Synchronous generator and excitation system parameter identification method and system based on online measurement, and medium

Abstract

The invention relates to a synchronous generator and excitation system parameter identification method and system based on online measurement and a medium. The method comprises the following steps: establishing a synchronous generator and excitation system model; for a specific operation point, the reference voltage of the excitation system is used as an input signal, and a pseudo-random binary sequence PRBS is used for disturbing the specific operation point; sampling simulated input and output signals by using actual parameters of the system; calculating the influence on the terminal voltage and the active power by changing the value of each parameter; identifying parameters; and the accuracy and reliability of the identification method are verified. According to the invention, accurate and efficient identification of the parameters of the synchronous generator and the excitation system is realized, defects in the prior art are overcome, and powerful technical support is provided for stable operation of a power system.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular to online identification technology for synchronous generator and excitation system parameters, and specifically to a synchronous generator and excitation system parameter identification method, system and medium based on online measurement. Background Art

[0002] In power systems, the parameters of synchronous generators and excitation systems are crucial for system stability and operational continuity. However, these parameters often vary over time, necessitating regular identification and updating. Traditional parameter identification methods are primarily categorized into offline and online methods. Offline identification methods include static frequency response, open-circuit frequency response, least squares methods, and DC excitation methods. However, these methods require downtime, disrupting the normal operation of the power system, are typically time-consuming and difficult to apply, and fail to account for changes in parameter values due to aging. Online identification methods can be categorized into two types: 1) black-box modeling and 2) white-box modeling. Black-box modeling aims to map a dataset of system inputs and outputs without considering its physical structure. Various tools have been used for black-box modeling, such as wavelet transforms and neural networks. In white-box modeling, a known structure of the synchronous machine is assumed. A common assumption is a third-order model of the synchronous machine. In these studies, the excitation system (EXS) is not considered, and the field voltage is perturbed as the system input. Although online identification methods can be performed during generator operation, they face challenges such as numerous parameters and complex relationships, which limits the identification accuracy and efficiency. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, system and medium for identifying parameters of a synchronous generator and an excitation system based on online measurement. By utilizing the online measurement data of the power system and combining it with multi-stage genetic algorithm optimization, accurate and efficient identification of the parameters of the synchronous generator and the excitation system is achieved, aiming to overcome the shortcomings of the existing technology and provide strong technical support for the stable operation of the power system.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a method for identifying parameters of a synchronous generator and an excitation system based on online measurement, characterized in that the method comprises the following specific steps:

[0006] Build a model of the synchronous generator and excitation system;

[0007] Acquisition of input signal: For a specific operating point, the reference voltage of the excitation system is used as the input signal and disturbed using a pseudo-random binary sequence (PRBS).

[0008] Signal sampling: The simulated input and output signals are sampled using the actual parameters of the system. The terminal voltage Vt and output active power Pe are regarded as output signals.

[0009] Sensitivity analysis: By changing the value of each parameter, the impact on the terminal voltage and active power is calculated, and the parameters are classified into the following three categories: EOV, which affects the terminal voltage; EOP, which affects the active power; and NEU, which has a smaller impact on both.

[0010] Parameter identification: A multi-stage genetic algorithm is proposed to iteratively identify parameters, which is divided into four stages of gradual optimization to ultimately obtain accurate parameter estimates;

[0011] Step 6. Result verification: Compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

[0012] The proposed multi-stage genetic algorithm iteratively identifies parameters, which is divided into four stages of gradual optimization, and ultimately obtains accurate parameter estimates as follows:

[0013] Phase 1: The EOV parameter is set as the decision variable of the genetic algorithm, the EOP and NEU parameters are initialized to the middle values of their allowable ranges, and the optimization objective function is to minimize the terminal voltage error;

[0014] The second stage: the EOP parameter is set as the decision variable of the genetic algorithm, the EOV parameter keeps the estimated value of the previous stage, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is to minimize the active power error;

[0015] The third stage: EOV and EOP parameters are set as decision variables of the genetic algorithm at the same time, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors;

[0016] The fourth stage: The NEU parameter is set as the decision variable of the genetic algorithm, the EOV and EOP parameters keep the estimated values of the previous stage, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors to obtain the final result.

[0017] The specific steps of establishing the synchronous generator model are:

[0018] The mathematical model of the synchronous generator consists of two state equations for rotor dynamics and five state equations for the stator, damper, and field winding, specifically:

[0019]

[0020] Among them, ω and δ are the speed and angle of the rotor respectively, M is the inertia coefficient of the rotor, T m is the mechanical input torque, Te is the electrical output torque, T D is the mechanical damping torque, ω b is the basic speed, are the magnetic flux of the direct axis and quadrature axis respectively, v d 、v q are the voltages of the direct axis and quadrature axis respectively, r a 、r f are the resistances of the stator and field winding respectively, i d 、i q are the direct-axis and quadrature-axis currents, v f is the voltage of the excitation winding, i f is the current of the excitation winding, r D 、r Q is the damping winding resistance, i D 、i Q is the damping winding current, is the excitation winding flux, is the damping winding flux.

[0021] The excitation system is a DC exciter, which includes a rectifier module, an automatic voltage regulator module, an excitation power supply module, an excitation regulator module, and a deexcitation and rotor overvoltage protection module.

[0022] The objective function of the first stage of parameter identification is:

[0023]

[0024] The objective function of the second stage is:

[0025] The objective function of the third stage is:

[0026]

[0027] Where N is the number of samples, V ti 、P ei is the sample vector of terminal voltage and output active power simulated with typical parameter values, V t_esti is the sampling vector for estimating the terminal voltage, P e_esti is the sampling vector for estimating the output active power.

[0028] For result verification, simulations were performed using both actual and estimated values of the parameters by applying a step change in the input mechanical power to change the operating point.

[0029] In a second aspect, an embodiment of the present application provides a synchronous generator and excitation system parameter identification system based on online measurement, comprising a system modeling unit, an input signal acquisition unit, a signal sampling unit, a sensitivity analysis unit, a parameter identification unit, and a result verification unit; wherein,

[0030] The system modeling unit is used to build synchronous generator and excitation system models;

[0031] The input signal acquisition unit is used to acquire the input signal: for a specific operating point, the reference voltage of the excitation system is used as the input signal and is disturbed using a pseudo-random binary sequence PRBS;

[0032] The signal sampling unit is used to sample the simulated input and output signals using the actual parameters of the system. The terminal voltage Vt and the output active power Pe are regarded as output signals.

[0033] The sensitivity analysis unit is used to calculate the impact on the terminal voltage and active power by changing the value of each parameter, thereby classifying the parameters into the following three categories: parameters EOV that affect the terminal voltage, parameters EOP that affect the active power, and parameters NEU that have a smaller impact on both;

[0034] The parameter identification unit is used to iteratively identify parameters using a multi-stage genetic algorithm, which is divided into four stages of step-by-step optimization to ultimately obtain accurate parameter estimates;

[0035] The result verification unit is used to compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

[0036] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores program code. When the program code is executed by a processor, the steps of the method for identifying parameters of a synchronous generator and excitation system based on online measurement as described above are implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. Improve identification accuracy: Through online measurement and multi-stage genetic algorithm optimization, the present invention can accurately identify the parameters of the synchronous generator and excitation system, thereby improving the identification accuracy.

[0039] 2. Improve identification efficiency: The method of the present invention can perform parameter identification during the operation of the generator without stopping the generator, thereby improving the efficiency of identification.

[0040] 3. Strong adaptability: The method of the present invention is applicable to different types of synchronous generators and excitation systems, and has strong versatility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 Flow chart of the method of the present invention.

[0043] Figure 2 This is the output signal simulation test diagram of the present invention.

[0044] Figure 3 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings. It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0046] The terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0047] The terms "first," "second," etc. are only used to distinguish one entity or operation from another entity or operation, and are not to be understood as indicating or implying relative importance, nor are they to be understood as requiring or implying any actual relationship or order between these entities or operations.

[0048] like Figure 1 A method for identifying parameters of a synchronous generator and an excitation system based on online measurement is shown, comprising:

[0049] Step 1: Establish the synchronous generator and excitation system model;

[0050] Step 2: Collect input signals: For a specific operating point, use the reference voltage of the excitation system as the input signal and perturb it using a pseudo-random binary sequence (PRBS);

[0051] Step 3: Signal sampling: Use the actual parameters of the system to sample the simulated input and output signals. The terminal voltage Vt and output active power Pe are regarded as output signals.

[0052] Step 4: Sensitivity analysis: By changing the value of each parameter, calculate the impact on the terminal voltage and active power, and thus classify the parameters into the following three categories: parameters that affect the terminal voltage (EOV), parameters that affect the active power (EOP), and parameters that have a smaller impact on both (NEU).

[0053] Step 5: Parameter identification: A multi-stage genetic algorithm is proposed to iteratively identify parameters, which is divided into four stages of gradual optimization to ultimately obtain accurate parameter estimates; specifically:

[0054] Phase 1: The EOV parameter is set as the decision variable of the genetic algorithm, the EOP and NEU parameters are initialized to the middle values of their allowable ranges, and the optimization objective function is to minimize the terminal voltage error;

[0055] The second stage: the EOP parameter is set as the decision variable of the genetic algorithm, the EOV parameter keeps the estimated value of the previous stage, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is to minimize the active power error;

[0056] The third stage: EOV and EOP parameters are set as decision variables of the genetic algorithm at the same time, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors;

[0057] The fourth stage: The NEU parameter is set as the decision variable of the genetic algorithm, the EOV and EOP parameters are kept at the estimated values of the previous stage, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors to obtain the final result;

[0058] Step 6. Result verification: Compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

[0059] Preferably, the mathematical model of the synchronous generator includes two state equations of rotor dynamics and five state equations of the stator, damper and excitation winding, specifically:

[0060]

[0061] Among them, ω and δ are the speed and angle of the rotor respectively, M is the inertia coefficient of the rotor, T m is the mechanical input torque, T e is the electrical output torque, T D is the mechanical damping torque, ω b is the basic speed, are the magnetic flux of the direct axis and quadrature axis respectively, vd 、v q are the voltages of the direct axis and quadrature axis respectively, r a 、r f are the resistances of the stator and field winding respectively, i d 、i q are the direct-axis and quadrature-axis currents, v f is the voltage of the excitation winding, i f is the current of the excitation winding, r D 、r Q is the damping winding resistance, i D 、i Q is the damping winding current, is the excitation winding flux, is the damping winding flux.

[0062] Preferably, the excitation system is a DC exciter, which includes five modules: a rectifier, an automatic voltage regulator, an excitation power supply, an excitation regulator, and a deexcitation and rotor overvoltage protection.

[0063] Preferably, the objective function of the first stage of parameter identification is:

[0064]

[0065] The objective function of the second stage is:

[0066] The objective function of the third stage is:

[0067]

[0068] Where N is the number of samples, V ti 、P ei is the sample vector of terminal voltage and output active power simulated with typical parameter values, V t_esti is the sampling vector for estimating the terminal voltage, P e_esti is the sampling vector for estimating the output active power.

[0069] Preferably, when verifying the results, simulations are performed using actual and estimated values of the parameters by making step changes in the input mechanical power to change the operating point.

[0070] For verification purposes, a step change in the input mechanical power was applied to change the operating point. Simulations were performed using both actual and estimated values of the parameters. The results show that Figure 2 As shown, the estimated parameters of the new operating point have good accuracy.

[0071] The case studied in this paper is a single-machine infinite system with a DC IEEE Type 1 excitation system. The specifications of the salient-pole synchronous motor, power transformer, transmission line, and excitation system are shown in Tables 1 and 2, respectively. It should be noted that since the motor is salient-pole, there is no quadrature-axis transient reactance. A PRBS with an amplitude of 5% of the rated value is added to the reference voltage as the input signal. The output signals are the terminal voltage and output active power. Additive white Gaussian noise (AWGN) is added to the output signal using the MATLAB command to make the identification process more realistic: In the simulations, the signal-to-noise ratio was set to 10, which is higher than the noise actually added to either output.

[0072] Table 1 System parameters

[0073]

[0074]

[0075] Table 2 Excitation system parameters

[0076] parameter Numerical parameter Numerical <![CDATA[K a ]]> 290.0000 <![CDATA[T e ]]> 0.3000s <![CDATA[T a ]]> 0.00095s A 0.2874 <![CDATA[K f ]]> 0.0001 B 0.3632 <![CDATA[T f ]]> 1.2000s <![CDATA[T r ]]> 0.0020s <![CDATA[K e ]]> 1.0000

[0077] According to the sensitivity analysis above, the 19 parameters of synchronous motor and excitation system are divided into EOV, EOP and NEU groups. It is worth noting that some of the parameters (such as X d and X q ) for V t and P e All have an impact, but H, D, T do '、T do ” and X d " and other parameters on P e The influence of is more obvious, so these last five parameters are selected as EOP. qo ”、X q ”、T f and T r These four parameters clearly belong to NEU. Of the remaining ten parameters, almost all of them have an impact on both voltage and power, but it should be noted that the main purpose of parameter classification is to perform identification through a multi-level process, which makes it easier to handle the identification of a large number of parameters. Therefore, when a parameter has an impact on both voltage and power, it can be classified as EOV or EOP according to the needs. Therefore, although the remaining ten parameters have an impact on P e have a significant impact, but due to their effect on V t It also has an impact, so it was selected as EOV.

[0078] After sensitivity analysis and classification, the identification process begins. The implementation parameters of the genetic algorithm optimization are shown in Table 3.

[0079] Table 3 Genetic algorithm optimization parameters

[0080]

[0081]

[0082] The identification results in Table 4 show the improvement in accuracy from the first stage to the third stage. Finally, in the fourth stage, the NEU parameters are identified using formula (4). Since these parameters have a significant impact on V t and P e The influence of is not significant, so the output signal estimated in this stage is similar to that in the previous stage. The final results of parameter identification are shown in column 4 of stage 4 of Table 4.

[0083] Table 4 Identification results

[0084]

[0085]

[0086] To show the accuracy of the estimated parameters, the error indicators are defined as follows:

[0087] Where pk is the kth parameter in Table 4, V t and P e is the sampling vector of the simulation system when all parameters are actual values, and is the sampling vector of the simulated system when all parameters are actual values (taken from the sixth column of Table 4) except the kth parameter which is an estimated value (taken from the fifth column of Table 4). In fact, this indicator shows the influence of the kth parameter on the estimated signal. The results are shown in Table 5. The last row of Table 5 shows the error when all parameters are estimated values. The results show that most parameters have been accurately estimated. Although from the results of Table 4, some parameters such as X d ''、X q ''、T do '', A and T r The estimates of some parameters do not seem to be very precise, but the errors in Table 5 prove that these estimates have acceptable accuracy.

[0088] Table 5 Estimation error

[0089] parameter error parameter error parameter error XD 0.0063 <![CDATA[T qo ‘’]]> 0.0020 <![CDATA[K e ]]> 0.1173 Xq 0.0105 H 0.0119 <![CDATA[T e ]]> 0.0656 <![CDATA[X d ’]]> 0.1234 D 0.0000 A 0.2512 <![CDATA[X d ‘’]]> 0.1815 <![CDATA[K a ]]> 0.0089 B 0.2512 <![CDATA[X q ‘’]]> 0.0013 <![CDATA[T a ]]> 0.0475 <![CDATA[T r ]]> 0.0070 <![CDATA[T do ’]]> 0.0045 <![CDATA[K f ]]> 0.0058 ALL 0.1910 <![CDATA[T do ‘’]]> 0.0496 <![CDATA[T f ]]> 0.0003 - -

[0090] like Figure 3 Furthermore, the present invention also provides a synchronous generator and excitation system parameter identification system based on online measurement, comprising: a system modeling unit, an input signal acquisition unit, a signal sampling unit, a sensitivity analysis unit, a parameter identification unit, and a result verification unit; wherein,

[0091] The system modeling unit is used to build synchronous generator and excitation system models;

[0092] The input signal acquisition unit is used to acquire the input signal: for a specific operating point, the reference voltage of the excitation system is used as the input signal and is disturbed using a pseudo-random binary sequence (PRBS);

[0093] The signal sampling unit is used to sample the simulated input and output signals using the actual parameters of the system. The terminal voltage Vt and the output active power Pe are regarded as output signals.

[0094] The sensitivity analysis unit is used to calculate the impact on the terminal voltage and active power by changing the value of each parameter, thereby classifying the parameters into the following three categories: parameters EOV that affect the terminal voltage, parameters EOP that affect the active power, and parameters NEU that have a smaller impact on both;

[0095] The parameter identification unit is used to iteratively identify parameters using a multi-stage genetic algorithm, which is divided into four stages of step-by-step optimization to ultimately obtain accurate parameter estimates;

[0096] The result verification unit is used to compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

[0097] An embodiment of the present application provides a computer-readable storage medium storing program code. When the program code is executed by a processor, the steps of the method for identifying parameters of a synchronous generator and an excitation system based on online measurement as described above are implemented.

[0098] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0103] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0104] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0105] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for identifying parameters of a synchronous generator and excitation system based on online measurement, characterized in that: The specific steps include: Build a model of the synchronous generator and excitation system; Acquisition of input signal: For a specific operating point, the reference voltage of the excitation system is used as the input signal and disturbed using a pseudo-random binary sequence (PRBS). Signal sampling: The simulated input and output signals are sampled using the actual parameters of the system. The terminal voltage Vt and output active power Pe are regarded as output signals. Sensitivity analysis: By changing the value of each parameter, the impact on the terminal voltage and active power is calculated, and the parameters are classified into the following three categories: EOV, which affects the terminal voltage; EOP, which affects the active power; and NEU, which has a smaller impact on both. Parameter identification: A multi-stage genetic algorithm is proposed to iteratively identify parameters, which is divided into four stages of gradual optimization to ultimately obtain accurate parameter estimates; Step 6. Result verification: Compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

2. A method for identifying parameters of a synchronous generator and excitation system based on online measurement according to claim 1, characterized in that: The proposed multi-stage genetic algorithm iteratively identifies parameters, which is divided into four stages of gradual optimization, and ultimately obtains accurate parameter estimates as follows: Phase 1: The EOV parameter is set as the decision variable of the genetic algorithm, the EOP and NEU parameters are initialized to the middle values of their allowable ranges, and the optimization objective function is to minimize the terminal voltage error; The second stage: the EOP parameter is set as the decision variable of the genetic algorithm, the EOV parameter keeps the estimated value of the previous stage, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is to minimize the active power error; The third stage: EOV and EOP parameters are set as decision variables of the genetic algorithm at the same time, the NEU parameter is initialized to the middle value of its allowable range, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors; The fourth stage: The NEU parameter is set as the decision variable of the genetic algorithm, the EOV and EOP parameters keep the estimated values of the previous stage, and the optimization objective function is the comprehensive minimization of the terminal voltage and active power errors to obtain the final result.

3. The method for identifying parameters of a synchronous generator and an excitation system based on online measurement according to claim 1, characterized in that: The specific steps of establishing the synchronous generator model are: The mathematical model of the synchronous generator consists of two state equations for rotor dynamics and five state equations for the stator, damper, and field winding, specifically: d=ω b (ω-1) Among them, ω and δ are the speed and angle of the rotor respectively, M is the inertia coefficient of the rotor, T m is the mechanical input torque, T e is the electrical output torque, T D is the mechanical damping torque, ω b is the basic speed, are the magnetic flux of the direct axis and quadrature axis respectively, v d 、v q are the voltages of the direct axis and quadrature axis respectively, r a 、r f are the resistances of the stator and field winding respectively, i d 、i q are the direct-axis and quadrature-axis currents, v f is the voltage of the excitation winding, i f is the current of the excitation winding, r D 、r Q is the damping winding resistance, i D 、i Q is the damping winding current, is the excitation winding flux, is the damping winding flux.

4. A method for identifying parameters of a synchronous generator and excitation system based on online measurement according to claim 1, characterized in that: The excitation system is a DC exciter, which includes a rectifier module, an automatic voltage regulator module, an excitation power supply module, an excitation regulator module, and a deexcitation and rotor overvoltage protection module.

5. The method for identifying parameters of a synchronous generator and an excitation system based on online measurement according to claim 2, characterized in that: The objective function of the first stage of parameter identification is: The objective function of the second stage is: The objective function of the third stage is: Where N is the number of samples, V ti 、P ei is the sample vector of terminal voltage and output active power simulated with typical parameter values, V t_esti is the sampling vector for estimating the terminal voltage, P e_esti is the sampling vector for estimating the output active power.

6. A method for identifying parameters of a synchronous generator and excitation system based on online measurement according to claim 1, characterized in that: For result verification, simulations were performed using both actual and estimated values of the parameters by applying a step change in the input mechanical power to change the operating point.

7. A synchronous generator and excitation system parameter identification system based on online measurement, characterized in that: It includes system modeling unit, input signal acquisition unit, signal sampling unit, sensitivity analysis unit, parameter identification unit and result verification unit; among them, The system modeling unit is used to build synchronous generator and excitation system models; The input signal acquisition unit is used to acquire the input signal: for a specific operating point, the reference voltage of the excitation system is used as the input signal and is disturbed using a pseudo-random binary sequence PRBS; The signal sampling unit is used to sample the simulated input and output signals using the actual parameters of the system. The terminal voltage Vt and the output active power Pe are regarded as output signals. The sensitivity analysis unit is used to calculate the impact on the terminal voltage and active power by changing the value of each parameter, thereby classifying the parameters into the following three categories: parameters EOV that affect the terminal voltage, parameters EOP that affect the active power, and parameters NEU that have a smaller impact on both; The parameter identification unit is used to iteratively identify parameters using a multi-stage genetic algorithm, which is divided into four stages of step-by-step optimization to ultimately obtain accurate parameter estimates; The result verification unit is used to compare the identified parameters with the simulation results to verify the accuracy and reliability of the identification method.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and when the program code is executed by a processor, the steps of the method for identifying parameters of a synchronous generator and an excitation system based on online measurement according to any one of claims 1 to 6 are implemented.

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