic or mechanical types to provide data for fan power curve matching, wind direction sensors help adjust blade angle to optimize wind energy capture, and air pressure / temperature / humidity sensors monitor cabin environment to assist in determining equipment operating conditions.

[0072] The data preprocessing module is responsible for preliminary processing of the collected data, extraction of effective information, and execution of edge-side data calculation and feature extraction during data acquisition and processing. The signal conditioning module uses high-precision ADC chips for analog-to-digital conversion, supports multi-channel synchronous sampling with a sampling rate ≥ 10 kHz to accurately capture transient processes, and uses hardware RC filtering or FIR / IIR digital filtering technology to eliminate high-frequency noise and reduce data mutations through data smoothing algorithms to ensure data integrity, stability and reliability. The edge computing unit uses high-performance MCUs or DSPs to realize real-time data processing, including electrical quantity effective value calculation, active / reactive power calculation, vibration signal spectrum analysis and envelope demodulation, data compression and outlier detection. The data storage module configures FLASH / EEPROM to store historical data, uses a lightweight file library management system, uses SQL standard interface, supports SQL data access, and meets the needs of the system platform for model and historical data management. Figure 5 The data preprocessing process is shown in

[0073] The communication platform adopts a fast communication protocol and a multi-level parallel network communication architecture. The fast communication protocol: the communication module undertakes the important task of data transmission and exchange, aiming to ensure reliable and real-time data transmission to the monitoring platform. In terms of industrial bus communication, diversified communication protocols are adopted to adapt to different scene requirements. CAN open / Modbus RTU is used to connect local sensors and processing units. With stable data transmission characteristics, it can support the transmission of control data with high real-time requirements such as speed and torque feedback, and is suitable for large-scale wind farms. Ether CAT / Ethernet IP is used as a high-speed Ethernet bus with a transmission rate of ≥100 Mbps, which can meet the synchronous communication needs of multi-node vibration sensor arrays.

[0074] Multi-level parallel network communication architecture: considering the capacity and quantity of new energy station / cluster generation terminal and the management mode of new energy generation, a four-layer communication architecture is designed, as shown in Figure 6 In the new energy station control system, the station single machine layer equipment is mainly wind turbine generators. The station slave layer and the station master layer belong to the station level active support equipment. The architecture design is flexible. For large new energy stations, a "one master and multiple slaves" network architecture is adopted, in which the station internal master layer coordinates multiple slaves to realize full-field active support control. The master is responsible for grid perception and slave unified coordination, and the slave is used as a decentralized control unit to control the power of the generation terminal. For small new energy stations, only one set of control master is needed to complete the grid perception and unified coordination control of all generation terminals. As for the cluster control system layer, in the scenario requiring cluster control, the cluster control master layer coordinates the field control master of each station to realize the active support control of the entire cluster. If there is no need for cluster control, the field control master perceives the grid information of the station, and based on the cluster layer active support control algorithm, combines the real-time operation state and support capacity of each station to issue active support control instructions to complete the control task.

[0075] The control platform adopts multi-level closed-loop control technology and primary frequency regulation response regulation technology. The multi-level closed-loop control technology realizes unified control of multiple types of power generation equipment through multi-level closed-loop control, can accurately and smoothly track power instructions, and improve power station control precision and power generation efficiency.

[0076] As shown in Figure 7As shown, the multi-stage closed-loop control technology is divided into a station layer, a cluster layer and a single machine layer. Through the station layer closed-loop control, the accurate tracking of the whole station active instruction is realized; the station layer distribution algorithm distributes the whole station closed-loop instruction to each cluster control module according to the characteristics of the divided clusters. In the cluster layer, some units can adjust power, and some can only start and stop control, so the closed-loop control needs to be carried out in the cluster layer according to the characteristics of the controlled single machines to realize the unified control of different types of equipment and the smooth output of instructions; the distribution algorithm of the cluster layer distributes power according to the operating characteristics of the single machines. The single machine layer realizes the tracking of the power instruction of the cluster distribution by the generator set, and comprehensively controls the generator set by combining the power estimation value, real-time power and initial instruction of the single machine to improve the power generation potential of the generator set.

[0077] Primary frequency response regulation technology, new energy station uses the corresponding active control system to complete the active-frequency droop characteristic control, so that it has the primary frequency regulation capability at the grid connection point;

[0078] Figure 8 The active-frequency regulation flow chart is shown in the figure. In the system regulation process, the electrical information of the station grid connection point is input through the data acquisition module; the frequency accurate perception module receives these electrical information to monitor the grid frequency, and then carries out support power calculation; at the same time, the state of the generator terminal data is obtained through external communication input, and the whole station support capacity is calculated; finally, the active multi-source coordination link integrates the support power calculation result and the whole station support capacity statistical information, coordinates the wind power resources, dynamically adjusts the active power, and ensures the stable operation of the power system.

[0079] The wind power station uses the corresponding active control system and single machine to complete the active-frequency droop characteristic control, so that it has the ability to participate in the rapid adjustment of the grid frequency at the grid connection point. The active-frequency droop characteristic of the fast frequency response is realized by setting a frequency and active power broken line function, as shown in formula (4):

[0080]

[0081] In the formula, P0 is the initial value of the active power of the new energy station; P n is the rated power of the new energy station; f d is the dead zone of the fast frequency response; f n is the rated frequency of the system; and δ is the fast frequency response regulation rate of the new energy station.

[0082] Figure 9 The active-frequency droop characteristic of the fast frequency response of the wind power station is shown in the figure. According to the droop characteristic curve, the whole station active output adjustment value corresponding to the frequency deviation is calculated to realize the primary frequency regulation function.

[0083] By detecting the frequency difference in real time, the power output of the station can be reduced by 10% at most when the frequency is disturbed to the limit value; in the case of power limit of high power / small power, the power output of the station can be increased by 10% at most when the frequency is disturbed to the limit value. The qualified rate of primary frequency modulation output response and the qualified rate of integrated electric quantity meet the index requirements. The integrated power control system can support the whole active frequency modulation according to the measurement data of the power grid, and can send target instructions to the monitoring background or the power generation terminal.

[0084] The monitoring platform: the monitoring management platform displays the running state information of each device in the new energy station through real-time monitoring of the wind turbine equipment in the new energy station system, and shows the running data of the controlled devices in the new energy station system through a friendly man-machine interface. Relying on the energy management control strategy, the operation mode control and energy management functions of the new energy station are realized. The main interface includes power station overview, operation monitoring and system configuration, and can also be designed according to the needs of the field or users. The man-machine interface and information display are as shown in Figure 10 The running overview block mainly presents the running overview of the new energy station and the main running information of the wind and electronic system; the operation monitoring block mainly displays the primary main wiring, protection measurement and control, public interval of the new energy station, and also contains the wind and electronic system subgraph of the wind turbine main control and converter running data; the system configuration block mainly displays the parameter configuration information of the new energy station, and provides the user with an intuitive presentation of the system running and configuration.

[0085] The simulation software PSCAD is used to build a network type wind turbine transient simulation test model; the network type wind turbine is simulated and tested according to the test index proposed in the application.

[0086] Based on the constructed evaluation index, the active frequency support characteristics of the network type wind turbine are analyzed under different disturbances, parameters and wind speed scenes. By simulating the performance changes under different working conditions, the support ability of the network type wind turbine to the grid frequency under different environments is revealed.

[0087] Different disturbance level experimental results: Figure 11 The frequency response curve under different disturbance levels is shown. With the increase of the disturbance level, the frequency change rate (RoCoF) is larger, the difficulty of the network type wind turbine to suppress the sharp change of the frequency is increased, and the transient frequency stability is reduced. At the same time, the lowest point of the frequency (FN) is reduced, the system frequency is more seriously disturbed by the impact, and the frequency support effect is poor; in addition, the steady-state frequency deviation (SF) also increases significantly with the expansion of the disturbance level, and the adjustment accuracy of the network type wind turbine in the frequency recovery stage is reduced, that is, high disturbance will weaken the frequency support performance of the network type wind turbine.

[0088] Different inertia time constant experimental results: observation Figure 12The frequency response curves corresponding to different inertia time constants show that the inertia time constant and the frequency variation characteristic present a clear negative correlation. With the increase of the inertia time constant, the frequency variation rate (RoCoF) decreases significantly, indicating that the suppression ability of the grid-forming wind turbine to sharp frequency changes is significantly enhanced, which can effectively slow down the frequency change speed. At the same time, the frequency minimum point (FN) gradually rises, reflecting that the degree of influence of the system frequency by the disturbance is significantly reduced, and the frequency support effect is significantly improved. It is worth noting that the stabilization time (T s ) also increases with the increase of the inertia time constant, which means that the recovery speed of the frequency of the grid-forming wind turbine system is slower. Therefore, although the larger inertia time constant has a positive effect on the frequency support ability of the grid-forming wind turbine, it will reduce its frequency rapid recovery performance.

[0089] Different wind speed experimental results: Figure 13 The frequency response curves shown in the different wind speeds show that the wind speed has a significant influence on the frequency support characteristics of the grid-forming wind turbine. The higher the wind speed, the greater the frequency variation rate (RoCoF), and the suppression ability of the wind turbine to sharp frequency changes is significantly weakened, resulting in a decrease in transient frequency stability. At the same time, the frequency minimum point (FN) decreases with the increase of the wind speed, the degree of impact of the system frequency by the disturbance is intensified, and the frequency support effect is deteriorated. In addition, the steady-state frequency deviation (SF) increases significantly under high wind speed conditions, indicating that the adjustment accuracy of the wind turbine in the frequency recovery stage is greatly reduced. Therefore, the high wind speed environment seriously restricts the grid-forming wind turbine to play a frequency support role.

[0090] In the performance evaluation and index construction aspect, the evaluation index system constructed provides a comprehensive basis for quantitatively evaluating the frequency support ability of the grid-forming wind turbine; in the data acquisition terminal architecture design, the perception, communication, control and monitoring platform architecture proposed realizes accurate acquisition, reliable transmission and efficient control of the wind turbine operation state, power grid parameters and environmental information, provides technical support for the "active grid forming" of the wind turbine, and helps to improve the operation and maintenance efficiency and the grid-connected stability.

[0091] Through multi-scenario simulation analysis, the influence mechanism of factors such as disturbance level, inertia time constant, wind speed and the like on the active frequency support characteristics of the grid-forming wind turbine is clarified. Among them, the disturbance intensifies, the inertia time constant decreases, and the wind speed increases, which will weaken the frequency support performance of the wind turbine, resulting in a decrease in frequency stability and a decrease in adjustment accuracy. These findings provide a direction for the optimization of the control strategy of the grid-forming wind turbine, and help to improve its frequency support ability under complex working conditions.

[0092] The above-described specific embodiments further illustrate the objects, technical solutions and advantages of the present application. It should be understood that the above-described specific embodiments are merely for the purpose of illustrating the present application, and are not intended to limit the scope of the present application. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present application shall fall within the scope of the present application.

Claims

1. A network-constructed fan data acquisition terminal architecture design method, characterized in that, It comprises the following steps: S1, constructing an active frequency support evaluation data requirement system covering multi-factor influence, determining the frequency change rate and maximum frequency deviation evaluation index; S2, designing a network type wind turbine data acquisition terminal architecture including perception, communication, control and monitoring platform, realizing multi-dimensional perception and efficient transmission control of data; S3, based on PSCAD simulation platform, analyzing the active frequency support characteristics of network type wind turbine from different disturbance levels, inertia time constant and wind speed scene.

2. The network-constructing type fan data acquisition terminal architecture design method according to claim 1, characterized in that, The multi-factor influence in S1 is divided into wind turbine parameters, grid operating state and weather conditions; Wind turbine parameters: the parameters of wind turbine have a direct impact on its active frequency support capability, including rated power, moment of inertia and converter control parameters; Grid operating state: the grid operating state is an important external factor affecting the active frequency support of network type wind turbine; the load level, short circuit capacity and grid structure data of the grid need to be monitored; Weather conditions: weather conditions have a non-negligible impact on the operation and active frequency support capability of wind turbine, including wind speed, wind direction and air temperature data.

3. The network-constructing type fan data acquisition terminal architecture design method according to claim 2, characterized in that, The frequency change rate RoCoF in S1 represents the change rate of system frequency with time, reflecting the transient frequency stability of the system, and the corresponding formula is: Where f(t1), f(t2) are the system frequencies at time t1 and t2; Maximum frequency change rate: during the process of system frequency disturbance, the maximum value of frequency change rate is recorded; Steady state frequency SF is the frequency value when the power system frequency returns to stable state after a certain time of adjustment after disturbance; steady state frequency deviation: calculate the difference between steady state frequency and system rated frequency, the formula is: Δf steady = f settling - f rated (2) where Δf steady is the steady-state frequency deviation, f setting is the steady-state frequency, f rated is the system rated frequency; the steady-state frequency deviation is used to evaluate the regulation accuracy of grid-forming wind turbines in the frequency recovery stage; Stabilization time T s : time required for the frequency to reach the stable frequency from the start of the disturbance to the system; Absolute frequency minimum Abs_FN: directly record the lowest actual value of system frequency drop: absolute frequency minimum is the most basic evaluation index, its value size directly reflects the severity of system frequency affected by disturbance; Relative frequency minimum Rel_FN: taking the rated frequency of the system as the benchmark, calculate the difference between the minimum frequency and the rated frequency as a percentage of the rated frequency, the formula is: where f rated is the system rated frequency, f nadir is the frequency minimum point, Δf nadir is the relative frequency minimum point.

4. The network-constructing type fan data acquisition terminal architecture design method according to claim 3, characterized in that, The network type power supply data acquisition terminal technology architecture in S2 is based on the perception platform to obtain and preprocess the real-time data of the controlled equipment in the new energy station; then rely on the communication platform to realize the data flow between the perception and control platforms through the multi-channel communication architecture; the control platform realizes the dynamic monitoring of the grid connection point information and the millisecond level rapid active support of primary frequency modulation function; the monitoring management platform is responsible for the monitoring of wind and light equipment, multi-scene dispatching and coordination control and the management and recording of active support.

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