Offshore wind turbine generator stability analysis method and device based on operating parameters and medium

Abstract

The invention provides an offshore wind turbine generator stability analysis method and device based on operation parameters and a medium, and belongs to the technical field of new energy. The technical problem that the operation stability of the offshore wind turbine generator set cannot be effectively monitored due to the fact that the oscillation risk is difficult to identify in real time in a weak power grid environment is solved. According to the technical scheme, the method comprises the steps that S1, electrical and mechanical parameters generated in the operation process of the offshore wind turbine generator are collected; s2, the electrical and mechanical parameters are preprocessed; s3, constructing a dynamic mapping model used for representing the wind turbine generator and the oscillation response; and S4, obtaining an integral result according to the oscillation amplitude and the oscillation energy in the time window. The method has the beneficial effects that the potential unstable operation state can be found in time by directly analyzing the operation parameters of the unit, decision support is provided for operation and maintenance personnel, and the operation safety of the offshore wind turbine unit in a weak power grid environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, and particularly relates to a method, device and medium for analyzing stability of an offshore wind turbine based on operation parameters. BACKGROUND

[0002] With the rapid development of renewable energy, offshore wind power has gradually become an important pillar of global energy structure transformation. Due to stable offshore wind resources and high area utilization efficiency, offshore wind power projects are accelerating deployment worldwide. However, offshore wind farms are usually connected to the land grid through long-distance submarine cables, resulting in high grid impedance and weak voltage support capability, forming a typical weak grid environment. Under such conditions, wind turbines are more susceptible to external disturbances and internal control interactions, thereby causing operation stability problems, including subsynchronous oscillation, control system coupling oscillation and high-frequency instability and other complex phenomena. Therefore, the analysis of the operation stability of offshore wind turbines has become one of the key technologies to ensure the safe access and efficient operation of wind power.

[0003] Currently, the analysis of the stability of wind turbines mainly adopts a method based on modeling and simulation, among which the impedance method is the most widely used. This method establishes a frequency domain model of the output impedance of the wind turbine and the equivalent impedance of the grid, analyzes the amplitude and phase relationship between the two at a specific frequency, and judges the stability of the system in combination with the Nyquist criterion. The impedance method can accurately reflect the interaction characteristics between the unit and the grid, and has advantages in studying subsynchronous oscillation and controller interaction. However, this method is highly dependent on model and controller parameters, which is difficult to obtain, and is not suitable for rapid diagnosis and analysis in the field. Another common method is the eigenvalue analysis method, which is based on the linearization model of the state space of the wind power system to solve the eigenvalues, to evaluate the dynamic stability under small disturbances. Although the eigenvalue analysis has a mature theoretical basis and the results are intuitive and clear, it also has the problems of complex modeling, time-consuming calculation, and difficulty in dealing with nonlinear and large disturbance conditions. In addition, the time domain simulation method is also widely used in the research and verification of the dynamic behavior of wind power systems, but it relies on complete simulation platforms and detailed model configurations, and requires large computing resources, making it difficult to realize online application.

[0004] In view of the deficiencies of traditional methods in real-time, model dependence and system adaptability, a kind of stability analysis method based on operating parameters has gradually emerged in recent years. This kind of method does not depend on the detailed model of the unit controller, but directly collects the original data generated by the wind turbine during operation, such as voltage, current, power, speed, torque, etc. Through frequency domain analysis, time-frequency transformation or pattern recognition, etc. Signal processing means, the frequency components representing the dynamic characteristics of the system are extracted, especially the trend of subsynchronous and supersynchronous oscillation frequency band. On this basis, by establishing the mapping relationship between parameter change and oscillation characteristics, the real-time evaluation and early warning of the wind turbine operating state are realized. Compared with traditional methods, the analysis method based on operating parameters is easier to deploy, suitable for various types of units, with good universality and real-time performance, especially suitable for online monitoring and dynamic response management in weak grid environment. Therefore, it has important engineering application value and practical significance to study a method and system based on operating parameters, with oscillation feature extraction capability and stability evaluation function. SUMMARY

[0005] The purpose of the present application is to provide a kind of offshore wind turbine stability analysis method based on operating parameters, equipment and medium, which can find potential unstable operating state in time by directly analyzing the operating parameters of unit, provide decision support for operation and maintenance personnel, and help to improve the operation safety of offshore wind turbine in weak grid environment.

[0006] The invention idea of the present application is: the method comprises: real-time acquisition of the electrical parameters (including voltage, current, power) and mechanical parameters (including speed, torque) of grid-connected wind turbine;Frequency domain analysis method is used to process the collected operating parameters, and the characteristic frequency components of subsynchronous and supersynchronous oscillation are extracted;Based on the extracted oscillation characteristics, a dynamic mapping model between operating parameters and oscillation characteristics is established, and the least square method is used to identify parameters for evaluating the operating stability of unit;When the oscillation energy index exceeds the set threshold, an unstable operating early warning signal is generated. The system includes a data acquisition module, a feature analysis module and a state evaluation module, which work cooperatively to realize real-time monitoring and analysis of the operating state of unit.

[0007] In order to realize the above-mentioned purpose of the application, the technical scheme adopted by the present application is as follows: a kind of offshore wind turbine stability analysis method based on operating parameters, equipment and medium, comprising: The electrical and mechanical parameters generated by the offshore wind turbine during operation are collected, the electrical parameters include three-phase voltage, current, instantaneous active power and reactive power at the grid connection point, and the mechanical parameters include generator speed, torque, pitch angle and yaw angle, etc. The electrical and mechanical parameters are preprocessed, data noise is removed through sliding average filtering, frequency domain decomposition is used to extract subsynchronous oscillation and supersynchronous oscillation characteristic components existing in the operation process of the wind turbine, and the following feature vector set is constructed: (1) Among them, is a subsynchronous frequency component; is a supersynchronous frequency component; A is an oscillation amplitude; is a phase offset; is an oscillation energy; A dynamic mapping model for representing the response between the wind turbine and the oscillation is constructed, and the mapping model is based on the following function relationship: (2) Among them, t is time, is the rotating speed; is the electromagnetic torque; is the phase difference between the generator set and the power grid; is the rotating speed change rate, which represents the mechanical disturbance strength; is the electromagnetic torque change rate, which represents the electrical disturbance response; are the characteristic weight coefficients of the rotating speed change rate, the electromagnetic torque change rate and the phase difference respectively; is a high-frequency residual disturbance; According to the integral results of the oscillation amplitude and the oscillation energy in the time window, the total energy is calculated to obtain the oscillation energy ratio index , and compared with the preset threshold . When , a unit operation instability warning signal is generated, and the warning signal includes: oscillation frequency energy distribution characteristic parameters, oscillation energy change trend, and key operating parameter deviation degree.

[0008] The analysis of the electrical and mechanical parameters includes the following steps: The collected electrical parameter signals and mechanical parameter signals are respectively subjected to sliding average filtering processing, and the filtering window width is N, and the filtered signals are represented as: (3) Among them: is the original electrical parameter signal; is the original mechanical parameter signal; N is the sliding window width; is the sampling interval time, are the filtered electrical and mechanical signals respectively.

[0009] Fast Fourier analysis is performed on the filtered signal to calculate the power spectral density, and the oscillation components are extracted to construct the feature vector: (4) Wherein, is the power spectral density of the electrical parameter signal; is the power spectral density of the mechanical parameter signal; T is the analysis time window length; f is the frequency; j is the imaginary unit; π is the circular constant; are the threshold coefficients of electrical and mechanical parameters, respectively; the value range is 0.05-0.2, and the optimal value can be 0.1.

[0010] The construction of the dynamic mapping model and the parameter identification process are as follows: A dynamic mapping model is constructed to represent the relationship between the wind turbine and the oscillation response, as shown in equation (2); Recursive least squares with forgetting factor are used for parameter identification: First, the initial setting is performed, and the parameter vector initial value is defined ; The covariance matrix initial value is (I is a 3*3 unit matrix); The forgetting factor (taking the value range 0.95~0.99); the optimal value is 0.98.

[0011] The sampling period .

[0012] Real-time update parameters (t moment calculation): k Construct data vector: , Indicates matrix transpose; T Calculate the prior error ; Update the gain matrix ; Parameter estimation update: ; Covariance matrix update: .

[0013] The determination coefficient is used to evaluate the fitting degree of the model: (5) Wherein, is the model prediction value; is the average value of the oscillation amplitude; N is the verification window length, and the determination coefficient ​Otherwise, trigger model reset.

[0014] According to the oscillation amplitude And the window time The integral result of the internal oscillation energy, calculate the oscillation energy ratio index : (6) Wherein, The reference energy reference value is the average value of the oscillation energy under the rated operation state of the unit; The integral time window is 5-10 power frequency cycles, and the oscillation energy is compared with the preset threshold .

[0015] When , generate a unit operation instability early warning signal, the early warning signal includes: Oscillation frequency energy distribution characteristic parameters:

[0016] Wherein, Subsynchronous and supersynchronous frequency band energy.

[0017] Oscillation energy change trend:

[0018] Key operation parameter deviation degree:

[0019] Wherein, The initial value of the speed, torque and phase.

[0020] In order to realize the above-mentioned invention purpose, the application further provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method as any one of the above.

[0021] In order to realize the above-mentioned invention purpose, the application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the method as any one of the above.

[0022] Compared with the prior art, the beneficial effects of the application are: 1. The application provides a method and system for analyzing the stability of offshore wind turbines based on operating parameters, which can effectively extract the subsynchronous and supersynchronous oscillation characteristics that may exist in the system by performing frequency domain analysis on the actual collected electrical parameters (including voltage, current, power) and mechanical parameters (including speed, torque) without relying on detailed controller models of the unit and grid topology information, thereby realizing real-time monitoring and stability evaluation of the operating state of the wind turbine. The method establishes a dynamic mapping model between operating parameters and oscillation behavior, which can accurately reflect the dynamic interaction characteristics during the operation of the unit, and timely generate a warning signal when the oscillation energy index exceeds the set threshold, which helps to identify potential unstable states in advance and improve the operational safety of the wind farm.

[0023] 2. Compared with existing methods based on impedance modeling, eigenvalue calculation or simulation analysis, the application does not require complex modeling process, has higher real-time performance and field adaptability, and is especially suitable for offshore wind farm environments with complex grid conditions and incomplete modeling information. At the same time, the analysis system constructed by the application has high modularization degree, integrates multiple functional units such as data acquisition, feature analysis and state evaluation, and is easy to deploy in the wind farm control center or edge node, realizing unified monitoring and dynamic management of multiple wind turbines, and having good engineering practicability and promotion value. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, which together with the embodiments of the application, is used to explain the application, and does not constitute a limitation on the application.

[0025] Figure 1 The flowchart of the offshore wind turbine stability analysis in the embodiments of the application. DETAILED DESCRIPTION

[0026] 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 herein are only used to explain the application, and do not limit the application.

[0027] Embodiment 1: The technical scheme provided in this embodiment is: a method for analyzing the stability of offshore wind turbines based on operating parameters, comprising: Collecting electrical and mechanical parameters generated during the operation of the offshore wind turbine, the electrical parameters including three-phase voltage, current, instantaneous active power and reactive power at the grid connection point, and the mechanical parameters including generator speed, torque, pitch angle, yaw angle and other state quantities; The electrical and mechanical parameters are preprocessed, data noise is removed through sliding average filtering, frequency domain decomposition is used to extract the subsynchronous oscillation and supersynchronous oscillation characteristic components existing in the operation process of the wind turbine, and the following feature vector set is constructed:

[0028] Among them, is a subsynchronous frequency component; is a supersynchronous frequency component; A is an oscillation amplitude; is a phase shift; is an oscillation energy; A dynamic mapping model for representing the response between the wind turbine and the oscillation is constructed, and the mapping model is based on the following function relationship: (1) Among them, is a rotational speed; is an electromagnetic torque; is a phase difference between the generator set and the power grid; is a rotational speed change rate, which represents the mechanical disturbance strength; is an electromagnetic torque change rate, which represents the electrical disturbance response; are feature weight coefficients of the rotational speed change rate, the electromagnetic torque change rate and the phase difference respectively, which are obtained based on historical operation data; is a high-frequency residual disturbance; According to the integral results of the oscillation amplitude and the oscillation energy in the time window, the oscillation energy ratio index is calculated, and compared with a preset threshold ; When , a unit operation instability warning signal is generated, and the warning signal includes: oscillation frequency energy distribution characteristic parameters, oscillation energy change trend, and key operation parameter deviation degree.

[0029] Embodiment 2: This example is further designed on the basis of embodiment 1, which is to analyze the electrical and mechanical parameters: The collected electrical parameter signals and mechanical parameter signals are respectively processed by sliding average filtering, and the filtering window width is N. The filtered signals are represented as: (2) Among them: is an original electrical parameter signal; is an original mechanical parameter signal; N is a sliding window width; is a sampling interval time; are filtered electrical and mechanical signals, respectively.

[0030] The filtered electrical and mechanical signals are subjected to fast Fourier analysis, power spectral density is calculated, and oscillation components are extracted from it to construct a feature vector: (3) wherein, is the power spectral density of the electrical parameter signal; is the power spectral density of the mechanical parameter signal; T is the analysis time window length; f is the frequency; is the threshold coefficient (value range 0.05-0.2); Example 3: This example is further designed on the basis of Example 2, which is the construction of dynamic mapping model and parameter identification process: A dynamic mapping model is constructed to represent the relationship between the wind turbine and the oscillation response, which is based on the following function relationship, as shown in equation (1); Recursive least squares method with forgetting factor is used for parameter identification: First, the initial setting is performed, and the initial value of the parameter vector is defined ; The initial value of the covariance matrix is (I is a 3*3 unit matrix); The forgetting factor (value range 0.95~0.99); The sampling period .

[0031] Real-time update parameters (k-time calculation): Construct data vector: ; Calculate the prior error ; Update the gain matrix ; Parameter estimation update: ; Covariance matrix update: .

[0032] The coefficient of determination is used to evaluate the fitting degree of the model: (4) wherein, is the model prediction value; is the average value of the oscillation amplitude; N is the verification window length, and the coefficient of determination , otherwise trigger model reset.

[0033] Example 4: This example is further designed on the basis of Example 3: According to the oscillation amplitude With the window time The integral result of the internal oscillation energy, the oscillation energy ratio index is calculated : (5) Wherein, The reference energy reference value is the average value of the oscillation energy in the rated operation state of the unit; The integral time window is 5-10 power frequency cycles, and the oscillation energy is compared with the preset threshold .

[0034] Embodiment 5: This example is further designed on the basis of embodiment four: When , the generator operation instability early warning signal is generated, and the early warning signal includes: Oscillation frequency energy distribution characteristic parameters:

[0035] Wherein, The subsynchronous and supersynchronous frequency band energy.

[0036] Oscillation energy change trend:

[0037] Key operating parameter deviation degree:

[0038] Wherein, The initial value of the speed, torque and phase.

[0039] Embodiment 6: An electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to execute the method according to any one of the preceding embodiments.

[0040] A computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the method according to any one of the preceding embodiments.

[0041] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of offshore wind turbine stability analysis based on operating parameters, characterized in that, The method comprises the following steps: S1, collecting electrical parameters and mechanical parameters generated by the offshore wind turbine during operation, the electrical parameters including three-phase voltage, current, instantaneous active power and reactive power at the grid connection point, and the mechanical parameters including generator speed, torque and pitch angle, yaw angle state variables; S2, preprocessing the electrical parameters and mechanical parameters, removing data noise through moving average filtering, extracting subsynchronous oscillation and supersynchronous oscillation characteristic components existing in the operation of the offshore wind turbine through frequency domain decomposition, and constructing the following feature vector set: （1） wherein is a sub-synchronous frequency component; is a super-synchronous frequency component; A is an oscillation amplitude; is a phase offset; is an oscillation energy; S3, constructing a dynamic mapping model for representing the relationship between the offshore wind turbine and the oscillation response, the mapping model being based on the following function relationship: （2） wherein, is the rotational speed; is the electromagnetic torque; is the phase difference between the generator set and the power grid; is the rotational speed change rate, reflecting the mechanical disturbance strength; is the electromagnetic torque change rate, reflecting the electrical disturbance response; are the characteristic weight coefficients of the rotational speed change rate, the electromagnetic torque change rate and the phase difference, respectively, and are obtained based on historical operation data; is the high-frequency residual disturbance; S4、according to the oscillation amplitude and the integral result of the oscillation energy in the time window, calculate the oscillation energy ratio index , and compare with a preset threshold ; When an unstable operation early warning signal of the offshore wind turbine generator is generated, the early warning signal comprising: an oscillation frequency energy distribution characteristic parameter, an oscillation energy change trend, and a key operation parameter deviation degree.

2. The offshore wind turbine stability analysis method of claim 1, wherein, In the step S1, the analysis of the electrical and mechanical parameters comprises the following steps: S11, performing moving average filtering on the collected electrical parameter signals and mechanical parameter signals respectively, the filtering window width being N, and the filtered signals being represented as: （3） wherein: is the original electrical parameter signal; is the original mechanical parameter signal; N is the sliding window width; is the sampling interval time; are the filtered electrical signal and the filtered mechanical signal, respectively; S12, performing fast Fourier analysis on the filtered electrical signals and mechanical signals, calculating the power spectral density, and extracting the oscillation components to construct the feature vector: （4） wherein; is the power spectral density of the electrical parameter signal; is the power spectral density of the mechanical parameter signal; T is the analysis time window length; f is the frequency; are threshold coefficients for the electrical and mechanical parameters, respectively, with a range of values from 0.05 to 0.

2.

3. The offshore wind turbine stability analysis method of claim 2, wherein, In the step S3, the construction of the dynamic mapping model and the parameter identification process comprise the following steps: S31, constructing a dynamic mapping model for representing the relationship between the offshore wind turbine and the oscillation response, as shown in formula (2); S32, performing parameter identification using a recursive least squares method with a forgetting factor: First, initialization setting is made, and parameter vector initial value is defined ; covariance matrix initial value I is a 3*3 identity matrix; forgetting factor with a value ranging from 0.95 to 0.99; Sampling period ; Updating the parameters at time k in real time: S33, configure the data vector: ; Computing a priori error ; updating the gain matrix ; Parameter estimation update: ; Covariance matrix update: ; Using the determination coefficient to evaluate the fitting degree of the model: （5） wherein, is the model prediction value; is the oscillation amplitude mean value; N is the validation window length, the determination coefficient is required, otherwise the model reset is triggered.

4. The offshore wind turbine stability analysis method of claim 3, wherein: In the step S4, the oscillation amplitude ratio index is calculated according to the oscillation amplitude and the window time and the integral result of the inner oscillation energy : （6） Wherein, is the reference energy reference value, taking the average value of the oscillation energy under the rated operating state of the unit; is the integral time window, the value range is 5~10 power frequency periods, and the oscillation energy is compared with a preset threshold .

5. The offshore wind turbine stability analysis method of claim 4, wherein: In the step S4, When a unit operation instability early warning signal is generated, the early warning signal comprising: Oscillation frequency energy distribution characteristic parameters; （7） wherein, is the sub-synchronous and super-synchronous band energy; oscillating energy change trend: ; Key operating parameter deviation degree: ； wherein are initial values for the parameters.

6. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1-5.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method according to any one of claims 1-5.

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