Phase modifier system data coordination processing method and device based on multi-sensor fusion

Abstract

The invention discloses a phase modifier system data coordination processing method and device based on multi-sensor fusion, and the method comprises the steps: selecting multi-sensor data from the operation process of a phase modifier system, the multi-sensor data comprising a measurement variable, an unmeasured variable and a measurement value of the measurement variable; establishing a constraint equation about measured variables and unmeasured variables according to the dynamic model of the operation of the phase modifier system; based on the measured value of the measured variable, solving the constraint equation by adopting a linearization method to obtain a coordinated value of the measured variable and an estimated value of an unmeasured variable; and correcting the coordination value of the measured variable by adopting a Proposed estimation function to obtain a corrected value of the measured variable, and outputting the corrected value of the measured variable and an estimated value of an unmeasured variable. According to the invention, the influence of random errors and significant errors of measurement parameters in the operation process of the phase modifier system can be effectively reduced, and the quality of operation data of the phase modifier system is effectively improved.

Description

Technical Field

[0001] The present application relates to a data coordination processing method and device for a synchronous condenser system based on multi-sensor fusion, belonging to the technical fields of signal processing and process monitoring. Background Art

[0002] During the operation of the synchronous condenser system, when the sensor has an instantaneous failure or is subjected to abnormal interference, the measurement data may contain significant errors. Especially for the sensor data of vibration protection, bearing bush temperature protection, lubricating oil tank liquid level protection, lubricating oil supply port pressure protection, and external cooling water flow protection used for the thermal main protection of the synchronous condenser, higher requirements are placed on the data accuracy and reliability.

[0003] Therefore, it is very important to effectively reduce the influence of significant errors and improve the accuracy of measurement data. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, the present application provides a data coordination processing method and device for a synchronous condenser system based on multi-sensor fusion, which can reduce the influence of noise and disturbance of each measurement parameter during the operation of the synchronous condenser system on the operation and maintenance and repair operations of the synchronous condenser.

[0005] To achieve the above object / to solve the above technical problems, the present application is implemented by the following technical solutions.

[0006] In a first aspect, the present application provides a data coordination processing method for a synchronous condenser system based on multi-sensor fusion, including:

[0007] S1. Select multi-sensor data from the operation process of the synchronous condenser system, where the multi-sensor data includes measured variables, unmeasured variables, and measured values of the measured variables;

[0008] S2. According to the dynamic model of the synchronous condenser system operation, establish a constraint equation about the measured variables and unmeasured variables;

[0009] S3. Based on the measured values of the measured variables, use the linearization method to solve the constraint equation to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables;

[0010] S4. Use the Proposed estimation function to correct the coordinated values of the measured variables to obtain the corrected values of the measured variables, and output the corrected values of the measured variables and the estimated values of the unmeasured variables.

[0011] In a second aspect, the present application provides a data coordination processing device for a synchronous condenser system based on multi-sensor fusion, including:

[0012] A data selection module, configured to: select multi-sensor data from the operation process of the synchronous condenser system, where the multi-sensor data includes measured variables, unmeasured variables, and measured values of the measured variables;

[0013] An equation establishment module, configured to: establish constraint equations regarding the measured variables and unmeasured variables according to the dynamic model of the synchronous condenser system operation;

[0014] A solution module, configured to: based on the measured values of the measured variables, solve the constraint equations by using the linearization method to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables;

[0015] A calibration module, configured to: perform data calibration on the coordinated values of the measured variables by using the Proposed estimation function to obtain the calibrated values of the measured variables, and output the calibrated values of the measured variables and the estimated values of the unmeasured variables.

[0016] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0017] In a fourth aspect, the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect are implemented.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0019] Compared with the prior art, the beneficial effects achieved by the present application are as follows: The data coordination processing method for the synchronous condenser system based on multi-sensor fusion provided by the present application has the following advantages: Without modifying the existing measurement devices and systems of the synchronous condenser, the data collected by the multi-sensors of each system of the synchronous condenser are utilized, processed and optimized by using the data coordination processing method, and calculated by using the data coordination method based on the constraint relationships of the devices in each system, so as to achieve the purpose of improving the accuracy of data by eliminating the influence of random errors. Furthermore, the present invention adopts the Proposed estimation function to ensure the reliability of the data coordination result by identifying and eliminating the data containing significant errors. Description of the Drawings

[0020] Figure 1 It is a schematic flowchart of the data coordination processing method for the synchronous condenser system based on multi-sensor fusion according to an embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of the lubricating oil system of the synchronous condenser according to an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of the probability density comparison between measurement data and calibration data according to an embodiment of the present application;

[0023] Figure 4 It is a schematic diagram of the weighted least squares data calibration result according to an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of the Proposed robust data calibration result according to an embodiment of the present application. Detailed implementation manners

[0025] The technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0026] In the description of the present application, the meaning of several is more than one, and the meaning of multiple is more than two. Understandings such as greater than, less than, exceeding, etc. do not include the present number, and understandings such as above, below, within, etc. include the present number. If the first and second are described only for the purpose of distinguishing technical features, they cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0027] In the description of the present application, the description with reference to terms such as "an embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0028] The term "and / or" is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0029] In a possible application scenario, such as Figure 1 As shown, multiple and various types of wind turbines are used for power generation in a wind farm system. Multiple energy management platforms are deployed in the station, including primary frequency modulation equipment, grid connection points, and multiple energy management platforms. Information is exchanged between the primary frequency modulation equipment, the grid connection points, and the multiple energy management platforms.

[0030] Example 1: As Figure 1 shown, this example provides a data coordination processing method for a synchronous condenser system based on multi-sensor fusion. The method includes:

[0031] S1. Select multi-sensor data during the operation of the synchronous condenser system. The multi-sensor data includes measured variables, unmeasured variables, and measured values of the measured variables.

[0032] The errors contained in the measurement data can be divided into two categories: random errors and gross errors. The former is affected by random factors and follows certain statistical laws, while the latter is caused by non-random events such as sensor failures and equipment leaks and thus does not follow statistical laws.

[0033] During the operation of the system, select the measured variable x and the unmeasured variable u, and denote the measured value of the measured variable x as y. Therefore, in some embodiments, in this step S1, the measured variable, the unmeasured variable, and the measured value of the measured variable are one-dimensional vectors composed of multiple variables, expressed as:

[0034]

[0035] where n is the number of all measured variables; p is the number of all unmeasured variables, X is an n-dimensional measured variable vector, 、 represent the 1st, nth measured variables; Y is an n-dimensional measured value vector of the measured variable, 、 represent the measured values of the 1st, nth measured variables; U is a p-dimensional unmeasured variable vector, 、 represent the 1st, pth unmeasured variables.

[0036] It should be noted that the instruments of the synchronous condenser system for measuring thermal parameters include measuring instruments, transmitters, display instruments, etc., which are used to immediately display and record various parameters of the equipment, such as rotational speed, vibration, temperature, pressure, flow rate, liquid level, etc.

[0037] More specifically, the measured variables include flow rate, pressure, temperature, and reactive power.

[0038] S2. Establish constraint equations regarding the measured variables and the unmeasured variables according to the dynamic model of the synchronous condenser system operation.

[0039] It should be noted that the constraint equations include mass balance equations, energy balance equations, and characteristic equations. As follows: F(x, u) = 0; where F is a function vector representing a set of algebraic equations.

[0040] The definition of data reconciliation is that when the measured data only contains random errors that follow a normal distribution, on the premise of satisfying the constraints such as mass conservation and energy conservation of the system, the reconciled values of the measured data are determined to minimize the sum of the squares of the deviations between the reconciled values and the measured values, and at the same time, the unmeasured variables are estimated. This type of optimization problem can be expressed as a least squares problem that satisfies multiple equality constraint equations.

[0041] In some embodiments, in this step S2, constraint equations regarding the measured variables and the unmeasured variables are established, including:

[0042] ,

[0043] ,

[0044] where: X is an n-dimensional vector of measured variables; is an n-dimensional vector of the reconciled values of the measured variables; T represents transpose; Q is the dimensional variance-covariance matrix of X; U is a p-dimensional vector of unmeasured variables; F is a vector of functions representing a set of algebraic equations.

[0045] For the synchronous condenser system, the constraint equations are non-linear. Therefore, the data reconciliation problem of the synchronous condenser system belongs to a non-linear constraint problem, and this patent uses the linearization method to solve it.

[0046] S3. Based on the measured values of the measured variables, use the linearization method to solve the constraint equations to obtain the reconciled values of the measured variables and the estimated values of the unmeasured variables.

[0047] In some embodiments, in this step S3, based on the measured values of the measured variables, using the linearization method to solve the constraint equations includes:

[0048] S31. Use Taylor's formula to expand the constraint equations, retaining only the first derivative terms to obtain the linearized constraint equations;

[0049] S32. Use the Lagrange multiplier method to iteratively solve the linearized constraint equations until the set accuracy is satisfied to obtain the reconciled values of the measured variables and the estimated values of the unmeasured variables.

[0050] Furthermore, using Taylor's formula to expand the constraint equations to obtain the linearized constraint equations includes:

[0051] The constraint equations are non-linear, and the Lagrange multiplier form of the constraint equations satisfies:

[0052] ,

[0053] ,

[0054] ,

[0055] In the formula: , are respectively the m×n dimensional Jacobian matrices of F with respect to and U, where m represents the number of constraint equations, denotes the Lagrange multiplier vector;

[0056] Expand the constraint equations using Taylor's formula to obtain the linearized constraint equations;

[0057] ,

[0058] In the formula: the superscript k represents the iteration number, and represent the and at the k-th iteration, and represent the coordinated value vectors of the measured variables after the k-th and (k + 1)-th iterations, and represent the estimated value vectors of the unmeasured variables after the k-th and (k + 1)-th iterations.

[0059] Furthermore, use the Lagrange multiplier method to iteratively solve the linearized constraint equations, including:

[0060] Select the initial values and U(0) of according to experience, substitute them into the linearized constraint equations to start the iteration; when the linearized constraint equations meet the set accuracy ξ n , terminate the iterative calculation;

[0061] ;

[0062] When the iteration terminates, ;

[0063] Finally, obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables:

[0064] ;

[0065] ;

[0066] ;

[0067] Among them, and are intermediate parameters, represents a set of algebraic equations at the k-th iteration.

[0068] General data reconciliation methods are based on weighted least squares estimation, and the reconciliation results are easily interfered by gross errors. This is because the data containing gross errors is further away from the sample population. If the sum of the squares of the residuals is to be minimized, it is necessary to accommodate these outliers. Robust data calibration introduces robust estimation into the field of data calibration and uses the characteristics of the robust estimation function to reduce the influence of gross errors on the calibration results.

[0069] To improve the performance of the robust estimation function in data calibration, a piecewise robust estimation function is constructed based on the robust estimation principle in this paper, which is called the Proposed estimation function. First, the influence function is constructed according to the above idea and is constructed in a piecewise form. The first half is the same as the influence function of the weighted least squares estimation, and the second half is constructed using an exponential function so that the influence function can gradually decrease and finally converge. In this way, both the calibration effect of the least squares estimation can be taken into account and the boundedness of the influence function can be ensured. Subsequently, the influence function is integrated to obtain the Proposed estimation function.

[0070] S4. Use the Proposed estimation function to perform data calibration on the reconciliation value of the measured variable to obtain the calibrated value of the measured variable, and output the calibrated value of the measured variable and the estimated value of the unmeasured variable.

[0071] In some embodiments, in this step S4, the Proposed estimation function is expressed as:

[0072] ;

[0073] The influence function is an important evaluation index of the estimation function and is used to measure the importance of different relative residuals to the estimation function. The influence function of the Proposed estimation function is:

[0074] ;

[0075] The weight function is another important evaluation index of the estimation function and describes the weight of the data point. The weight function of the Proposed estimation function is:

[0076] ;

[0077] In the formula: both c and a are adjustable parameters, is the reconciliation value of the measured variable.

[0078] The parameter a is the base of the exponential function and ranges from 0 to 1, which is used to adjust the descending speed of the exponential function. The smaller the parameter a, the faster the function descends and converges earlier, achieving the effect of suppressing significant error transmission. The parameter c is the break point of the piecewise function, which is used to adjust the timing of suppressing significant errors. When the relative residual is greater than c, it is considered that the measurement data contains significant errors; when the relative residual is less than c, it is considered that the measurement data does not contain significant errors. In the actual application process, the parameters can be reasonably adjusted according to the error distribution of historical data. When the accuracy of historical data is high, the value of the parameter c can be appropriately reduced to ensure a better suppression effect on significant errors.

[0079] Specific application example: As Figure 2 shown, when the synchronous condenser is running, heat is generated by the friction between the shaft of the rotor 7 and the bearing bush of the bearing housing 5. The lubricating oil carries the heat into the storage tank 1. Under the power of the lubricating oil pump 2, it is cooled by the oil cooler 3 and filtered by the duplex filter 4, and then enters the bearing housing 5 again under the power of the jacking oil pump 6, forming a circulating lubrication and cooling. When the heat generated by the bearing is equal to the heat dissipation of the oil cooler and the oil tank, a stable operating state is reached, and the oil temperature tends to a stable value. Thus, the energy balance equation of the lubricating oil can be obtained.

[0080] In the pipeline network of the lubricating oil system, for each node (such as the outlet of the oil pump, the inlet of the storage tank, etc.), the total flow rate flowing into the node is equal to the total flow rate flowing out of the node. Thus, the flow rate (mass) balance equation is obtained.

[0081] According to the principle of fluid mechanics, there are frictional pressure losses and local pressure losses in the flow of lubricating oil in the oil pipeline. Based on the relationship between the pressure losses and the flow rate and pipeline parameters, the pressure constraint equation is obtained.

[0082] The following three constraint equations are the constraint equations of the lubricating oil system. Taking the lubricating oil system as the research object, eight variables are selected, including 5 measured variables and 3 unmeasured variables.

[0083]

[0084] In the formula: x 1, x 2, x 3, x 4, x 5 represents the measured variable, u 1, u 2, u 3 represents the unmeasured variable.

[0085] Independent repeated correction calculations are performed on 2000 sets of temperature measurement data containing only random errors, and 2000 sets of corrected data are obtained.

[0086] To more intuitively observe the changes before and after the correction of the measured variables, taking the temperature of the lubricating oil inlet main pipe to the bearing housing as an example, the probability densities of the measured data and the corrected data are compared, as Figure 3 shown.

[0087] As can be seen from Figure 3 , the 2000 groups of measured data show a normal distribution change between 45.4 °C and 46.8 °C. After data correction, the temperature of the lubricating oil inlet main pipe to the bearing housing still shows a normal distribution change, the expected value remains basically unchanged, while the standard deviation decreases, and the probability density distribution graph becomes more "skinny and tall", that is, the corrected data is more concentrated near the expected value, and the data fluctuation range is smaller. This shows that the measured data has higher accuracy after data correction.

[0088] To intuitively compare the data correction effects of the weighted least squares method and the Proposed robust data correction method, continue to take the temperature of the lubricating oil inlet main pipe to the bearing housing as the research object. During the normal operation of the synchronous condenser, the temperature of the lubricating oil inlet main pipe to the bearing housing is about 46.5 °C. Simulate 2000 groups of measured data containing only random errors, and then select the 500th, 1000th, and 1500th measured data to add significant errors of 8% in magnitude.

[0089] The weighted least squares method and the Proposed robust data correction method are respectively used to correct the measured data, and the results are as shown in Figure 4 and Figure 5 shown.

[0090] Figure 4 and Figure 5 The results show that for the measured data without significant errors, the correction results of the two methods are basically the same, indicating that the Proposed robust data correction method inherits the good characteristics of the weighted least squares method in the case of no significant errors. For the data containing significant errors, the weighted least squares method is greatly affected by the significant errors, resulting in the correction value deviating significantly from the true value; the Proposed robust data correction method is insensitive to the significant errors, and the correction value is closer to the true value. Taking the 500th group of data with significant errors as an example, at this time the magnitude of the significant error is 8%, the relative error of the weighted least squares correction method is 5.8%, and the relative error of the Proposed robust data correction method is only 0.4%. It fully demonstrates that the proposed robust data correction method of the present invention has better resistance to outliers, and the correction result is closer to the true value, especially when the magnitude of the significant error is large, it shows greater advantages.

[0091] Embodiment 2: Based on the same inventive concept as Embodiment 1, this embodiment provides a data coordination processing device for a synchronous condenser system based on multi-sensor fusion, including:

[0092] A data selection module, configured to: select multi-sensor data from the operation process of the synchronous condenser system, where the multi-sensor data includes measured variables, unmeasured variables, and measured values of the measured variables;

[0093] An equation establishment module, configured to: establish constraint equations regarding the measured variables and unmeasured variables according to the dynamic model of the synchronous condenser system operation;

[0094] A solution module, configured to: based on the measured values of the measured variables, solve the constraint equations by using the linearization method to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables;

[0095] A calibration module, configured to: perform data calibration on the coordinated values of the measured variables by using the Proposed estimation function to obtain the calibrated values of the measured variables, and output the calibrated values of the measured variables and the estimated values of the unmeasured variables.

[0096] The specific function implementation of each of the above modules refers to the relevant content in the method of Embodiment 1, and will not be elaborated here.

[0097] Embodiment 3: Based on the same inventive concept as other embodiments, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0098] Embodiment 4: Based on the same inventive concept as other embodiments, this embodiment provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in Embodiment 1 are implemented.

[0099] Embodiment 5: Based on the same inventive concept as other embodiments, this embodiment provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0100] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. 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 disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0101] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 and / or means for implementing the functions specified in one or more of the blocks.

[0102] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 and / or means for implementing the functions specified in one or more of the blocks.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 and / or means for implementing the functions specified in one or more of the blocks.

[0104] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the spirit and scope protected by the claims of the present application, can also make many forms, and all of these fall within the protection scope of the present application.

Claims

1. A data coordination processing method for a synchronous condenser system based on multi-sensor fusion, characterized in that, Including: S1. Select multi-sensor data from the operation process of the synchronous condenser system. The multi-sensor data includes measured variables, unmeasured variables, and the measured values of the measured variables; S2. Establish constraint equations regarding the measured variables and the unmeasured variables according to the dynamic model of the synchronous condenser system operation; S3. Based on the measured values of the measured variables, use the linearization method to solve the constraint equations to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables; S4. Use the Proposed estimation function to perform data correction on the coordinated values of the measured variables to obtain the corrected values of the measured variables, and output the corrected values of the measured variables and the estimated values of the unmeasured variables.

2. The method according to claim 1, wherein In S1, the measured variables, unmeasured variables, and the measured values of the measured variables are one-dimensional vectors composed of multiple variables, expressed as: ； where n is the number of all measured variables; p is the number of all unmeasured variables, X is an n-dimensional vector of measured variables, and represent the first and nth measured variables; Y is an n-dimensional vector of measured values of measured variables, and represent the measured values of the first and nth measured variables; U is a p-dimensional vector of unmeasured variables, and represent the first and pth unmeasured variables.

3. The method according to claim 1, wherein In S2, establishing the constraint equations regarding the measured variables and the unmeasured variables includes: ， ， Where: X is an n-dimensional measurement variable vector; is the concordance value vector of the n-dimensional measurement variable; T represents transpose; Q is the dimensional variance-covariance matrix; U is a p-dimensional unmeasured variable vector; F is a function vector representing a set of algebraic equations.

4. The method according to claim 3, wherein In S3, based on the measured values of the measured variables, using the linearization method to solve the constraint equations includes: S31. Expand the constraint equations using the Taylor formula to obtain linearized constraint equations; S32. Use the Lagrange multiplier method to iteratively solve the linearized constraint equations until the set accuracy is met to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables.

5. The method according to claim 4, characterized in that Expanding the constraint equations using the Taylor formula to obtain linearized constraint equations includes: The constraint equations are non-linear, and the Lagrange multiplier form of the constraint equations satisfies: ， ， ， In the formula: , , are respectively the m×n dimensional Jacobian matrices of F with respect to , U, where m represents the number of constraint equations, represents the Lagrange multiplier vector; Expand the constraint equations using the Taylor formula to obtain linearized constraint equations; ， where: the superscript k represents the number of iterations, and represent the and of the k-th iteration, and represent the vector of the coordinated values of the measured variables after the k-th and (k + 1)-th iterations, and represent the vector of the estimated values of the unmeasured variables after the k-th and (k + 1)-th iterations.

6. The method according to claim 5, characterized in that Use the Lagrange multiplier method to iteratively solve the linearized constraint equations, including: Selected according to experience The initial value of U (0) and U(0), substitute them into the linearized constraint equation and start iteration; when the linearized constraint equation meets the set accuracy ξ n terminate the iterative calculation ； When the iteration terminates, ; Finally, obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables: ； ； ； Among them, , are intermediate parameters, represents a set of algebraic equations for the k-th iteration.

7. The method according to claim 1, characterized in that, In S4, the Proposed estimation function is expressed as: ； where: both c and a are adjustable parameters, is the coordinated value of the measured variable.

8. A data coordination processing device for a synchronous condenser system based on multi-sensor fusion, characterized in that, Including: A data selection module for selecting multi-sensor data from the operation process of the synchronous condenser system. The multi-sensor data includes measured variables, unmeasured variables, and the measured values of the measured variables; An equation establishment module for establishing constraint equations regarding the measured variables and the unmeasured variables according to the dynamic model of the synchronous condenser system operation; A solution module for using the linearization method to solve the constraint equations based on the measured values of the measured variables to obtain the coordinated values of the measured variables and the estimated values of the unmeasured variables; A correction module for using the Proposed estimation function to perform data correction on the coordinated values of the measured variables to obtain the corrected values of the measured variables, and outputting the corrected values of the measured variables and the estimated values of the unmeasured variables.

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

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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