Fan master control PLC detection method, system, equipment and medium

Abstract

The invention discloses a fan master control PLC detection method, system and device and a medium, and belongs to the technical field of PLC detection, and the method comprises the following steps: building Ethernet communication connection between a test controller and a to-be-tested PLC; detecting manufacturer information, model information and communication protocol information of the to-be-tested PLC, and generating a test parameter configuration table according to an identification result; executing the function test of the to-be-tested PLC interface; performing fan control logic verification, and verifying the accuracy of a variable pitch control mechanism, a yaw control mechanism and a power control mechanism of the to-be-tested PLC by inputting a fan operation characteristic signal; and displaying an interface function test result and a control logic verification result on a human-computer interaction interface. According to the invention, interface function testing and fan control logic verification are organically combined to form an integrated detection system, the basic hardware function of the PLC can be verified, the control performance of the PLC can be evaluated in a real control environment, and deep integration of hardware detection and software verification is realized.

Description

Technical Field

[0001] This invention relates to the field of PLC testing technology, specifically to a method, system, equipment, and medium for testing a fan main control PLC. Background Technology

[0002] As the core control device of a wind turbine system, the main control PLC plays a crucial role in key functions such as pitch control, yaw control, and power control, directly impacting the turbine's power generation efficiency and operational safety. With the rapid development of wind power technology and the continuous growth of installed wind turbine capacity, the performance requirements for the main control PLC are becoming increasingly stringent, demanding high reliability, rapid response capabilities, and adaptability to complex operating conditions. Traditional PLC testing primarily employs single hardware interface testing or simple control logic verification. This fragmented testing method cannot comprehensively evaluate the PLC's overall performance in actual wind turbine operating environments.

[0003] In existing technologies, testing methods for wind turbine main control PLCs typically separate interface function testing from control logic verification, lacking a holistic performance evaluation of the PLC under multi-variable coupled control conditions. Furthermore, current control logic verification methods often employ single-type excitation signals, failing to simulate the complex wind conditions encountered by wind turbines in actual operation, particularly the real-world working environment where gradual and sudden wind conditions alternate. In addition, traditional testing methods lack in-depth analysis of the interactions between control mechanisms, making it difficult to detect potential stability issues in the control system under complex operating conditions, leading to significant discrepancies between test results and actual operating performance. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for detecting the main control PLC of a wind turbine.

[0005] Therefore, the technical problem solved by this invention is that existing control logic verification mostly uses a single type of excitation signal, which cannot simulate the complex wind conditions encountered by wind turbines in actual operation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for detecting a wind turbine main control PLC, comprising the following steps: Establish an Ethernet communication connection between the test controller and the PLC under test; The system detects the manufacturer, model, and communication protocol information of the PLC under test and generates a test parameter configuration table based on the identification results. Perform interface function tests on the PLC under test, including digital input / output interface tests, analog input / output interface tests, and communication interface tests; Verify the wind turbine control logic by inputting wind turbine operating characteristic signals to verify the accuracy of the pitch control mechanism, yaw control mechanism and power control mechanism of the PLC under test; The interface function test results and control logic verification results are displayed on the human-computer interaction interface.

[0007] As a preferred embodiment of the wind turbine main control PLC testing method described in this invention, the step of generating the test parameter configuration table includes: Send a standardized device description request; Parse the structured device description information corresponding to the device description request returned by the PLC under test; Extract the manufacturer information, model information, and hardware configuration information of the PLC under test from the structured equipment description information; Based on the manufacturer and model information, the corresponding wind turbine technical parameters are obtained by matching from the built-in wind turbine main control PLC parameter database; The interface test parameters are determined using the hardware configuration information, and the control logic verification parameter set is established using the wind turbine technical parameters. The interface test parameters and control logic verification parameter sets are integrated to generate a test parameter configuration table.

[0008] The beneficial effects of this preferred technical solution are as follows: by automatically identifying PLC equipment information and matching fan technical parameters, intelligent configuration of test parameters is realized. The combination of hardware configuration information and fan technical parameters enables interface test parameters and control logic verification parameters to be optimized in synergy, avoiding parameter mismatch problems that may be caused by manual configuration.

[0009] As a preferred embodiment of the wind turbine main control PLC testing method described in this invention, the step of performing the interface function test of the PLC under test includes: According to the test parameter configuration table, input test signals to the digital interface of the PLC under test and detect its output response. Input different voltage values ​​into the analog interface of the PLC under test and test its output accuracy; Send test data to the communication interface of the PLC under test and verify whether the communication is normal; Record the test results for each interface.

[0010] As a preferred embodiment of the wind turbine main control PLC testing method described in this invention, the step of verifying the wind turbine control logic includes: A dynamic simulation model of the wind turbine is established based on the control logic verification parameter set in the test parameter configuration table. The wind turbine operating characteristic signal is generated by combining gradual excitation signal and sudden excitation signal, and the signal is modulated by the wind turbine dynamic simulation model. The wind turbine operating characteristic signals are input to the pitch control input, yaw control input, and power control input of the PLC under test, respectively. Response data from each control output is collected synchronously. For the three subsystems of pitch control, yaw control, and power control, a unit step excitation is applied to each control input, and the corresponding output change is measured. A three-by-three interaction matrix M is constructed using the following formula: ; in, Let be the gain representing the relative influence of the j-th control input on the i-th control output. Let i be the change in the i-th output. For the change of the j-th input, and These are the corresponding nominal values; Calculate the multivariate coupling evaluation index based on the interaction matrix M. ,when A value greater than 0.3 indicates a strongly coupled system, and the calculation formula is as follows: ; For strongly coupled systems, the interaction influence matrix is... As the steady-state gain matrix of the system, the frequency domain transfer function matrix is ​​constructed by combining the dynamic characteristic parameters of each control loop of the PLC under test. and The interaction influence matrix M is and The value at zero frequency, through stability analysis using the generalized Nyquist stability criterion, is expressed mathematically as follows: ; In the formula, Let be the determinant of the matrix. It is the identity matrix. Let be the transfer function matrix of the wind turbine controlled object. The transfer function matrix of the PLC controller. For complex frequency domain variables, This holds true for all frequencies; The system is considered unstable if there exists any frequency point where the determinant is equal to zero, and stable if the determinant of all frequency points is not equal to zero.

[0011] The beneficial effects of this preferred technical solution are as follows: By establishing an interaction influence matrix M and combining it with frequency domain stability analysis, a quantitative evaluation of the multivariable coupled control system of the wind turbine PLC is achieved, and the mutual influence strength between the three control mechanisms of pitch, yaw, and power can be identified. When the coupling index... When the value exceeds 0.3, the generalized Nyquist stability criterion analysis is triggered, which effectively predicts the stability risk of the system under complex operating conditions.

[0012] As a preferred embodiment of the wind turbine main control PLC detection method of the present invention, the step of combining the gradual excitation signal and the sudden excitation signal includes: The gradual excitation signal is generated using a linear ramp function, and its mathematical expression is: ; in, As the initial value, The slope coefficient is used; the sudden excitation signal adopts a step function, and the amplitude is set according to the steady-state gain of the system. A gradual excitation signal is generated and input into the PLC under test. When the output of the PLC under test is stable, a sudden excitation signal is superimposed. After the abrupt excitation signal ends, a gradual excitation signal is input to form a combined test sequence; By adjusting the rate of change of the gradual excitation signal and the jump amplitude of the abrupt excitation signal, combined with the aforementioned combined test sequence, combined excitation modes of different intensities can be constructed. The PLC under test was tested sequentially using the combined excitation modes of different intensities, and the control mechanism response characteristics of the PLC under test were recorded.

[0013] The beneficial effects of this preferred technical solution are as follows: the timing combination of gradual excitation and sudden excitation simulates the real operating condition change mode in wind turbine operation; the construction of excitation modes with different intensities enables a comprehensive evaluation of the adaptability of the control mechanism under various operating conditions and discovers potential problems that cannot be exposed by a single excitation method.

[0014] As a preferred embodiment of the wind turbine main control PLC detection method described in this invention, the step of analyzing the response characteristics of the control mechanism includes: The response time and control accuracy of the PLC under test were measured in the gradual excitation stage and the sudden excitation stage, respectively. Calculate the control output fluctuation amplitude of the PLC under test at the instant of excitation signal switching; The response time, control accuracy, and output fluctuation amplitude are compared with preset control mechanism performance benchmark values; When any parameter deviates from the baseline value by more than a preset threshold, it is determined that the control mechanism is abnormal.

[0015] The beneficial effects of this preferred technical solution are: the combination of phased response characteristic measurement and instantaneous fluctuation analysis during switching comprehensively captures the dynamic performance of the control mechanism.

[0016] As a preferred embodiment of the wind turbine main control PLC detection method described in this invention, the in-depth diagnosis of control mechanism anomalies includes: For abnormal control mechanisms, activate the corresponding control loop separately and shield interference from other control loops; Input a standard step signal into the control loop and measure its dynamic response parameters; The dynamic response parameters are compared with the factory standard parameters of this PLC model to determine their compatibility. The performance degradation rate of the control mechanism is determined based on the matching degree calculation results.

[0017] This invention provides a fan main control PLC detection system.

[0018] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wind turbine main control PLC detection system, comprising: The test controller is used to establish an Ethernet communication connection with the PLC under test, detect the device information of the PLC under test, and generate a test parameter configuration table. The signal generation module is used to generate digital test signals, analog test signals, and communication test data according to the test parameter configuration table. The wind turbine simulation module is used to build a dynamic simulation model of the wind turbine and generate characteristic signals of wind turbine operation; The data acquisition module is used to collect interface response data and control mechanism response data of the PLC under test. The analysis and processing module is used to analyze and process the collected data, establish the interaction and influence matrix between control mechanisms, and perform stability analysis. The human-computer interaction interface is used to display the test results of the interface functions and the verification results of the control logic.

[0019] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the wind turbine main control PLC detection method.

[0020] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the wind turbine main control PLC detection method.

[0021] The beneficial effects of this invention are: By organically combining interface function testing with wind turbine control logic verification, an integrated testing system has been formed. This system not only verifies the basic hardware functions of the PLC but also evaluates its control performance in a real control environment, achieving a deep integration of hardware testing and software verification. This combined approach allows the testing process to uncover potential problems that cannot be detected by individual tests, especially anomalies that may occur due to the interaction between interface performance and control algorithms.

[0022] By employing a combination of gradual and abrupt excitation signals, the complex wind condition changes encountered by wind turbines in actual operation were creatively simulated. This combined excitation method not only tests the PLC's tracking capability under stable operating conditions but also evaluates its rapid response characteristics under sudden operating conditions. The organic combination of the two excitation methods produced unexpected test results, comprehensively revealing the true performance of the control mechanism during dynamic operating condition transitions.

[0023] The introduction of a dynamic simulation model for wind turbines achieves a high degree of consistency between the testing environment and the actual operating environment. By modulating signals through the simulation model, the test signals more closely resemble actual wind conditions. Compared with traditional static signal testing, this simulation-based testing method can capture subtle changes in the control system during dynamic response, providing a more reliable technical foundation for accurately evaluating PLC performance. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Fig. 1 The above is a flowchart of a wind turbine main control PLC detection method provided in one embodiment of the present invention.

[0026] Fig. 2 This is a schematic diagram of a combination of gradual excitation and sudden excitation signals for a wind turbine main control PLC detection method provided in one embodiment of the present invention.

[0027] Fig. 3 This is a structural diagram of a fan main control PLC detection system provided in one embodiment of the present invention. Detailed Implementation

[0028] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0029] Example 1, referring to Figs. 1-2 This is one embodiment of the present invention, which provides a method for detecting a wind turbine main control PLC, including the following steps S1 to S5: S1. Establish an Ethernet communication connection between the test controller and the PLC under test; In this embodiment, the test controller is an industrial-grade embedded controller with dual network interface cards (NICs). The main NIC's IP address is set to 192.168.1.100, and the subnet mask is 255.255.255.0. The PLC under test is connected to the test controller via a standard RJ45 network cable. The connection topology uses a point-to-point direct connection to avoid communication delays and interference that may be introduced by network switching equipment.

[0030] The test controller first executes the network initialization program to check the network card hardware status and network connectivity. It scans the network segment from 192.168.1.1 to 192.168.1.254 by sending ICMP ping packets to find the IP address of the PLC under test. Upon detecting a response from the PLC, the test controller records its IP address and performs a TCP connection test to verify the reachability of port 502 (the default Modbus TCP port).

[0031] To ensure communication stability, the test controller is configured with the following TCP connection parameters: connection timeout of 5 seconds, read / write timeout of 3 seconds, and a maximum of 3 retries. The test controller also monitors network quality parameters, including round-trip time, packet loss rate, and connection stability. If the network quality does not meet the test requirements, the user will be prompted to check the network connection.

[0032] S2. Detect the manufacturer information, model information and communication protocol information of the PLC under test, and generate a test parameter configuration table based on the identification results; In this embodiment, the steps for generating the test parameter configuration table include S2.1 to S2.6: S2.1 Send a standardized device description request; The test controller sends standardized device description requests to the PLC under test via the established Ethernet connection. First, it attempts to send a Modbus TCP function code 43 (Read Device Identifier) ​​request to read basic device information such as object ID 0x00 (manufacturer name), 0x01 (product code), and 0x02 (major version number). If the PLC supports the EtherNet / IP protocol, it sends a CIP (Common Industrial Protocol) Get Attribute Single service request targeting IdentityObject (category code 0x01) to read information such as manufacturer ID, device type, and product code. Simultaneously, the test controller also sends an OPC UA Browse service request to attempt to obtain detailed description information of the device nodes.

[0033] S2.2. Parse the structured device description information corresponding to the device description request returned by the PLC under test; After receiving the response data from the PLC, the test controller parses the data according to the protocol type. For Modbus TCP responses, it parses the return data of function code 43 to extract the manufacturer name string, product code, and version information. For EtherNet / IP responses, it parses the CIP-encapsulated data packet and extracts the manufacturer ID (e.g., 0x0800 for Schneider Electric), device type, product code, and version information from the Identity Object's attributes. For OPC UA responses, it parses the XML-formatted node description information to obtain the device's detailed specifications. The test controller employs a multi-protocol parallel query approach to improve the success rate and completeness of device information acquisition.

[0034] S2.3 Extract the manufacturer information, model information, and hardware configuration information of the PLC under test from the structured equipment description information; From the parsed structured data, the test controller extracts key information and performs standardization. Manufacturer information is identified by manufacturer ID or manufacturer name string, such as "Schneider Electric", "Siemens", "ABB", etc. Model information is extracted from product code and model string, such as "M580", "S7-1500", "AC500-eCo", etc. Hardware configuration information includes parameters such as the number of digital input / output points (e.g., 32DI / 16DO), the number of analog input / output points (e.g., 8AI / 4AO), communication interface type (e.g., 2 Ethernet ports, 1 serial port), memory capacity, and processor type.

[0035] S2.4. Based on the manufacturer and model information, obtain the corresponding fan technical parameters from the built-in fan main control PLC parameter database; The test controller's built-in wind turbine main control PLC parameter database stores technical parameters for various wind turbines, indexed by manufacturer and model. Taking the Schneider M580 PLC as an example, the wind turbine technical parameters matched in the database include: applicable wind turbine power rating (1.5MW-3.0MW), pitch system type (electric pitch), rated wind speed (12m / s), cut-in wind speed (3m / s), cut-out wind speed (25m / s), pitch angle range (0-90°), yaw accuracy requirement (±5°), power control mode (maximum power point tracking + constant power control), and other key parameters.

[0036] S2.5. Determine the interface test parameters through the hardware configuration information, and establish a control logic verification parameter set through the wind turbine technical parameters; Based on the hardware configuration information, the test controller determines the interface test parameters: the digital interface test voltage is 24VDC, and the test frequency range is 1Hz-1000Hz; the analog interface test range is 4-20mA / 0-10VDC, with an accuracy requirement of 0.1%; the communication interface test data transmission rate is 100Mbps, supporting Modbus TCP and EtherNet / IP protocols. Based on the wind turbine technical parameters, a control logic verification parameter set is established: wind speed signal range is 3-25m / s, pitch angle control range is 0-90°, yaw angle range is ±180°, power control range is 0-3000kW, and the response time requirements are pitch <2s, yaw <10s, and power adjustment <1s.

[0037] S2.6 Integrate the interface test parameters and control logic verification parameter set to generate a test parameter configuration table; The test controller integrates interface test parameters and control logic verification parameters into a structured test parameter configuration table, stored in JSON format. The configuration table comprises three main parts: basic device information, interface test parameters, and control verification parameters. The basic device information section records the PLC manufacturer, model, and hardware / software version; the interface test parameters section includes the test voltage, frequency, and accuracy requirements for various interfaces; and the control verification parameters section includes simulation model parameters, excitation signal range, and control algorithm baseline values. The generated configuration table is automatically saved and displayed on the human-machine interface, allowing users to view and verify the rationality of the test parameters.

[0038] S3. Perform interface function tests on the PLC under test, including digital input / output interface tests, analog input / output interface tests, and communication interface tests. In this embodiment, the steps for performing the interface function test of the PLC under test include S3.1 to S3.4: S3.1 According to the test parameter configuration table, input test signals to the digital interface of the PLC under test and detect its output response; The test controller is configured with a digital output module to send test signals to the digital input interface of the PLC under test according to the 24VDC voltage level in the test parameter configuration table. First, a static level test is performed by inputting a high level (24V) and a low level (0V) to each of the PLC's DI1-DI32 channels, maintaining each state for 3 seconds. The corresponding digital status register inside the PLC is read through the communication interface to verify the correctness of the signal recognition. Next, a dynamic response test is performed by inputting a 10Hz square wave signal to channel DI1 for 10 seconds, monitoring the PLC's response delay time. Under normal circumstances, the response time should be less than 10ms.

[0039] For digital output interface testing, the test controller sends control commands to the PLC via the communication interface to control the high and low levels of each channel (DO1-DO16). A digital multimeter is used to measure the actual output voltage value. Taking channel DO1 as an example, when the PLC outputs a high level, the measured voltage should be 24V±1V; when the output is low, the voltage should be less than 2V. Simultaneously, the load capacity is tested by connecting a 200mA standard load to the DO1 output terminal to verify the stability of the output voltage; the voltage drop should be less than 1V.

[0040] S3.2 Input different voltage values ​​into the analog interface of the PLC under test and test its output accuracy; The test controller is equipped with a precision voltage and current source to send standard test signals to the analog input interface of the PLC under test. For voltage-type analog inputs (0-10V), five voltage points—0V, 2.5V, 5V, 7.5V, and 10V—are input sequentially, with each point held for 5 seconds to stabilize. The A / D conversion value inside the PLC is then read through the communication interface. Taking a 5V input as an example, the corresponding digital value inside the PLC should be approximately 50% of 32767, or 16384, with an allowable error range of ±33 (corresponding to 0.1% accuracy). For current-type analog inputs (4-20mA), five current points—4mA, 8mA, 12mA, 16mA, and 20mA—are input sequentially for similar testing.

[0041] For analog output interface testing, the test controller sends D / A control commands to the PLC via the communication interface, controlling each channel (AO1-AO4) to output a specified value. A digital multimeter is used to measure the actual output. When a 50% full-scale command is sent to the PLC, the output channel for 0-10V should measure 5V ± 0.01V, and the output channel for 4-20mA should measure 12mA ± 0.02mA. Simultaneously, a linearity test is performed, outputting in 10% steps within the 0-100% range. The actual measured values ​​at each point are recorded, and the linearity error is calculated, requiring it to be less than 0.2%.

[0042] S3.3 Send test data to the communication interface of the PLC under test and verify whether the communication is normal; The test controller performs a comprehensive communication performance test on the Ethernet communication interface of the PLC under test. First, a basic connectivity test is performed by sending a Modbus TCP read holding register instruction (function code 03) to read the PLC's internal system status register and verify the correctness of the communication protocol. Then, a data transmission rate test is performed by continuously sending 1000 read / write instructions, each containing 100 register data points. The average response time and data throughput are calculated. Under normal circumstances, the single read / write response time should be less than 50ms, and the data transmission rate should reach at least 10Mbps.

[0043] Communication stability tests were conducted, continuously sending read and write commands at 10 times per second for 30 minutes, and recording the communication success rate and error types. Network latency changes and packet loss were monitored during the test, and any communication anomalies and recovery times were recorded. For PLCs supporting redundant communication, the switching function of the primary and backup communication links was also tested, simulating a primary link failure to verify the automatic switching time and data continuity of the backup link.

[0044] S3.4 Record the test results for each interface; The test controller records detailed data from all interface tests in the test report database. Digital interface test results include: level recognition accuracy for each channel (100% for all 32 input channels), response latency (average 6ms), and output voltage accuracy (24.1V high, 0.2V low). Analog interface test results include: A / D conversion accuracy (maximum error 0.08%), D / A output linearity (0.15%), and temperature drift coefficient (50ppm / ℃). Communication interface test results include: protocol compatibility (supports Modbus TCP and EtherNet / IP), average response time (35ms), 30-minute stability test success rate (99.9%), and maximum data transmission rate (15Mbps).

[0045] All test data is stored in a structured format, including fields such as test timestamp, test conditions, measured values, and judgment results. Interface functional test reports are generated, displaying various performance indicators in chart form and comparing them with technical specifications. Any anomalies discovered during testing are recorded in detail, including the anomaly type, time of occurrence, scope of impact, and possible causes.

[0046] S4. Verify the wind turbine control logic by inputting wind turbine operating characteristic signals to verify the accuracy of the pitch control mechanism, yaw control mechanism and power control mechanism of the PLC under test. The steps for verifying the wind turbine control logic include S4.1 to S4.4: S4.1 Establish a dynamic simulation model of the wind turbine based on the control logic verification parameter set in the test parameter configuration table; Based on the wind turbine technical parameters in the configuration table, the test controller establishes a comprehensive simulation model encompassing the aerodynamic characteristics of the wind turbine, the dynamics of the transmission chain, and the electromagnetic characteristics of the generator. Taking a 1.5MW wind turbine as an example, the wind turbine model employs blade element momentum theory, inputting blade geometric parameters (blade length 35m, chord length distribution, torsion angle distribution) and an aerodynamic coefficient database to establish a nonlinear mapping relationship between wind speed, wind turbine torque, and power. The transmission chain model considers factors such as gearbox transmission ratio (1:97), bearing friction, and elastic coupling to establish the power transmission equation from the low-speed shaft to the high-speed shaft. The generator model is based on the mathematical model of a permanent magnet synchronous generator, including the dq-axis inductance parameters, flux linkage coefficient, and torque constant.

[0047] The simulation model runs on a real-time simulation platform, employing numerical integration calculations with a fixed step size of 0.01s. Model inputs include wind speed, wind direction, pitch angle commands, and yaw angle commands. Outputs include physical quantities such as generator speed, output power, nacelle vibration, and tower load. The simulation model also integrates theoretical algorithms for the wind turbine control system as a benchmark, including an optimal pitch angle lookup table, a yaw-to-wind algorithm, and a maximum power point tracking algorithm.

[0048] S4.2. The wind turbine operating characteristic signal is generated by combining gradual excitation signal and sudden excitation signal, and the signal is modulated by the wind turbine dynamic simulation model. The steps for combining gradual and abrupt excitation signals include A1 to A4: It is important to know that the gradual excitation signal uses a linear ramp function. The mathematical expression for generation is: ; in, As the initial value, The slope coefficient is used; the sudden excitation signal adopts a step function, and the amplitude is set according to the steady-state gain of the system.

[0049] A1. Generate a gradual excitation signal and input it into the PLC under test. When the output of the PLC under test is stable, superimpose the sudden excitation signal. The test controller first generates a wind speed gradient excitation signal, starting from an incoming wind speed of 3 m / s and linearly increasing at a rate of 0.2 m / s per second to the rated wind speed of 12 m / s, lasting for 45 seconds. Simultaneously, a wind direction gradient excitation signal is generated, slowly changing from due north (0°) to northeast (45°) at a rate of 1° per second, lasting for 45 seconds. The corresponding generator speed, output power, and other parameters are calculated using a dynamic simulation model of the wind turbine, and these signals are sent to the corresponding input channels of the PLC under test via an analog output module.

[0050] The test controller continuously monitors the PLC's pitch angle output, yaw angle output, and power control output. When the change in each output is less than 0.1% within 5 consecutive seconds, it is considered to be in a stable output state. Taking pitch control as an example, when the wind speed reaches 12m / s, the PLC should output a pitch angle of 15°. At this time, the detected pitch angle output is stable at 15.2±0.1°.

[0051] A2. After the sudden excitation signal ends, continue to input the gradual excitation signal to form a combined test sequence; Once the PLC output stabilizes, the test controller immediately overlays a sudden excitation signal. The wind speed signal jumps instantaneously from 12 m / s to 18 m / s, simulating a sudden gust; the wind direction signal jumps instantaneously from 45° to 90°, simulating a rapid change in wind direction. The sudden excitation lasts for 10 seconds and then ends, followed by a gradual excitation signal. The wind speed decreases from 18 m / s to 8 m / s at a rate of 0.3 m / s per second, and the wind direction returns from 90° to 30° at a rate of 2° per second.

[0052] The entire combined test sequence forms a complete test process of "gradual change (45s) → sudden change (10s) → gradual change (30s)," totaling 85 seconds. The PLC should respond quickly during the sudden change excitation phase, adjusting the pitch angle from 15.2° to 8.5° and the yaw angle from 45° to 90°, with response times of 1.8 seconds and 8.2 seconds, respectively.

[0053] A3. By adjusting the rate of change of the gradual excitation signal and the jump amplitude of the sudden excitation signal, and combining the combined test sequence, a combined excitation mode with different intensities can be constructed. The test controller was configured with three combined excitation modes of different intensities. The mild excitation mode featured a gradual change in velocity of 0.1 m / s and a sudden change in velocity of 3 m / s; the moderate excitation mode featured a gradual change in velocity of 0.2 m / s and a sudden change in velocity of 6 m / s; and the strong excitation mode featured a gradual change in velocity of 0.5 m / s and a sudden change in velocity of 10 m / s. The wind direction excitation intensity was also adjusted accordingly for each mode: the mild mode featured a gradual change in wind direction of 0.5° per second and a sudden change of 15°; the moderate mode featured a gradual change in wind direction of 1° per second and a sudden change of 30°; and the strong mode featured a gradual change in wind direction of 2° per second and a sudden change of 60°.

[0054] A4. Test the PLC under test by using the different combined excitation modes of the above-mentioned intensity in sequence, and record the control mechanism response characteristics of the PLC under test under various excitation intensities. The test controller executed three excitation modes sequentially: mild, moderate, and strong, with a 5-minute interval between each mode to ensure a complete PLC reset. In mild excitation mode, the PLC's pitch control response time was 2.1 seconds, with a control accuracy error of 0.3°; in moderate excitation mode, the response time was 1.8 seconds, with a control accuracy error of 0.5°; and in strong excitation mode, the response time was 1.5 seconds, with a control accuracy error of 0.8°.

[0055] The analysis steps for the response characteristics of the control mechanism include A4.1 to A4.4: A4.1 Measure the response time and control accuracy of the PLC under test in the gradual excitation stage and the sudden excitation stage respectively; During the gradual excitation phase, the test controller recorded the PLC's pitch control response as the wind speed gradually increased from 8 m / s to 12 m / s. When the wind speed reached 10 m / s, the theoretically optimal pitch angle should be 25°, and the PLC actually output 25.3°, with a control accuracy error of 0.3°. The time from when the PLC received the wind speed signal to when the pitch angle output reached 90% of the target value was 1.85 seconds, meaning the response time was 1.85 seconds. During the sudden excitation phase, the wind speed jumped instantaneously from 12 m / s to 18 m / s. The theoretically optimal pitch angle should have been adjusted from 15° to 5°, and the PLC actually output 5.7°, with a control accuracy error of 0.7°. The time from when the sudden excitation signal was input to when the pitch angle reached 90% of the new target value was 1.42 seconds.

[0056] Measurement results of the yaw control mechanism show that during the gradual excitation phase, when the wind direction gradually changes from 30° to 60°, the PLC yaw angle output is adjusted from 30.5° to 59.2°, with a control accuracy error of 0.8° and a response time of 7.3 seconds. During the sudden excitation phase, when the wind direction jumps instantaneously from 60° to 90°, the PLC yaw angle is adjusted from 59.2° to 89.1°, with a control accuracy error of 0.9° and a response time of 6.8 seconds.

[0057] A4.2 Calculate the control output fluctuation amplitude of the PLC under test at the instant of excitation signal switching; At the instant of switching from gradual excitation to sudden excitation (at 45 seconds), the pitch control output fluctuated from 15.2° to 13.8° and then returned to the target value of 5.7° within 0.2 seconds, with the maximum fluctuation amplitude being [missing value]. The yaw control output fluctuated from 45.3° to 42.1° within 0.5 seconds before adjusting back to the target value of 89.1°, with a maximum fluctuation range of [missing value]. The power control output fluctuated from 1.5MW to 1.38MW during a sudden change before stabilizing at the target value of 2.8MW, with a maximum fluctuation range of [missing value]. .

[0058] A4.3 Compare the response time, control accuracy, and output fluctuation amplitude with the preset control mechanism performance benchmark values; The test controller compares the measurement results with preset control mechanism performance benchmark values. Pitch control benchmark values: response time ≤ 2.0 seconds, control accuracy ≤ ±1.0°, output fluctuation amplitude ≤ 2.0°; measured values: response time 1.42-1.85 seconds, control accuracy ±0.3-0.7°, output fluctuation amplitude 1.4°, all meeting the benchmark requirements. Yaw control benchmark values: response time ≤ 10.0 seconds, control accuracy ≤ ±2.0°, output fluctuation amplitude ≤ 5.0°; measured values: response time 6.8-7.3 seconds, control accuracy ±0.8-0.9°, output fluctuation amplitude 3.2°, all meeting the benchmark requirements.

[0059] A4.4 When any parameter deviates from the benchmark value by more than the preset threshold, it is determined that the control mechanism is abnormal; Since all measured parameters are within the preset threshold range, it is determined that the pitch control mechanism, yaw control mechanism, and power control mechanism are all functioning normally. A green status indicator is then generated, displaying "Control mechanism performance is normal" on the human-machine interface. If, in a test, the yaw control response time is detected to be 12.5 seconds, exceeding the 25% threshold of the baseline value of 10.0 seconds, a control mechanism anomaly alarm will be automatically triggered, and a deep diagnostic procedure will be initiated.

[0060] It is also important to know that in-depth diagnosis of control mechanism abnormalities includes B1 to B4: B1. For abnormal control mechanisms, activate the corresponding control loop separately and shield the interference from other control loops; When a yaw control anomaly is detected, the test controller sends control commands to the PLC via the communication interface, setting the pitch control loop to manual mode and locking it at the current angle of 15°, and setting the power control loop to constant output mode and locking it at 1.5MW. Only the yaw control loop remains in automatic mode to ensure that other control loops do not couple into the yaw control. Simultaneously, input signals that may affect yaw control, such as wind speed and power, are disconnected, leaving only the wind direction signal input.

[0061] B2. Input a standard step signal into the control loop and measure its dynamic response parameters; The test controller inputs a standard 30° step signal into the yaw control loop, meaning the wind direction signal jumps instantaneously from the current 60° to 90°. An encoder is used to monitor the yaw angle change in real time, with a sampling frequency of 100Hz. The measured dynamic response parameters include: delay time (time from the input step signal to the start of output change) of 0.8 seconds, rise time (time from 10% to 90% of the output) of 8.5 seconds, settling time (time from the start of the step response to entering the ±2% error band) of 15.2 seconds, overshoot (percentage of maximum overshoot to steady-state value) of 6.8%, and steady-state error (deviation of steady-state output value from the target value) of 1.3°.

[0062] B3. Perform a matching degree analysis between the dynamic response parameters and the factory standard parameters of this PLC model; Obtain the factory standard parameters for the yaw control loop of this PLC model from the technical documentation provided by the PLC manufacturer: delay time 0.5 seconds, rise time 6.0 seconds, settling time 10.0 seconds, overshoot 5.0%, and steady-state error 0.5°. Calculate the deviation of each parameter: Delay time deviation Rise time deviation Adjusting time deviation Overshoot deviation Steady-state error deviation .

[0063] B4. Determine the performance degradation degree of the control mechanism based on the matching degree calculation results; The overall matching degree was calculated using a weighted average method, with the following weights for each parameter: delay time 15%, rise time 25%, settling time 30%, overshoot 15%, and steady-state error 15%.

[0064] Overall matching degree is According to the performance degradation classification standard: a matching degree >80% indicates slight degradation, 50%-80% indicates moderate degradation, and <50% indicates severe degradation. The matching degree of this yaw control mechanism is 30.9%, which is considered severe performance degradation. Immediate shutdown and inspection of the yaw motor and reducer are recommended.

[0065] S4.3. Input the wind turbine operating characteristic signals into the pitch control input, yaw control input, and power control input of the PLC under test, respectively, and simultaneously collect the response data of each control output. For the three subsystems of pitch control, yaw control, and power control, apply a unit step excitation to each control input and measure the corresponding output change. Construct a three-by-three interaction matrix M using the formula, specifically expressed as: ; in, Let be the gain representing the relative influence of the j-th control input on the i-th control output. Let i be the change in the i-th output. For the change of the j-th input, and These are the corresponding nominal values; In this embodiment, the test controller simultaneously inputs the composite signal modulated by the wind turbine dynamic simulation model into three control input channels of the PLC under test: the wind speed signal is input to the pitch control analog input AI1 (corresponding to pitch control), the wind direction signal is input to the yaw control analog input AI2 (corresponding to yaw control), and the power demand signal is input to the power control analog input AI3 (corresponding to power control). The test controller acquires the three control outputs of the PLC in real time through the Ethernet communication interface: pitch angle output AO1, yaw angle output AO2, and power control output AO3, with the sampling frequency set to 100Hz to ensure that the dynamic response process of the control system can be captured.

[0066] To construct the interaction matrix M, the test controller employs a sequential excitation method to perform unit step tests on the three control inputs. First, a unit step excitation is applied to the pitch control input: the wind speed signal is instantaneously changed from its current steady-state value of 10 m / s to 11 m / s. Keeping the other two input signals constant, output data was continuously collected for 60 seconds. The measurement results showed that the pitch angle output changed from 18.5° to 16.2°. The yaw angle output changed from 45.0° to 45.1°. The power control output changed from 1.8MW to 2.1MW. ).

[0067] Next, a unit step excitation is applied to the yaw control input: the wind direction signal is changed from the current steady-state value of 60° to 61°. While maintaining constant wind speed and power demand signals, the measurement results show that the pitch angle output changed from 16.2° to 16.3°. The yaw angle output changed from 45.1° to 46.1°. The power control output changed from 2.1MW to 2.15MW. ).

[0068] Finally, a unit step excitation is applied to the power control input: the power demand signal is jumped from the current steady-state value of 2.0MW to 2.1MW. While keeping wind speed and direction signals constant, the measurement results show that the pitch angle output changed from 16.3° to 15.8°. The yaw angle output changed from 46.1° to 46.0°. The power control output changed from 2.15MW to 2.25MW. ).

[0069] Based on the test data, the nominal values ​​were normalized as follows: U1,nom = 12m / s (rated wind speed), U2,nom = 180° (yaw range), U3,nom = 3.0MW (rated power); Y1,nom = 45° (pitch midpoint angle), Y2,nom = 180° (yaw range), Y3,nom = 3.0MW (rated power).

[0070] Next, the elements of the interaction effect matrix are calculated according to the formula, and are expressed as follows: , which represents the effect of pitch input on pitch output; This represents the effect of yaw input on pitch output. This includes the impact of power input on pitch output, etc.

[0071] The final 3×3 interaction matrix M is obtained: ; The matrix reflects the coupling relationship between the three control subsystems of the wind turbine PLC: the diagonal elements M11, M22, and M33 represent the main control gains of each control loop; the off-diagonal elements represent the cross-coupling strength, where M32 = 3.0 indicates a strong coupling effect of yaw control on power output, because yaw error directly affects the effective wind energy received by the wind turbine. The test controller stores this interaction matrix in the test database to provide basic data for subsequent coupling analysis and stability assessment.

[0072] S4.4 Calculate the multivariate coupling evaluation index based on the interaction influence matrix M. ,when A value greater than 0.3 indicates a strongly coupled system, and the calculation formula is as follows: ; For strongly coupled systems, the interaction influence matrix is... As the steady-state gain matrix of the system, the frequency domain transfer function matrix is ​​constructed by combining the dynamic characteristic parameters of each control loop of the PLC under test. and The interaction influence matrix M is and The value at zero frequency, through stability analysis using the generalized Nyquist stability criterion, is expressed mathematically as follows: ; In the formula, Let be the determinant of the matrix. It is the identity matrix. Let be the transfer function matrix of the wind turbine controlled object. The transfer function matrix of the PLC controller. For complex frequency domain variables, This holds true for all frequencies; The system is considered unstable if there exists any frequency point where the determinant is equal to zero, and the system is considered stable if the determinant of all frequency points is not equal to zero. In this embodiment, the test controller calculates the multivariate coupling evaluation index according to the formula based on the interaction influence matrix M constructed in step S4.3. First, the sum of the absolute values ​​of the off-diagonal elements is calculated: Then calculate the sum of the absolute values ​​of the diagonal elements: .

[0073] Therefore, the coupling degree evaluation index A value greater than the threshold of 0.3 indicates that the PLC control system of the wind turbine is a strongly coupled system, requiring further stability analysis. The test controller displays the status message "Strongly coupled system detected, CI = 1.897, stability analysis in progress..." on the human-machine interface.

[0074] For strongly coupled systems, the test controller uses the interaction matrix M as the system's steady-state gain matrix, and constructs the frequency domain transfer function matrix by combining it with the dynamic characteristic parameters of each control loop obtained from the PLC technical documentation. The transfer function of the pitch control loop is a first-order inertial element. ,in , The transfer function of the yaw control loop is a second-order underdamped element. ,in , , The transfer function of the power control loop is an integral plus an inertial element. ,in , .

[0075] Transfer function matrix of the wind turbine controlled object Identified through simulation modeling. The pitch control object is... The yaw control object is The power control object is The cross-coupling term was fitted using measured data: , , , , , .

[0076] The test controller constructs a complete frequency domain transfer function matrix. And sample 1000 frequency points at logarithmic intervals within the frequency range of 0.01Hz to 100Hz, and calculate point by point. The value. In At that location, the calculation yielded: ; ; Calculate the determinant of this matrix: Its modulus .

[0077] The test controller performed similar calculations on all 1000 frequency points and found that... The determinant modulus reaches its minimum value of 0.087, but is still greater than zero; Another minimum value of 0.156 appears at point 1. The determinant of all frequency points is not equal to zero, which satisfies the requirements of the generalized Nyquist stability criterion, and the system is determined to be stable.

[0078] Further analysis revealed that although the system was generally stable, The low margin value of 0.087 indicates a potential oscillation risk in the system around this frequency. The test controller calculated the system's minimum singular value margin to be 6.8 dB and the phase margin to be 28.5°, both of which meet the engineering stability requirements, but the phase margin is close to the critical value of 30°.

[0079] The final stability analysis report shows that the system is generally stable under the current control parameters. However, the strong coupling between yaw control and power control (M32 = 3.0) may cause system oscillations under certain operating conditions. It is recommended to add a feedforward decoupling element to the PLC control algorithm or appropriately reduce the gain coefficient of yaw control to improve the robustness and stability of the system. The test controller displays the analysis results in graphical form on the human-machine interface, including frequency response curves, Nyquist plots, and stability margin indices.

[0080] S5. Display the interface function test results and control logic verification results on the human-computer interaction interface.

[0081] Example 2, refer to Fig. 3 As shown, this is an embodiment of the present invention, which provides a wind turbine main control PLC detection system, including: The test controller is used to establish an Ethernet communication connection with the PLC under test, detect the device information of the PLC under test, and generate a test parameter configuration table. The signal generation module is used to generate digital test signals, analog test signals, and communication test data according to the test parameter configuration table.

[0082] The wind turbine simulation module is used to build a dynamic simulation model of the wind turbine and generate characteristic signals of wind turbine operation; The data acquisition module is used to collect interface response data and control mechanism response data of the PLC under test. The analysis and processing module is used to analyze and process the collected data, establish the interaction and influence matrix between control mechanisms, and perform stability analysis. The human-computer interaction interface is used to display the test results of the interface functions and the verification results of the control logic.

[0083] Example 3: This example also provides an electronic device applicable to a wind turbine main control PLC detection method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wind turbine main control PLC detection method proposed in the above examples.

[0084] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a wind turbine main control PLC detection method as proposed in the above embodiment.

[0085] The storage medium proposed in this embodiment and the method for detecting a wind turbine main control PLC proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0086] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the main control PLC of a wind turbine, characterized in that: Includes the following steps: Establish an Ethernet communication connection between the test controller and the PLC under test; The system detects the manufacturer, model, and communication protocol information of the PLC under test and generates a test parameter configuration table based on the identification results. Perform interface function tests on the PLC under test, including digital input / output interface tests, analog input / output interface tests, and communication interface tests; Verify the wind turbine control logic by inputting wind turbine operating characteristic signals to verify the accuracy of the pitch control mechanism, yaw control mechanism and power control mechanism of the PLC under test; The interface function test results and control logic verification results are displayed on the human-computer interaction interface.

2. The method for detecting a wind turbine main control PLC as described in claim 1, characterized in that: The steps for generating the test parameter configuration table include: Send a standardized device description request; Parse the structured device description information corresponding to the device description request returned by the PLC under test; Extract the manufacturer information, model information, and hardware configuration information of the PLC under test from the structured equipment description information; Based on the manufacturer and model information, the corresponding wind turbine technical parameters are obtained by matching from the built-in wind turbine main control PLC parameter database; The interface test parameters are determined using the hardware configuration information, and the control logic verification parameter set is established using the wind turbine technical parameters. The interface test parameters and control logic verification parameter sets are integrated to generate a test parameter configuration table.

3. The method for detecting a wind turbine main control PLC as described in claim 2, characterized in that: The steps for performing a functional test on the PLC interface under test include: According to the test parameter configuration table, input test signals to the digital interface of the PLC under test and detect its output response. Input different voltage values ​​into the analog interface of the PLC under test and test its output accuracy; Send test data to the communication interface of the PLC under test and verify whether the communication is normal; Record the test results for each interface.

4. The method for detecting a wind turbine main control PLC as described in claim 3, characterized in that: The steps for verifying the wind turbine control logic include: A dynamic simulation model of the wind turbine is established based on the control logic verification parameter set in the test parameter configuration table. The wind turbine operating characteristic signal is generated by combining gradual excitation signal and sudden excitation signal, and the signal is modulated by the wind turbine dynamic simulation model. The wind turbine operating characteristic signals are input to the pitch control input, yaw control input, and power control input of the PLC under test, respectively. Response data from each control output is collected synchronously. For the three subsystems of pitch control, yaw control, and power control, a unit step excitation is applied to each control input, and the corresponding output change is measured. A three-by-three interaction matrix M is constructed using the following formula: ； in, Let be the gain representing the relative influence of the j-th control input on the i-th control output. Let i be the change in the i-th output. For the change of the j-th input, and These are the corresponding nominal values; Calculate the multivariate coupling evaluation index based on the interaction matrix M. ,when A value greater than 0.3 indicates a strongly coupled system, and the calculation formula is as follows: ； For strongly coupled systems, the interaction influence matrix is... As the steady-state gain matrix of the system, the frequency domain transfer function matrix is ​​constructed by combining the dynamic characteristic parameters of each control loop of the PLC under test. and The interaction influence matrix M is and The value at zero frequency, through stability analysis using the generalized Nyquist stability criterion, is expressed mathematically as follows: ； In the formula, Let be the determinant of the matrix. It is the identity matrix. Let be the transfer function matrix of the wind turbine controlled object. The transfer function matrix of the PLC controller. For complex frequency domain variables, This holds true for all frequencies; The system is considered unstable if there exists any frequency point where the determinant is equal to zero, and stable if the determinant of all frequency points is not equal to zero.

5. The method for detecting a wind turbine main control PLC as described in claim 4, characterized in that: The step of combining the gradual excitation signal and the abrupt excitation signal includes: The gradual excitation signal uses a linear ramp function. The mathematical expression for generation is: ； in, As the initial value, The slope coefficient is used; the sudden excitation signal adopts a step function, and the amplitude is set according to the steady-state gain of the system. A gradual excitation signal is generated and input into the PLC under test. When the output of the PLC under test is stable, a sudden excitation signal is superimposed. After the abrupt excitation signal ends, a gradual excitation signal is input to form a combined test sequence; By adjusting the rate of change of the gradual excitation signal and the jump amplitude of the abrupt excitation signal, combined with the aforementioned combined test sequence, combined excitation modes of different intensities can be constructed. The PLC under test was tested sequentially using the combined excitation modes of different intensities, and the control mechanism response characteristics of the PLC under test were recorded.

6. The method for detecting a wind turbine main control PLC as described in claim 5, characterized in that: The analysis steps for the response characteristics of the control mechanism include: The response time and control accuracy of the PLC under test were measured in the gradual excitation stage and the sudden excitation stage, respectively. Calculate the control output fluctuation amplitude of the PLC under test at the instant of excitation signal switching; The response time, control accuracy, and output fluctuation amplitude are compared with preset control mechanism performance benchmark values; When any parameter deviates from the baseline value by more than a preset threshold, it is determined that the control mechanism is abnormal.

7. The method for detecting a wind turbine main control PLC as described in claim 6, characterized in that: In-depth diagnosis of control mechanism abnormalities includes: For abnormal control mechanisms, activate the corresponding control loop separately and shield interference from other control loops; Input a standard step signal into the control loop and measure its dynamic response parameters; The dynamic response parameters are compared with the factory standard parameters of this PLC model to determine their compatibility. The performance degradation rate of the control mechanism is determined based on the matching degree calculation results.

8. A wind turbine main control PLC testing system, using the wind turbine main control PLC testing method as described in any one of claims 1 to 7, characterized in that, include: The test controller is used to establish an Ethernet communication connection with the PLC under test, detect the device information of the PLC under test, and generate a test parameter configuration table. The signal generation module is used to generate digital test signals, analog test signals, and communication test data according to the test parameter configuration table. The wind turbine simulation module is used to build a dynamic simulation model of the wind turbine and generate characteristic signals of wind turbine operation; The data acquisition module is used to collect interface response data and control mechanism response data of the PLC under test. The analysis and processing module is used to analyze and process the collected data, establish the interaction and influence matrix between control mechanisms, and perform stability analysis. The human-computer interaction interface is used to display the test results of the interface functions and the verification results of the control logic.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the wind turbine main control PLC detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind turbine main control PLC detection method according to any one of claims 1 to 7.

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