Method and system for measuring real-time short-circuit ratio of new energy station based on Kalman filtering

Abstract

The invention discloses a method and a system for measuring the real-time short-circuit ratio of a new energy station based on Kalman filtering. The method comprises the following steps: constructing a Thevenin equivalent circuit of a new energy station connected to an alternating current system; inputting a reference voltage into the Thevenin equivalent circuit, and measuring two groups of voltages and currents of the grid-connected point at different moments; filtering the measured voltage and current values by using Kalman filtering; calculating system parameters of the Thevenin equivalent circuit by using the filtered voltage and current values; and calculating the real-time short-circuit ratio of the new energy station according to the system parameters of the Thevenin equivalent circuit. The problem that the real-time short-circuit ratio of the system is difficult to accurately measure due to the fact that large-scale new energy is connected to the power system at present can be solved.

Description

Technical Field

[0001] The present invention belongs to the field of power system stability assessment, and relates to a real-time short-circuit ratio measurement method, system, device and storage medium for new energy power stations based on Kalman filtering. Background Art

[0002] With the increasing proportion of new energy in the power system, the system strength becomes weaker, leading to problems such as broadband oscillation and overvoltage. Therefore, it is necessary to study grid strength quantification evaluation indexes for large-scale new energy access applicable to engineering. The voltage support strength after the access of power electronic devices can be measured by the ratio of the short-circuit capacity of the AC system to the rated capacity of the device, that is, the short-circuit ratio (SCR). There are significant differences in the short-circuit ratio calculation between the multi-new energy power station access system and the traditional AC system. The traditional short-circuit ratio cannot reflect the reactive power output of new energy power generation devices and the differences in the amplitudes and phase differences of electrical quantities between different nodes in the new energy power station. There is an urgent need for a real-time measurement method of the short-circuit ratio of new energy power stations to provide a theoretical and practical basis for the grid strength analysis of high-proportion new energy access to the power system. Summary of the Invention

[0003] Object of the Invention: The object of the present invention is to provide a real-time short-circuit ratio measurement method and system for new energy power stations based on Kalman filtering to solve the problem that it is difficult to accurately measure the real-time short-circuit ratio of the system due to the large-scale access of new energy to the power system.

[0004] Technical Solution: A real-time short-circuit ratio measurement method for new energy power stations based on Kalman filtering according to the present invention includes:

[0005] Construct a Thevenin equivalent circuit for the new energy power station accessing the AC system; input a reference voltage to the Thevenin equivalent circuit, and measure the voltages and currents at two different moments at the connection point.

[0006] Use Kalman filtering to filter the measured voltage and current values to obtain the values of the measured voltage and current after Kalman filtering.

[0007] Use the filtered voltage and current values to calculate the system parameters of the Thevenin equivalent circuit.

[0008] Calculate the real-time short-circuit ratio of the new energy power station according to the system parameters of the Thevenin equivalent circuit.

[0009] Further, the use of Kalman filtering to filter the measured voltage and current values includes:

[0010] Use Kalman filtering to predict the parameters of the new energy grid-connected system to obtain predicted values; calculate the Kalman gain according to the predicted values; use the Kalman gain to correct the error of the predicted values.

[0011] Furthermore, predicting the parameters of the new - energy grid - connected system by using Kalman filtering to obtain predicted values includes:

[0012] The available state - space equation of the real - time short - circuit ratio measurement system is described as:

[0013] x k = Ax k-1 + Bu k-1 + w k-1

[0014] z k = Hx k + v k

[0015] In the formula, x k and A represent the state variables of the current step and the previous step respectively, which are the measured values at the grid - connection point; B and w k-1 are the coefficient matrix of the state variable of the system and the coefficient matrix of the input variable respectively; z k represents the input variable of the previous step; H represents the process error; v k represents the current measured value; H represents the linear relationship between the true value and the measured value; v k represents the observation error; among them, the process error w k-1 and the observation error v k are assumed to satisfy the Gaussian distribution:

[0016] p(w)~N(0,Q)

[0017] p(v)~N(0,R)

[0018] In the formula, p represents probability; N represents the Gaussian distribution; Q and R represent the covariance of the Gaussian distributions of the process error and the observation error respectively;

[0019] Using Kalman filtering for state estimation of the new - energy grid - connected system needs to be carried out in two steps. First, predict the system as shown in the following formula:

[0020] Deduce the state quantity:

[0021]

[0022] In the formula, is the predicted value of the state variable of the current step; is the estimated value of the state variable of the previous step;

[0023] Deduce the error covariance:

[0024]

[0025] In the formula, Pk-1 represents the previous step value of the posterior error covariance matrix; P k represents the prior error covariance matrix; Q represents the covariance of the process error Gaussian distribution.

[0026] Furthermore, the expression of the Kalman gain is as follows:

[0027]

[0028] In the formula, K k represents the Kalman gain; represents the prior error covariance matrix; H represents the linear relationship between the true value and the measured value; H T represents the transpose matrix of H; R represents the covariance of the observation error Gaussian distribution.

[0029] Furthermore, using the Kalman gain to correct the error of the predicted value includes:

[0030] Updating the state estimate:

[0031]

[0032] In the formula, represents the optimal estimate value of the state variable; represents the result predicted according to the previous state; z k represents the current measured value;

[0033] Updating the error covariance:

[0034]

[0035] In the formula, P k represents the current step value of the posterior error covariance matrix; I represents the identity matrix.

[0036] Furthermore, the expression of the system parameters of the Thevenin equivalent circuit is as follows:

[0037]

[0038] In the formula, E d represents the d-axis value of the equivalent voltage of the grid-side power supply; E q represents the q-axis value of the equivalent voltage of the grid-side power supply; R represents the resistance value of the line equivalent impedance; X represents the inductance value of the line equivalent impedance; I d1 and I d2 respectively represent the first group and the second group of values of the current d-axis; U d1 and U d2 respectively represent the first group and the second group of values of the voltage d-axis; I q1 and I q2respectively represent the first and second groups of values of the current q-axis; U q1 and U q2 respectively represent the first and second groups of values of the voltage q-axis.

[0039] Furthermore, the expression of the real-time short-circuit ratio of the new energy power station is as follows:

[0040]

[0041] In the formula, R SCRi represents the real-time short-circuit ratio of node i; S aci is the short-circuit capacity of node i; U REi represents the voltage on the power system side of node i; U N represents the rated voltage of the system; E i represents the grid-connected voltage of node i; I i represents the grid-connected current of node i.

[0042] Based on the same inventive concept, a real-time short-circuit ratio measurement system for a new energy power station based on Kalman filtering of the present invention includes:

[0043] A measurement module, configured to construct a Thevenin equivalent circuit for the new energy power station accessing the AC system; input a reference voltage to the Thevenin equivalent circuit, and measure the voltages and currents at two different times at the grid connection point;

[0044] A filtering module, configured to filter the measured voltage and current values by using Kalman filtering to obtain the values of the measured voltage and current after Kalman filtering;

[0045] A system parameter calculation module, configured to construct a Thevenin equivalent circuit for the new energy power station accessing the AC system; calculate the system parameters of the Thevenin equivalent circuit by using the filtered voltage and current values;

[0046] A real-time short-circuit ratio calculation module, configured to calculate the real-time short-circuit ratio of the new energy power station according to the system parameters of the Thevenin equivalent circuit.

[0047] Based on the same inventive concept, a real-time short-circuit ratio measurement device for a new energy power station based on Kalman filtering of the present invention includes a processor and a memory, wherein computer instructions are stored in the memory, and the processor is configured to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned real-time short-circuit ratio measurement method for a new energy power station based on Kalman filtering.

[0048] Based on the same inventive concept, a computer-readable storage medium of the present invention stores a computer program, and when the program is executed by a processor, the steps of the above-mentioned real-time short-circuit ratio measurement method for a new energy power station based on Kalman filtering are implemented.

[0049] Beneficial effects: Compared with the prior art, the remarkable technical effects of the present invention are as follows:

[0050] Compared with the traditional short-circuit ratio calculation method, the real-time short-circuit ratio measurement method for new energy power stations proposed by the present invention does not require data communication between power stations and multi-point measurement. It only needs to measure the voltage and current at the grid connection point, effectively reducing the complexity of data processing and short-circuit ratio calculation.

[0051] At the same time, the data is processed by Kalman filtering, improving the accuracy of parameter identification and effectively ensuring the accuracy of short-circuit ratio measurement. Description of the Drawings

[0052] Figure 1 is a schematic flow chart of a real-time short-circuit ratio measurement method for new energy power stations based on Kalman filtering disclosed in an embodiment of the present invention;

[0053] Figure 2 is an equivalent circuit diagram of the AC side of the grid connection point of a new energy power station disclosed in an embodiment of the present invention;

[0054] Figure 3 is a schematic structural diagram of a real-time short-circuit ratio measurement system for new energy power stations based on Kalman filtering disclosed in an embodiment of the present invention;

[0055] Figure 4 is a schematic structural diagram of a real-time short-circuit ratio measurement device for new energy power stations based on Kalman filtering disclosed in an embodiment of the present invention. Detailed Embodiments

[0056] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the specific beneficial effects described above, and the above and other purposes that the present invention can achieve will be more clearly understood from the following detailed description.

[0057] Those of ordinary skill in the art should understand that the various exemplary components, systems, and methods described in connection with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Specifically, whether to implement in hardware or software depends on the specific application and design and tree conditions of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0058] As used in this invention, "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0059] Embodiment 1

[0060] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a real-time short-circuit ratio measurement method for a new energy power station based on Kalman filtering disclosed in an embodiment of the present invention. Among them, Figure 1 the described real-time short-circuit ratio measurement method for a new energy power station is applied to a power system, such as for measuring the real-time short-circuit ratio of the system after large-scale new energy is connected to the power system, etc., which is not limited in the embodiments of the present invention. As Figure 1 shown, the real-time short-circuit ratio measurement method for a new energy power station based on Kalman filtering may include the following operations:

[0061] S1. Construct a Thevenin equivalent circuit for the new energy power station connected to the AC system; input a reference voltage to the Thevenin equivalent circuit, and measure the voltages and currents at two different moments at the point of common coupling (PCC point).

[0062] As Figure 2 shown, the AC system to which the new energy power station is connected can be equivalent to a series of a voltage source and an equivalent impedance through the Thevenin equivalent method.

[0063] S2. Use Kalman filtering to filter the measured voltage and current values to obtain the values of the measured voltage and current after Kalman filtering.

[0064] Use Kalman filtering to predict the parameters of the new energy grid-connected system to obtain predicted values; calculate the Kalman gain according to the predicted values; use the Kalman gain to correct the error of the predicted values. The specific steps are as follows:

[0065] The available state space equation of the real-time short-circuit ratio measurement system can be described as:

[0066] x k = Ax k-1 + Bu k-1 + w k-1 (1)

[0067] z k = Hx k + v k (2)

[0068] In the formula, x k and xk-1 respectively represent the state variables of the current step and the previous step, which are the measured values at the grid connection point; A and B are the coefficient matrices of the state variables and the input variables of the system respectively; u k-1 represents the input variable of the previous step; w k-1 represents the process error; z k represents the current measured value; H represents the linear relationship between the true value and the measured value; v k represents the observation error; among them, the process error w k-1 and the observation error v k can be assumed to satisfy the Gaussian distribution:

[0069] p(w) ∼ N(0, Q) (3)

[0070] p(v) ∼ N(0, R) (4)

[0071] In the formula, p represents probability; N represents the Gaussian distribution; Q and R represent the covariance of the Gaussian distributions of the process error and the observation error respectively.

[0072] Using the Kalman filter for state estimation of the new energy grid-connected system needs to be carried out in two steps:

[0073] The first step: Predict the system as shown in the following formula:

[0074] Deduce the state quantity:

[0075]

[0076] In the formula, is the predicted value of the state variable of the current step, which is the result predicted according to the previous state and has not been corrected according to the observation information; is the estimated value of the state variable of the previous step.

[0077] Deduce the error covariance:

[0078]

[0079] In the formula, P k-1 represents the previous value of the posterior error covariance matrix; represents the prior error covariance matrix; Q is the covariance of the Gaussian distribution of the process error in formula (3).

[0080] The second step: Correct the parameters of the new energy grid-connected system to obtain the optimal estimated value as shown in the following formula:

[0081] Calculate the Kalman gain:

[0082]

[0083] In the formula, Kk denotes the Kalman gain, which is solved according to the goal of minimizing the posterior error and is used to correct the error of the predicted value; denotes the prior error covariance matrix; H denotes the linear relationship between the true value and the measured value; H T denotes the transpose matrix of H; R is the covariance of the observed error Gaussian distribution in formula (4).

[0084] Update the state estimate:

[0085]

[0086] where denotes the optimal estimate of the state variable; z k denotes the current measured value;

[0087] Update the error covariance:

[0088]

[0089] where P k denotes the current step value of the posterior error covariance matrix; I denotes the identity matrix, whose diagonal elements are 1 and the rest are 0.

[0090] The Kalman filter solves the nonlinear problem through local linearization and performs a first-order Taylor expansion at the mean value to calculate the real-time short-circuit ratio. The system parameters are corrected by the Kalman filter to improve the accuracy of the real-time short-circuit ratio.

[0091] S3. Use the filtered voltage and current values to calculate the system parameters of the Thevenin equivalent circuit.

[0092] Assume that the voltage and current values at the connection points of two new energy power stations are known. According to the d-axis representing the real part and the q-axis representing the imaginary part, the following equations can be written:

[0093] U d1 = E d + RI d1 - XI q1 (10)

[0094] U q1 = E q + XI d1 + RI q1 (11)

[0095] U d2 = E d + RI d2 - XI q2 (12)

[0096] U q2 = E q+XI d2 +RI q2 (13)

[0097] Writing the above formula in matrix form, the equivalent voltage E and equivalent impedance Z of the grid-side power supply can be obtained. eq The expressions of the system parameters of the Thevenin equivalent circuit are as follows:

[0098]

[0099] In the formula, E d represents the d-axis value of the equivalent voltage of the grid-side power supply; E q represents the q-axis value of the equivalent voltage of the grid-side power supply; R represents the resistance value of the line equivalent impedance; X represents the inductance value of the line equivalent impedance; I d1 and I d2 respectively represent the first group and the second group of values of the current d-axis; U d1 and U d2 respectively represent the first group and the second group of values of the voltage d-axis; I q1 and I q2 respectively represent the first group and the second group of values of the current q-axis; U q1 and U q2 respectively represent the first group and the second group of values of the voltage q-axis.

[0100] S4. Calculate the real-time short-circuit ratio of the new energy power station according to the system parameters of the Thevenin equivalent circuit.

[0101] The relationship between the voltage at the grid connection point of the new energy power station and the voltage on the power system side is as follows:

[0102]

[0103] The short-circuit capacity of the system at node i can be approximately expressed as:

[0104]

[0105] According to the definition of the short-circuit ratio, the real-time short-circuit ratio can be expressed as:

[0106]

[0107] In the formula, R SCRi represents the real-time short-circuit ratio of node i; S aci is the short-circuit capacity of node i; U REi represents the voltage on the power system side of node i; U N represents the rated voltage of the system; E i represents the voltage at the grid connection point of node i; I i represents the current at the grid connection point of node i.

[0108] For the technical solution of the present invention, first, the voltage and current values of two grid connection points are measured, the value of the grid-side equivalent voltage source is calculated according to formula (14), and then the measured grid connection point voltage and the value of the grid-side equivalent voltage source are filtered by the Kalman filter and substituted into formula (17) to solve the real-time short-circuit ratio.

[0109] For the measurement of the short-circuit ratio of new energy power stations with a large number of power electronic converters, the present invention proposes a real-time measurement method and system for the short-circuit ratio of new energy power stations based on Kalman filtering, which can identify the changes in the short-circuit ratio of new energy power stations under different working conditions and provide support for the judgment of grid strength. By constructing the Thevenin equivalent circuit of the new energy power station connected to the AC system and based on the voltage and current measurement values at two different times, the system parameters are calculated in real time through Kalman filtering to realize the real-time measurement of the short-circuit ratio of the new energy power station.

[0110] Embodiment 2

[0111] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a real-time short-circuit ratio measurement system for a new energy power station based on Kalman filtering disclosed in an embodiment of the present invention. This system can realize the real-time measurement of the short-circuit ratio of the power system and specifically includes:

[0112] A measurement module for constructing the Thevenin equivalent circuit of the new energy power station connected to the AC system; inputting a reference voltage to the Thevenin equivalent circuit and measuring the voltage and current at two different times at the grid connection point;

[0113] A filtering module for filtering the measured voltage and current values by using Kalman filtering;

[0114] A system parameter calculation module for calculating the system parameters of the Thevenin equivalent circuit by using the filtered voltage and current values;

[0115] A real-time short-circuit ratio calculation module for calculating the real-time short-circuit ratio of the new energy power station according to the system parameters of the Thevenin equivalent circuit.

[0116] In an optional embodiment, the real-time short-circuit ratio measurement method for a new energy power station based on Kalman filtering includes: a) constructing the Thevenin equivalent circuit of the new energy power station connected to the AC system; inputting a reference voltage to the Thevenin equivalent circuit and measuring the voltage and current at two different times at the grid connection point; b) filtering the measured voltage and current values by using Kalman filtering; c) calculating the system parameters of the Thevenin equivalent circuit by using the filtered voltage and current values; d) calculating the real-time short-circuit ratio of the new energy power station according to the system parameters of the Thevenin equivalent circuit.

[0117] Embodiment 3

[0118] Please refer to Figure 4 , Figure 4It is a schematic structural diagram of a real-time short-circuit ratio measurement device for new energy power stations based on Kalman filtering disclosed in an embodiment of the present invention. Among them, Figure 4 The described device can be applied to the power system, such as for measuring the real-time short-circuit ratio of the system after large-scale new energy access to the power system, etc., which is not limited in the embodiments of the present invention.

[0119] Such as Figure 4 As shown, the device may include a processor and a memory. Computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiments and can achieve the same technical effects as the above method.

[0120] The memory may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write a non-removable, non-volatile magnetic medium (commonly referred to as a "hard disk drive"). Programs / utilities having a set (at least one) of program modules may be stored, for example, in the memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples. The program modules generally execute the functions and / or methods in the embodiments described in the present invention.

[0121] The processor executes various functional applications and data processing by running the programs stored in the memory, such as implementing the method provided in Embodiment 1 of the present invention.

[0122] Embodiment 4

[0123] Embodiment 4 of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the method described in the above embodiments and can achieve the same technical effects as the above method.

[0124] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0125] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, and this computer-readable media can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0126] The program codes contained on the computer-readable media can be transmitted by any appropriate media, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0127] The computer program codes for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0128] Of course, for the storage medium containing computer-executable instructions provided by the embodiments of the present invention, the computer-executable instructions are not limited to the above method operations, and can also execute relevant operations in the methods provided by any embodiments of the present invention.

[0129] The specific embodiments described above have further elaborated on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A real-time short-circuit ratio measurement method for new energy stations based on Kalman filtering, characterized in that: include: Construct the Thevenin equivalent circuit for connecting new energy stations to the AC system; Input a reference voltage into the Thevenin equivalent circuit and measure the voltage and current of two groups of grid connection points at different times; The measured voltage and current values ​​are filtered by using Kalman filtering to obtain the values ​​of the measured voltage and current after Kalman filtering; The filtered voltage and current values ​​are used to calculate the system parameters of the Thevenin equivalent circuit; The real-time short-circuit ratio of the new energy station is calculated based on the system parameters of the Thevenin equivalent circuit.

2. According to the Kalman filter-based real-time short-circuit ratio measurement method for new energy stations in claim 1, it is characterized in that: The method of filtering the measured voltage and current values ​​by using Kalman filtering includes: The Kalman filter is used to predict the parameters of the renewable energy grid-connected system to obtain the predicted value; the Kalman gain is calculated according to the predicted value; and the Kalman gain is used to correct the error of the predicted value.

3. According to the real-time short-circuit ratio measurement method of new energy stations based on Kalman filtering in claim 2, it is characterized in that: The method of using Kalman filtering to predict the parameters of the new energy grid-connected system to obtain the predicted values ​​includes: The real-time short-circuit ratio measurement system can be described by the state space equation: x k =Ax k-1 +Bu k-1 +w k-1 z k =Hx k +v k In the formula, x k and x k-1 represents the state variables of the current step and the previous step respectively, and is the measured value of the grid connection point; A and B are the coefficient matrix of the state variables and the coefficient matrix of the input variables of the system respectively; u k-1 represents the input variable of the previous step; w k-1 represents process error; z k Indicates the current measured value; H indicates the linear relationship between the true value and the measured value; v k represents the observation error; where the process error w k-1 and the observation error v k Assume that the Gaussian distribution is satisfied: p(w)~N(0,Q) p(v)~N(0,R) In the formula, p represents probability, N represents Gaussian distribution, Q and R represent the covariance of Gaussian distribution of process error and observation error respectively; Estimated state quantity: In the formula, is the predicted value of the state variable of the current step; is the estimated value of the state variable in the previous step; Estimated error covariance: Where P k-1 represents the previous step value of the posterior error covariance matrix; represents the prior error covariance matrix; Q represents the covariance of the process error Gaussian distribution.

4. According to the real-time short-circuit ratio measurement method of new energy stations based on Kalman filtering in claim 2, it is characterized in that: The expression of the Kalman gain is as follows: In the formula, K k represents the Kalman gain; represents the prior error covariance matrix; H represents the linear relationship between the true value and the measured value; H T represents the transposed matrix of H; R represents the covariance of the Gaussian distribution of the observation error.

5. According to the real-time short-circuit ratio measurement method of new energy stations based on Kalman filtering in claim 4, it is characterized in that: The method of using the Kalman gain to correct the error of the predicted value includes: Update the state estimate: In the formula, represents the optimal estimate of the state variable; Indicates the result predicted based on the previous state; z k Indicates the current measurement value; Update the error covariance: Where P k represents the current step value of the posterior error covariance matrix; I represents the identity matrix.

6. The real-time short-circuit ratio measurement method for new energy stations based on Kalman filtering according to claim 1 is characterized in that: The expressions of the system parameters of the Thevenin equivalent circuit are as follows: In the formula, E d The d-axis value of the equivalent voltage of the grid-side power supply; E q The q-axis value of the equivalent voltage of the grid-side power supply; R represents the resistance value of the line equivalent impedance; X represents the inductance value of the line equivalent impedance; I d1 and I d2 Respectively represent the first and second group values ​​of the current d axis; U d1 and U d2 Respectively represent the first and second group values ​​of the voltage d axis; I q1 and I q2 Respectively represent the first and second group values ​​of the current q axis; U q1 and U q2 Represent the first and second set of values ​​of the voltage q axis respectively.

7. The real-time short-circuit ratio measurement method for new energy stations based on Kalman filtering according to claim 1 is characterized in that: The expression of the real-time short-circuit ratio of the new energy station is as follows: In the formula, R SCRi represents the real-time short-circuit ratio of node i; S aci is the short-circuit capacity of node i; U REi represents the voltage on the power system side of node i; U N Indicates the system rated voltage; E i I represents the grid-connected voltage of node i; i Represents the grid-connected current at node i.

8. A real-time short-circuit ratio measurement system for new energy stations based on Kalman filtering, characterized in that: include: The measurement module is used to construct the Thevenin equivalent circuit of the new energy station connected to the AC system; Input a reference voltage into the Thevenin equivalent circuit and measure the voltage and current of two groups of grid connection points at different times; A filtering module is used to filter the measured voltage and current values ​​using Kalman filtering to obtain the values ​​of the measured voltage and current after Kalman filtering; A system parameter calculation module, used to calculate the system parameters of the Thevenin equivalent circuit using the filtered voltage and current values; The real-time short-circuit ratio calculation module is used to calculate the real-time short-circuit ratio of the new energy station according to the system parameters of the Thevenin equivalent circuit.

9. A real-time short-circuit ratio measurement device for new energy stations based on Kalman filtering, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the real-time short-circuit ratio measurement method of a new energy station based on Kalman filtering as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the real-time short-circuit ratio measurement method for a new energy station based on Kalman filtering as described in any one of claims 1 to 7.

Images