Alternating current transmission line state estimation fault phase selection method, system, equipment and medium

Abstract

The invention relates to the technical field of power system relay protection, in particular to an alternating-current transmission line state estimation fault phase selection method, system and device and a medium, and the method comprises the steps: mapping mutually coupled three-phase voltage and current components in a three-phase alternating-current transmission line from a phase domain to a mode domain space; mode domain voltage and current components of the new energy side and the power grid side are obtained; constructing a system measurement equation based on the mode domain voltage and current components to obtain an optimal estimation value; calculating a residual normalized sum of squares according to the optimal estimation value, and screening out an intra-region fault line by comparing the residual normalized sum of squares with a chi-square distribution threshold value; and performing secondary decoupling on the same fault current data of the fault line in the area, and determining the fault phase according to the residual normalized quadratic sum ratio of each mode domain component under the multi-mode phase-mode transformation combination. Through the state estimation method combining phase-mode transformation and the Bergeron model, rapid and accurate phase selection of the AC power transmission line fault is realized, and the fault identification precision is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of relay protection for power systems, and particularly to a method, system, device and medium for fault phase selection in state estimation of AC transmission lines. Background Art

[0002] In the context of large-scale access of new energy to the power system, the protection of AC transmission lines faces unprecedented challenges. With the annual increase in the power generation capacity of new energy power stations such as wind power and photovoltaic power, the structural characteristics of the power system have changed significantly. Traditional AC power frequency quantity protection gradually exposes many adaptability problems when dealing with large-scale and high-proportion new energy access. Its fault characteristics are significantly different from those of synchronous generators, resulting in an increased risk of protection refusal or misoperation. A large number of power electronic devices in new energy power sources are extremely sensitive to overvoltage and overcurrent, and have a weak ability to withstand fault impacts. Current limiting measures are often required, which not only weakens the sensitivity of overcurrent protection and differential protection, but also makes the fault response process more complex and the uncertainty stronger.

[0003] In traditional power systems, the protection of AC transmission lines mainly relies on classical AC power frequency quantity protection, such as pilot differential protection, distance protection, etc. However, with the annual increase in the access capacity of new energy, but still limited by the characteristics of traditional synchronous machines, the adaptability problems of these classical protection principles in new energy power systems are gradually exposed, and they cannot ensure the safe and stable operation of the outgoing lines of new energy power stations; the other is the new protection principle based on fault transient characteristics. The new protection principle based on fault transient characteristics is divided into single-ended quantity protection principle and double-ended quantity protection principle. Although the action speed is relatively fast, there are also many problems in practical applications. For example, the protection scheme based on traveling wave characteristics has a protection dead zone at the voltage zero crossing, requires a high protection sampling rate, and is easily affected by noise interference and transition resistance.

[0004] As an emerging protection technology, the state estimation protection principle for AC transmission lines provides a new solution to the problem of deteriorated traditional protection performance with its advantages of fast action speed, strong reliability, and not being affected by external system control. However, at present, the fault phase selection function of AC line state estimation protection can only identify the fault phase of single-phase grounding faults, and it is difficult to accurately determine the fault phases of two-phase short circuit faults, two-phase short circuit grounding faults and three-phase faults, which brings difficulties to fault isolation and fault analysis. Therefore, the existing technology has many deficiencies in meeting the protection requirements of AC outgoing lines of new energy power stations, especially the defect in the fault phase selection function, which seriously restricts the overall performance of the protection system. There is an urgent need to provide a fault phase selection method that can effectively solve the defects of the existing technology to improve the reliability of AC transmission line state estimation protection. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method, system, device and medium for fault phase selection in the state estimation of AC transmission lines.

[0006] In the first aspect, the present invention provides a method for fault phase selection in the state estimation of AC transmission lines, the method comprising the following steps: Using a phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space, obtaining the mode domain voltage and current components on the new energy side and the grid side; Based on the Bergeyron transmission line model, equivalent a uniformly lossy transmission line into two lossless transmission lines in series with four lumped resistors, and taking the mode domain voltage and current components as inputs, constructing a system measurement equation through state variables; Adopting a weighted least squares estimation model to solve the system measurement equation, obtaining the optimal estimated values of the state variables; According to the optimal estimated values, calculating the residual normalized sum of squares of the measurement estimated values and the actual measurement values in the mode domain, and screening out the in-zone fault lines by comparing the residual normalized sum of squares with the chi-square distribution threshold; Using a multi-mode phase-mode transformation combination to decouple the same fault current data of the in-zone fault line for the second time, generating the residual normalized sum of squares of each mode domain component under the multi-mode phase-mode transformation combination; According to the ratio of the residual normalized sum of squares of each mode domain component under the multi-mode phase-mode transformation combination, determining the fault phase, and generating a corresponding tripping command according to the fault phase to control the circuit breaker to act to complete fault isolation.

[0007] In a further embodiment, the step of using a phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space, obtaining the mode domain voltage and current components on the new energy side and the grid side includes: Real-time collecting the instantaneous sampling values of the three-phase voltage and the instantaneous sampling values of the three-phase current on both sides of the AC transmission line, obtaining the three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the grid side of the AC transmission line; Mapping the three-phase voltage and current components from the phase domain to the mode domain space, obtaining the mode domain voltage and current components on the new energy side and the grid side; the mode domain voltage and current components include zero-mode components, one-mode components and two-mode components.

[0008] In a further embodiment, the step of based on the Bergeyron transmission line model, equivalent a uniformly lossy transmission line into two lossless transmission lines in series with four lumped resistors, and taking the mode domain voltage and current components as inputs, constructing a system measurement equation through state variables includes: Obtaining the parameters of the AC transmission line; the parameters of the AC transmission line include the resistance per unit length, the inductance per unit length, the capacitance per unit length and the total length of the line; Based on the parameters of the AC transmission line, using the Bergeron transmission line model, the uniform lossy AC transmission line is equivalent to a form of two lossless AC transmission lines connected in series with four lumped resistors, and the lossless transmission line model after equivalent conversion is obtained; According to the lossless transmission line model after equivalent conversion, the key parameters of the lossless transmission line are calculated; the key parameters of the lossless transmission line include characteristic impedance, propagation time, and attenuation coefficient; According to the key parameters of the lossless transmission line and the modal voltage and current components, a dynamic voltage-current relationship equation between the new energy side and the grid side of the AC transmission line is established; Define the estimated values of the modal voltage and modal current on both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

[0009] In a further embodiment, the steps of solving the system measurement equation using the weighted least squares estimation model to obtain the optimal estimated value of the state variable include: According to the difference between the true measurement value in the system measurement equation and the virtual measurement value estimated based on the current state variable, a residual vector is constructed; the true measurement value is the modal voltage and current components on the new energy side and the grid side; Taking the minimization of the weighted sum of squares of the residual vector as the optimization objective, a residual weighted sum of squares minimization objective function is constructed; Use the Jacobian matrix to relate the state variable to the actual measurement value, and use the weighted least squares method to solve the residual weighted sum of squares minimization objective function to obtain the optimal estimated value of the state variable; the actual measurement value includes the true measurement value and the virtual measurement value.

[0010] In a further embodiment, the steps of calculating the normalized sum of squares of the residuals between the measured estimated value and the actual measurement value in the modal domain according to the optimal estimated value, and screening out the faulty line within the zone by comparing the normalized sum of squares of the residuals with the chi-square distribution threshold include: Input the optimal estimated value into the system measurement equation, calculate the measured estimated value in the modal domain, and calculate the residual estimate between the measured estimated value and the actual measurement value; Normalize the residual estimate to obtain the normalized residual, and calculate the sum of squares of the normalized residual according to the total number of actual measurement values to obtain the normalized sum of squares of the residuals; According to the difference between the total number of actual measurement values and the number of state variables in the system measurement equation, determine the degrees of freedom, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table; Compare the calculated normalized sum of squared residuals with the chi-square distribution threshold. If the normalized sum of squared residuals is greater than the chi-square distribution threshold, it is determined that a fault has occurred within the line, and the faulty line within the zone is screened out; otherwise, the line is operating normally.

[0011] In a further embodiment, the faults within the zone include single-phase ground faults within the zone, two-phase short-circuit faults within the zone, and three-phase ground faults within the zone.

[0012] In a further embodiment, the multi-mode phase-mode transformation combination includes a standard phase sequence transformation, a cyclic phase sequence transformation, and a reverse phase sequence transformation.

[0013] In a second aspect, the present invention provides a fault phase selection system for the state estimation of an AC transmission line, and the system includes: A phase-mode mapping module, configured to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space by using a phase-mode transformation matrix, and obtain the mode domain voltage and current components on the new energy side and the grid side; A measurement construction module, configured to equivalent a uniform lossy transmission line into two lossless transmission lines connected in series with four lumped resistors based on the Bergeyron transmission line model, and construct a system measurement equation with the mode domain voltage and current components as inputs through state variables; An optimal estimation module, configured to solve the system measurement equation by using a weighted least squares estimation model to obtain the optimal estimated values of the state variables; A fault analysis module, configured to calculate the normalized sum of squared residuals between the measured estimated values and the actual measured values in the mode domain according to the optimal estimated values, and screen out the faulty line within the zone by comparing the normalized sum of squared residuals with the chi-square distribution threshold; A secondary decoupling module, configured to perform secondary decoupling on the same fault current data of the faulty line within the zone by using the multi-mode phase-mode transformation combination to generate the normalized sum of squared residuals of each mode domain component under the multi-mode phase-mode transformation combination; A fault phase selection module, configured to determine the fault phase according to the ratio of the normalized sum of squared residuals of each mode domain component under the multi-mode phase-mode transformation combination, and generate a corresponding tripping command according to the fault phase to control the circuit breaker to act to complete fault isolation.

[0014] In a further embodiment, the phase-mode mapping module is specifically configured to: Real-time collect the instantaneous sampling values of the three-phase voltage and the instantaneous sampling values of the three-phase current on both sides of the AC transmission line to obtain three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the grid side of the AC transmission line; Map the three-phase voltage and current components from the phase domain to the mode domain space to obtain the mode domain voltage and current components on the new energy side and the grid side; the mode domain voltage and current components include a zero-mode component, a one-mode component, and a two-mode component.

[0015] In a further embodiment, the measurement construction module is specifically configured to: Obtain AC transmission line parameters; the AC transmission line parameters include resistance per unit length, inductance per unit length, capacitance per unit length, and total line length; Based on the AC transmission line parameters, use the Bergeron transmission line model to equivalent the uniform lossy AC transmission line into a form of two lossless AC transmission lines in series with four lumped resistors, and obtain the equivalent converted lossless transmission line model; According to the equivalent converted lossless transmission line model, calculate the key parameters of the lossless transmission line; the key parameters of the lossless transmission line include characteristic impedance, propagation time, and attenuation coefficient; According to the key parameters of the lossless transmission line and the mode domain voltage and current components, establish a dynamic voltage-current relationship equation between the new energy side and the grid side of the AC transmission line; Define the mode domain voltage estimate and mode domain current estimate at both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

[0016] In a further embodiment, the optimal estimation module is specifically configured to: According to the difference between the true measurement value in the system measurement equation and the virtual measurement value estimated based on the current state variables, construct a residual vector; the true measurement value is the mode domain voltage and current components on the new energy side and the grid side; With the minimization of the weighted sum of squares of the residual vector as the optimization objective, construct a residual weighted sum of squares minimization objective function; Use the Jacobian matrix to relate the state variables to the actual measurement values, and use the weighted least squares method to solve the residual weighted sum of squares minimization objective function to obtain the optimal estimated value of the state variables; the actual measurement values include the true measurement value and the virtual measurement value.

[0017] In a further embodiment, the fault analysis module is specifically configured to: Input the optimal estimated value into the system measurement equation, calculate the measurement estimate value in the mode domain, and calculate the residual estimate between the measurement estimate value and the actual measurement value; Perform normalization processing on the residual estimate to obtain a normalized residual, and calculate the sum of squares of the normalized residual according to the total number of actual measurement values to obtain a residual normalized sum of squares; According to the difference between the total number of actual measurement values and the number of state variables in the system measurement equation, determine the degrees of freedom, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table; Compare the calculated normalized sum of squares of residuals with the chi-square distribution threshold. If the normalized sum of squares of residuals is greater than the chi-square distribution threshold, it is determined that a fault has occurred within the line, and the faulty line within the zone is screened out; otherwise, the line is operating normally.

[0018] In a further embodiment, the faults within the zone include single-phase ground faults within the zone, two-phase short-circuit faults within the zone, and three-phase ground faults within the zone.

[0019] In a further embodiment, the multi-mode phase-mode transformation combination includes standard phase sequence transformation, cyclic phase sequence transformation, and reverse phase sequence transformation.

[0020] In a third aspect, the present invention also provides a computer device, including a processor and a memory. The processor is connected to the memory. The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device performs the steps of implementing the above method.

[0021] In a fourth aspect, the present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method are implemented.

[0022] The present invention provides a method, system, device, and medium for fault phase selection in the state estimation of AC transmission lines. The method maps the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space through a phase-mode transformation matrix to obtain the mode-domain voltage and current components on the new energy side and the grid side. Based on the Berreman transmission line model, a uniformly lossy transmission line is equivalent to two lossless transmission lines connected in series with four lumped resistors, and with the mode-domain voltage and current components as inputs, a system measurement equation is constructed through state variables. A weighted least squares estimation model is used to solve the system measurement equation to obtain the optimal estimated values of the state variables. According to the optimal estimated values, the normalized sum of squares of the residuals between the measured estimated values and the actual measured values in the mode domain is calculated, and the faulty line within the zone is screened out by comparing the normalized sum of squares of the residuals with the chi-square distribution threshold. The same fault current data of the faulty line within the zone is decoupled twice using the multi-mode phase-mode transformation combination to generate the normalized sum of squares of the residuals of each mode-domain component under the multi-mode phase-mode transformation combination. According to the ratio of the normalized sum of squares of the residuals of each mode-domain component under the multi-mode phase-mode transformation combination, the faulty phase is determined, and a corresponding tripping command is generated according to the faulty phase to control the circuit breaker to operate to complete fault isolation. Compared with the prior art, this method uses a state estimation method combining phase-mode transformation and the Berreman model to achieve fast and accurate fault phase selection for AC transmission lines, automatically generates tripping commands to isolate the fault area, effectively improves the fast detection and precise positioning of faults within the zone of AC transmission lines, and ensures the stable operation of the power grid. Description of the Drawings

[0023] Figure 1 It is a schematic flow chart of the method for fault phase selection in the state estimation of an AC transmission line provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the Bergeon transmission line model provided by an embodiment of the present invention; Figure 3 It is a process block diagram of the protection fault phase selection in the state estimation of an AC transmission line provided by an embodiment of the present invention; Figure 4 It is a topological example diagram of a new energy transmission system via a flexible DC provided by an embodiment of the present invention; Figure 5 It is a system block diagram of the fault phase selection in the state estimation of an AC transmission line provided by an embodiment of the present invention; Figure 6 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention.

[0024] Explanation of reference numerals: 101, phase-mode mapping module; 102, measurement construction module; 103, optimal estimation module; 104, fault analysis module; 105, secondary decoupling module; 106, fault phase selection module. Detailed implementation manners

[0025] The following specifically illustrates the implementation manners of the present invention in conjunction with the accompanying drawings. The given embodiments are only for illustrative purposes and should not be construed as limiting the present invention. The accompanying drawings are only for reference and illustration and do not constitute a limitation on the scope of protection of the present invention patent, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0026] Refer to Figure 1 , an embodiment of the present invention provides a method for fault phase selection in the state estimation of an AC transmission line. As Figure 1 shown, the method includes the following steps: S1. Use a phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space, and obtain the mode domain voltage and current components on the new energy side and the grid side.

[0027] In some implementation manners, the step of using a phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the mode domain space and obtaining the mode domain voltage and current components on the new energy side and the grid side includes: Real-time collect the instantaneous sampling values of the three-phase voltage and the instantaneous sampling values of the three-phase current on both sides of the AC transmission line to obtain the three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the grid side of the AC transmission line; Map the three-phase voltage and current components from the phase domain to the modal domain space to obtain the modal domain voltage and current components on the new energy side and the grid side; the modal domain voltage and current components include zero-mode components, one-mode components, and two-mode components.

[0028] Specifically, in this embodiment, voltage transformers and current transformers are installed on the new energy side and the grid side of the AC transmission line respectively, and it is ensured that these sampling devices such as voltage transformers and current transformers have high precision and real-time performance, and can accurately capture the instantaneous changes of voltage and current. Then, the sampling devices are started in real time to start collecting the three-phase voltage instantaneous sampling values and three-phase current instantaneous sampling values on the new energy side and the grid side, and preprocess the three-phase voltage instantaneous sampling values and three-phase current instantaneous sampling values, such as denoising, filtering, etc., to improve the accuracy of the data. At the same time, in this embodiment, a Karenbauer transformation matrix is constructed according to the definition of the Karenbauer transformation. The Karenbauer transformation matrix is a 3x3 matrix, and this phase-modal transformation matrix is used to transform the three-phase voltage and current components from the phase domain to the modal domain. The specific form of the Karenbauer transformation matrix S is: For the three-phase voltage instantaneous sampling values and three-phase current instantaneous sampling values on the new energy side and the grid side, this embodiment respectively uses the Karenbauer transformation matrix S for transformation to obtain the zero-mode components, one-mode components, and two-mode components on the new energy side and the grid side. These modal domain components are independent of each other and are no longer coupled to each other like the phase domain components. The specific transformation formula is: In the formula, S is the Karenbauer transformation matrix; is the zero-mode component of the modal domain current on the new energy side or the grid side; is the one-mode component of the modal domain current on the new energy side or the grid side; is the two-mode component of the modal domain current on the new energy side or the grid side; is the instantaneous sampling value of the A-phase current on the new energy side or the grid side; is the instantaneous sampling value of the B-phase current on the new energy side or the grid side; is the instantaneous sampling value of the C-phase current on the new energy side or the grid side; is the zero-mode component of the modal domain voltage on the new energy side or the grid side; is the one-mode component of the modal domain voltage on the new energy side or the grid side; is the two-mode component of the modal domain voltage on the new energy side or the grid side; is the instantaneous sampling value of the A-phase voltage on the new energy side or the grid side; is the instantaneous sampling value of the B-phase voltage on the new energy side or the grid side; is the instantaneous sampled value of the C-phase voltage on the new energy side or the grid side; respectively represent the names of the new energy side and the grid side of the AC line; it should be noted that in the subsequent analysis, unless otherwise specified, it represents the modal domain, and it is no longer explicitly pointed out whether it is the 0 mode, 1 mode or 2 mode.

[0029] S2. Based on the Bergeon transmission line model, the uniform lossy transmission line is equivalent to two lossless transmission lines in series with four lumped resistors, and taking the modal voltage and current components as inputs, a system measurement equation is constructed through state variables.

[0030] In some embodiments, the step of equivalenting the uniform lossy transmission line to two lossless transmission lines in series with four lumped resistors based on the Bergeon transmission line model and constructing a system measurement equation through state variables with the modal voltage and current components as inputs includes: Obtain the AC transmission line parameters; the AC transmission line parameters include the resistance per unit length, the inductance per unit length, the capacitance per unit length, and the total line length; Based on the AC transmission line parameters, use the Bergeon transmission line model to equivalent the uniform lossy AC transmission line to the form of two lossless AC transmission lines in series with four lumped resistors, and obtain the equivalent converted lossless transmission line model; According to the equivalent converted lossless transmission line model, calculate the key parameters of the lossless transmission line; the key parameters of the lossless transmission line include the characteristic impedance, the propagation time, and the attenuation coefficient; According to the key parameters of the lossless transmission line and the modal voltage and current components, establish the dynamic voltage-current relationship equation between the new energy side and the grid side of the AC transmission line; Define the estimated values of the modal voltage and modal current on both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

[0031] Specifically, for a given AC transmission line, in this embodiment, the AC transmission line parameters can be obtained from the power system database or through on-site measurement. The AC transmission line parameters include the resistance per unit length , the inductance per unit length , the capacitance per unit length , and the total line length , these AC transmission line parameters reflect the electrical characteristics and physical structure of the line. The Beroulli transmission line model is an equivalent method for dealing with lossy transmission lines. It regards a lossy transmission line as a combination of a lossless transmission line and a concentrated resistance. In this embodiment, according to the definition of the Beroulli model and the AC transmission line parameters, a uniformly lossy AC transmission line is equivalently transformed according to the Beroulli model to construct an equivalent lossless transmission line model. Two lossless transmission lines and four concentrated resistances are connected according to the structure of the Beroulli model to form an equivalent transmission line model. The two lossless transmission lines are located on both sides of the line respectively, and a concentrated resistance is connected in series at each end of each transmission line to simulate the lossy characteristics of the line, so that the whole line is equivalently in the form of two lossless transmission lines connected in series with four concentrated resistances. The resistance value of the concentrated resistance is determined by the line parameters and length, that is, the resistance connected in series at both ends of each lossless transmission line is the product of the resistance per unit length of the line and half of the line length. In this embodiment, the length of each lossless transmission line is taken as half of the original line length, that is , the Beroulli transmission line model is as Figure 2 shown. In Figure 2 , the r side represents the new energy side of the AC transmission line, and the g side represents the grid side of the AC transmission line.

[0032] In this embodiment, according to the equivalent lossless transmission line model after equivalent conversion, the equivalent characteristic impedance, propagation time, and attenuation coefficient of the lossless transmission line are calculated. Specifically, the characteristic impedance of the lossless transmission line is one of its important parameters, which reflects the transmission characteristics of the line. In this embodiment, according to the inductance per unit length and capacitance per unit length of the line, the equivalent characteristic impedance can be calculated by the following formula: The characteristic impedance formula shows that the characteristic impedance is proportional to the square root of the inductance and capacitance per unit length, which reflects the influence of the inductive and capacitive characteristics of the line on the transmission characteristics. At the same time, the propagation time represents the time required for an electromagnetic wave to propagate in the transmission line, which is related to the length of the line and the signal propagation speed. The propagation time can be calculated by the following formula: In the formula, is the total length of the line. The propagation time reflects the delay situation of the signal in the transmission line and is of great significance for analyzing the dynamic response and time-domain characteristics of the line.

[0033] The attenuation coefficient reflects the attenuation effect of line loss on the traveling wave. The attenuation coefficient can be calculated by the following formula: In the modal domain (i.e., positive sequence, negative sequence, or zero sequence components), this embodiment considers the characteristics of a lossless transmission line and establishes a dynamic voltage-current relationship equation between the new energy side and the grid side by using the characteristic impedance, propagation time, and modal domain voltage and current components of the lossless transmission line. This equation describes the dynamic relationship between the voltages and currents on both sides of the line, taking into account the propagation delay of the signal and the transmission characteristics of the line. Therefore, this equation contains coupling terms at the current moment and the historical moment (delay ) and is in the following specific form: In the formula, is the instantaneous sampled value of the current on the new energy side; is the equivalent characteristic impedance; is the instantaneous sampled value of the voltage on the new energy side; is the attenuation coefficient; is the instantaneous sampled value of the voltage on the grid side; t is time; is the traveling wave propagation time; is the instantaneous sampled value of the current on the grid side; is the response of the current after time at the other end.

[0034] Using the dynamic voltage-current relationship equation shown above, this embodiment can conveniently calculate the voltages and currents on both sides of the line. Moreover, considering the influence of distributed capacitance during the modeling process reduces the calculation error, and it can be used for the research of dynamic state estimation protection. Since this embodiment has equivalent the line to two sections of lossless transmission lines connected in series with four lumped resistors, it is necessary to consider the influence of the resistors in the above equation, that is, the voltage drop across the resistors added at both ends. At the same time, the estimated values of the modal domain voltages and modal domain currents on both sides of the AC transmission line at the current moment are defined as state variables. These state variables reflect the estimated states of the voltages and currents on both sides of the line and are key variables in state estimation and fault detection. The historical values are defined as the measured values and state variables before the delay . This embodiment combines the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation. The system measurement equation relates the measured modal domain voltage and current values to the state variables and is used to describe the relationship between the system state variables and the measured values. The specific form is as follows: From the above formula, the following form can be derived: In the formula, is the estimated value of the modal domain voltage on the new energy side of the AC transmission line at the current moment t; is the estimated value of the modal domain voltage on the grid side of the AC transmission line before time, that is, the delay The state variable before; is a column vector of measurement values, including true measurement values and virtual measurement values; H is the Jacobian matrix; is a column vector of state variables; is a column vector of historical values; e is the measurement error phasor, and it is assumed that the measurement error follows a normal distribution.

[0035] In this embodiment, the complex lossy line is simplified into a combination of a lossless line and a concentrated resistor through the Beranek model, and the three-phase quantities are decoupled by the Karenbauer transformation. Finally, a system measurement equation based on state variables and historical data is constructed, providing a basis for subsequent state estimation and protection criteria. In this embodiment, the system measurement equation includes three types of information: measurement values, state variables, and historical values. The measurement values include true measurement values and virtual measurement values. Among them, the true measurement values are the data directly measured by voltage and current transformers; the virtual measurement values represent the physical laws satisfied by healthy lines, which are usually replaced by 0 in actual situations; the state variables represent the time-varying quantities that are difficult to directly measure in the system measurement equation. These state variables include the estimated current values and voltage values on both sides of the line at the current moment (denoted as , , , , where is the estimated value of the modal current on the new energy side of the AC transmission line at the current moment t; is the estimated value of the modal current on the grid side of the AC transmission line at the current moment t); the historical values include the measurement historical values of voltage and current on both sides of the line and the historical values of state variables.

[0036] S3. Solve the system measurement equation using a weighted least squares estimation model to obtain the optimal estimated values of the state variables.

[0037] In some embodiments, the steps of solving the system measurement equation using a weighted least squares estimation model to obtain the optimal estimated values of the state variables include: Construct a residual vector based on the difference between the true measurement values in the system measurement equation and the virtual measurement values estimated based on the current state variables; the true measurement values are the modal voltage and current components on the new energy side and the grid side; Construct a residual weighted sum of squares minimization objective function with the minimization of the weighted sum of squares of the residual vector as the optimization goal; Use the Jacobian matrix to relate the state variables to the actual measurement values, and use the weighted least squares method to solve the residual weighted sum of squares minimization objective function to obtain the optimal estimated values of the state variables; the actual measurement values include true measurement values and virtual measurement values.

[0038] Specifically, in this embodiment, the system measurement equation can be used to determine that the number of measurement values is greater than the number of state variables, indicating that the system is observable. Based on this determination, this embodiment can further utilize the idea of dynamic state estimation and use mathematical methods to solve for the optimal estimated values of the state variables. This embodiment selects the weighted least squares (WLS) estimation model to solve the state estimation problem. This model constructs the model with the weighted sum of squares of the residuals (i.e., the difference between the measurement value and the measurement estimated value) minimized as the criterion function: When the line parameters are known and the measurement function is in a linear form, the Jacobian matrix H is a constant matrix. Under this condition, considering that the errors of different measurement values are different, this embodiment introduces the weight matrix W to construct the objective function. To solve for the minimum value of the objective function, this embodiment needs to use the Jacobian matrix to relate the state variables and the actual measurement values. During the iterative solution process, this embodiment linearizes the nonlinear equation around its current estimated value and iteratively solves for the minimum value of the objective function. In each iteration, the estimated value of the state variable is updated. When the objective function value converges to a sufficiently small value, the current estimated value of the state variable is considered the optimal estimated value. The optimal estimated value is used for the voltage and current estimated values that are closest to the actual line state. The optimal estimated value of the state variable can be calculated by the following formula: As Figure 3 shown, in this embodiment, after obtaining the optimal estimated value of the state variable, the optimal estimated value can be substituted into the system measurement equation to further obtain the estimated value of the measured quantity and the normalized sum of squares of the residuals. The specific calculation formula is: In the formula, J is the objective function for minimizing the weighted sum of squares of the residuals; W is the weight matrix; is the residual vector; is the optimal estimated value; is the measurement estimated value; is the normalized sum of squares of the residuals; p is the total number of measurement estimated values in the system measurement equation; num is the index of the measurement estimated value in the system measurement equation; is the num-th measurement estimated value; is the num-th measurement value; is the standard deviation of the measured quantity.

[0039] The elements of the Jacobian matrix in this embodiment are the partial derivatives of the measured values in the system measurement equation with respect to the state variables, which reflect the sensitivity of the measured values to the changes in the state variables. By calculating the Jacobian matrix in this embodiment, the non-linear relationship between the state variables and the measured values can be linearized, facilitating subsequent optimization and solution.

[0040] S4. According to the optimal estimated value, calculate the normalized sum of squares of the residuals between the measured estimated value and the actual measured value in the modal domain, and screen out the faulty line within the zone by comparing the normalized sum of squares of the residuals with the chi-square distribution threshold.

[0041] In some embodiments, the step of calculating the normalized sum of squares of the residuals between the measured estimated value and the actual measured value in the modal domain according to the optimal estimated value and screening out the faulty line within the zone by comparing the normalized sum of squares of the residuals with the chi-square distribution threshold includes: Input the optimal estimated value into the system measurement equation, calculate the measured estimated value in the modal domain, and calculate the residual estimate between the measured estimated value and the actual measured value; Perform normalization processing on the residual estimate to obtain the normalized residual, and calculate the sum of squares of the normalized residuals according to the total number of actual measured values to obtain the normalized sum of squares of the residuals; Determine the degrees of freedom according to the difference between the total number of actual measured values and the number of state variables in the system measurement equation, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table; Compare the calculated normalized sum of squares of the residuals with the chi-square distribution threshold. If the normalized sum of squares of the residuals is greater than the chi-square distribution threshold, it is determined that a fault has occurred within the line, and the faulty line within the zone is screened out; otherwise, the line is operating normally.

[0042] In this embodiment, by calculating the normalized sum of squares of the residuals, it is possible to effectively determine whether a fault has occurred within the line. Specifically, when the protected line is in a normal state (i.e., the deviation between the measured value and the measured estimated value is small, and the mathematical model highly matches the physical line), the normalized sum of squares of the residuals will conform to the chi-square distribution. On the contrary, if a fault occurs within the protected line, resulting in a significant increase in the deviation between the measured value and the measured estimated value, and the mathematical model no longer matches the physical line, at this time, the normalized sum of squares of the residuals will deviate from the chi-square distribution and no longer conform to the chi-square distribution. The probability density function of the chi-square distribution is shown in the following formula: In the formula, P(F) is the confidence level; is the normalized sum of squares of the residuals; is the chi-square distribution threshold in the chi-square distribution table, obtained by querying the chi-square distribution table according to the degrees of freedom F; is the probability density function of the chi-square distribution; s is the integration variable; F is the degrees of freedom of the chi-square distribution; is the residual normalized sum of squares greater than the chi-square threshold value confidence level.

[0043] After the system measurement equation is determined, the degree of freedom F of the chi-square distribution is determined accordingly. The confidence level P(F), as an important indicator to measure the matching degree between the mathematical model and the physical circuit, further reflects the characteristics of the chi-square distribution. It can be seen from the probability density curve of the chi-square distribution that when the chi-square distribution value is large, the corresponding probability is low. In this embodiment, by integrating the probability density function, the confidence level can be expressed in the form of the probability density function of the above chi-square distribution, where the chi-square distribution threshold is obtained by querying the chi-square distribution table according to the degree of freedom F; and the confidence level represents the probability that the residual normalized sum of squares value is greater than this threshold value. Therefore, when the residual normalized sum of squares value is large, the confidence level is correspondingly low, which indicates that the value conforms to the chi-square distribution with a small probability, that is, the matching degree between the mathematical model and the physical circuit is low, indicating that the protected line may have an internal fault. On the contrary, when the residual normalized sum of squares value is small, the confidence level is high, indicating that the value conforms to the chi-square distribution with a large probability, that is, the matching degree between the mathematical model and the physical circuit is high, and the protected line is in a normal state.

[0044] Based on the above principle, in this embodiment, the following action criterion is set for the protection device on the new energy r side of the line. When the following action criterion conditions are met, it means that the protection device on the r side of the line sends out a trip outlet signal. At this time, the circuit breaker on the r side of the protected line should trip immediately to ensure the safe and stable operation of the power grid. The specific expression form of the action criterion is: In the formula, is the trip outlet signal sent by the protection device on the new energy side of the line, means trip is required, means no trip; is the window length of the protection action criterion; is the threshold value of the protection action; is the allow signal of the main criterion of the protection device on the new energy side of the line, means trip is allowed, means no trip is allowed; is the residual normalized sum of squares on the new energy side of the line.

[0045] S5. Use the multi-mode phase-mode transformation combination to perform secondary decoupling on the same fault current data of the internal fault line, and generate the residual normalized sum of squares of each mode domain component under the multi-mode phase-mode transformation combination.

[0046] S6. Determine the faulty phase according to the ratio of the residual normalized sum of squares of each mode domain component under the multi-mode phase-mode transformation combination, and generate a corresponding tripping command according to the faulty phase to control the circuit breaker to operate to complete fault isolation.

[0047] The relay protection device for AC transmission lines with a voltage level of 220 kV and above needs to be equipped with a fault phase selection function to ensure accurate judgment and corresponding protection actions in case of a fault. Specifically, when a single-phase grounding fault occurs on the protected line, the protection device should be able to identify the faulty phase and trip the corresponding single-phase circuit breaker; for two-phase short circuit, two-phase short circuit grounding or three-phase short circuit faults, the three-phase circuit breaker needs to be tripped. The protection principle described in this embodiment has a built-in fault phase selection function without additional auxiliary criteria. In this embodiment, the Karenbauer transformation formula is used to convert the measured data from the time domain to the mode domain for analysis. Through the phase-mode transformation, the three-phase voltage and current components can be converted into mode domain components, specifically including zero-mode component, one-mode component and two-mode component information. For example, after the phase-mode transformation in step S1, the one-mode component contains the measurement information of phases A and B, and the two-mode component contains the measurement information of phases A and C; after the phase-mode transformation by the first rewritten formula, the one-mode component contains the measurement information of phases B and C, and the two-mode component contains the measurement information of phases A and B; after the phase-mode transformation by the second rewritten formula, the one-mode component contains the measurement information of phases A and C, and the two-mode component contains the measurement information of phases B and C. The first rewritten formula is specifically: The second rewritten formula is specifically: In the formula, is the current value under the zero-mode component obtained by the first rewritten formula; is the current value under the one-mode component obtained by the first rewritten formula; is the current value under the two-mode component obtained by the first rewritten formula; is the instantaneous sampled value of phase A current on the new energy side or the grid side; is the instantaneous sampled value of phase B current on the new energy side or the grid side; is the instantaneous sampled value of phase C current on the new energy side or the grid side; is the current value under the zero-mode component obtained by the second rewritten formula; is the current value under the one-mode component obtained by the second rewritten formula; is the current value under the two-mode component obtained by the second rewritten formula.

[0048] In this embodiment, taking the case of a single-phase ground fault on phase A of the protected line as an example, the measured values of phase A are decomposed into each mode through different phase-mode transformation methods, which will cause the residual changes in the corresponding modes. If decomposed into mode 1 and mode 2 through step S1, it will cause the residuals of both modes to increase; if decomposed into mode 2 through the first rewriting formula, it will cause the residual of mode 2 to increase; if decomposed into mode 1 through the second rewriting formula, it will cause the residual of mode 1 to increase; while the BC two-phase lines of the non-fault phase are in normal operation. Although the single-phase ground fault may cause the voltage of the non-fault phase to rise, the line model has not changed, so it will not cause the residual to increase. Utilizing this characteristic, this embodiment can perform fault phase selection through different phase-mode transformation methods. The multi-mode phase-mode transformation combination includes standard phase sequence transformation, cyclic phase sequence transformation, and reverse phase sequence transformation. For quantitative analysis, this embodiment defines the residual normalized sum of squares of each mode component and judges the fault type according to its ratio (including three single-phase ground faults AG, BG, CG, three phase-to-phase short circuit faults AB, BC, AC, and three phase-to-phase short circuit ground faults ABG, BCG, ACG), and determines the specific fault phase selection method. Among them, the residual normalized sum of squares value of the mode 1 current decomposed through step 1 in this embodiment is , and the residual normalized sum of squares value of the mode 2 current is ; the residual normalized sum of squares value of the mode 1 current decomposed by the first rewriting formula is , and the residual normalized sum of squares value of the mode 2 current is ; the residual normalized sum of squares value of the mode 1 current decomposed by the second rewriting formula is , and the residual normalized sum of squares value of the mode 2 current is . Table 1 details the ratio characteristics of the residuals of each mode under different fault types. By comparing the residual ratios, the fault type can be accurately judged. For example, when the residual ratio of a certain mode is 0 or close to 0, it means that the phase corresponding to this mode has not failed; while when the residual ratio is close to 1, it means that the phase corresponding to this mode has failed; in addition, for two-phase short circuit and two-phase short circuit ground faults, they can be distinguished by further analyzing the cells with a residual ratio of 1. If all residual ratios do not meet the table content, it can be determined as a three-phase fault. This embodiment sets the value range of the "1" cell as [0.9, 1.1], and the value range of the "0" cell as (0, 0.01]. The specific fault phase selection method is shown in Table 1: Table 1 To verify the feasibility of the AC transmission line state estimation protection fault phase selection method proposed in this embodiment, this embodiment is built in PSCAD / EMTDC as Figure 4The shown simulation model of the new energy flexible DC transmission system selects a flexible DC transmission scheme with a true bipolar structure, and the rated voltage of the DC line is set to 800 kV. Among them, the MMC1 controller adopts a control strategy of constant AC voltage and AC frequency, and the MMC2 controller adopts a control strategy of constant DC voltage and constant reactive power; the rated voltage of the AC line is 220 kV, and the new energy power station selects a full-power inverter power supply. The full-power inverter power supply selects a control mode of constant active power and constant reactive power. In this model, this embodiment determines that the AC transmission line is the protected object, and its line length is 100 km. For the convenience of analysis, this embodiment marks the new energy power station on the line as the r side and the grid side of the line as the g side, and sets the sampling frequency to 5 kHz. In order to comprehensively evaluate the performance of the proposed protection scheme under different fault types and influencing factors, this embodiment conducts simulation verification for various fault scenarios. Figure 4 The fault positions are marked, specifically including: f1 is a proximal fault outside the r side area; f2 is a fault at the beginning of the r side area; f3 is a fault at the midpoint of the area; f4 is a fault at the beginning of the g side area; f5 is a proximal fault outside the g side area.

[0049] In order to deeply explore the performance of the proposed fault phase selection method under different in-zone metallic fault scenarios, this embodiment sets various fault types at the f2, f3, and f4 positions of the AC line and records the simulation results. Table 2 lists the simulation results under different fault positions and fault types, and Table 2 is as follows: Table 2 The simulation results in Table 2 show that the proposed protection principle can quickly and accurately identify various in-zone faults within a very short time (within 2 ms). Specifically, whether it is a single-phase fault (such as an A-phase, B-phase, or C-phase fault), a two-phase fault (such as a BC-phase or CA-phase fault), or a three-phase fault scenario, the fault phase selection scheme can accurately judge the fault phase. For example, when a metallic A-phase fault occurs at the f2 position, the protection devices on both the r side and the g side can accurately identify the A-phase fault within about 1.5 ms to 1.9 ms. Similarly, for BC-phase and CA-phase faults, as well as more severe three-phase faults, the protection devices also show good fault phase selection ability and fast action response. These simulation results fully verify the effectiveness and reliability of the proposed fault phase selection method.

[0050] An embodiment of the present invention provides a method for fault phase selection in the state estimation of an AC transmission line. The method maps the mutually coupled three-phase voltage and current components in the three-phase AC transmission line from the phase domain to the modal domain space through a phase-mode transformation matrix, obtaining the modal domain voltage and current components on the new energy side and the grid side; based on the Bergeyron transmission line model, the uniformly lossy transmission line is equivalent to two lossless transmission lines connected in series with four lumped resistors, and using the modal domain voltage and current components as inputs, a system measurement equation is constructed through state variables; a weighted least squares estimation model is used to solve the system measurement equation to obtain the optimal estimated value of the state variables; according to the optimal estimated value, the normalized sum of squares of the residuals between the measured estimated value and the actual measured value in the modal domain is calculated, and the in-zone fault line is screened out by comparing the normalized sum of squares of the residuals with the chi-square distribution threshold; the same fault current data of the in-zone fault line is decoupled twice using a multi-mode phase-mode transformation combination to generate the normalized sum of squares of the residuals of each modal domain component under the multi-mode phase-mode transformation combination; according to the ratio of the normalized sum of squares of the residuals of each modal domain component under the multi-mode phase-mode transformation combination, the fault phase is determined, and a corresponding tripping instruction is generated according to the fault phase to control the circuit breaker to operate to complete fault isolation. Compared with the prior art, this method uses a state estimation method combining phase-mode transformation and the Bergeyron model to achieve fast and accurate fault phase selection for AC transmission lines, automatically generates tripping instructions to isolate the fault area, effectively improves the fast detection and precise positioning of in-zone faults in AC transmission lines, and ensures the stable operation of the power grid.

[0051] It should be noted that the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0052] In one embodiment, as Figure 5 shown, an embodiment of the present invention provides a system for fault phase selection in the state estimation of an AC transmission line, and the system includes: A phase-mode mapping module 101, configured to map the mutually coupled three-phase voltage and current components in the three-phase AC transmission line from the phase domain to the modal domain space through a phase-mode transformation matrix, obtaining the modal domain voltage and current components on the new energy side and the grid side; A measurement construction module 102, configured to, based on the Bergeyron transmission line model, equivalent the uniformly lossy transmission line to two lossless transmission lines connected in series with four lumped resistors, and using the modal domain voltage and current components as inputs, construct a system measurement equation through state variables; An optimal estimation module 103, configured to use a weighted least squares estimation model to solve the system measurement equation to obtain the optimal estimated value of the state variables; The fault analysis module 104 is used to calculate the residual normalized sum of squares of the measured estimated value and the actual measured value in the modal domain according to the optimal estimated value, and screen out the in-zone fault lines by comparing the residual normalized sum of squares with the chi-square distribution threshold; The secondary decoupling module 105 is used to perform secondary decoupling on the same fault current data of the in-zone fault line by using the multi-mode phase-mode transformation combination, and generate the residual normalized sum of squares of each modal domain component under the multi-mode phase-mode transformation combination; The fault phase selection module 106 is used to determine the fault phase according to the ratio of the residual normalized sum of squares of each modal domain component under the multi-mode phase-mode transformation combination, and generate a corresponding tripping instruction according to the fault phase to control the circuit breaker to act to complete fault isolation.

[0053] In some embodiments, the phase-mode mapping module is specifically used for: Real-time collect the instantaneous sampling values of the three-phase voltages and the instantaneous sampling values of the three-phase currents on both sides of the AC transmission line to obtain the three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the grid side of the AC transmission line; Map the three-phase voltage and current components from the phase domain to the modal domain space to obtain the modal domain voltage and current components on the new energy side and the grid side; the modal domain voltage and current components include zero-mode components, one-mode components, and two-mode components.

[0054] In some embodiments, the measurement construction module is specifically used for: Obtain the AC transmission line parameters; the AC transmission line parameters include the resistance per unit length, the inductance per unit length, the capacitance per unit length, and the total line length; Based on the AC transmission line parameters, use the Bergeon transmission line model to equivalent the uniform lossy AC transmission line into the form of two lossless AC transmission lines connected in series with four lumped resistors to obtain the lossless transmission line model after equivalent conversion; According to the lossless transmission line model after equivalent conversion, calculate the key parameters of the lossless transmission line; the key parameters of the lossless transmission line include characteristic impedance, propagation time, and attenuation coefficient; According to the key parameters of the lossless transmission line and the modal domain voltage and current components, establish the dynamic voltage-current relationship equation between the new energy side and the grid side of the AC transmission line; Define the modal domain voltage estimated value and the modal domain current estimated value on both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

[0055] In some embodiments, the optimal estimation module is specifically used for: Construct a residual vector based on the difference between the true measurement values in the system measurement equation and the virtual measurement values estimated based on the current state variables; the true measurement values are the modal voltage and current components on the new energy side and the grid side. Construct an objective function for minimizing the weighted sum of squares of residuals with the optimization objective of minimizing the weighted sum of squares of the residual vector. Use the Jacobian matrix to relate the state variables to the actual measurement values, and use the weighted least squares method to solve the objective function for minimizing the weighted sum of squares of residuals to obtain the optimal estimated values of the state variables; the actual measurement values include the true measurement values and the virtual measurement values.

[0056] In some embodiments, the fault analysis module is specifically configured to: Input the optimal estimated values into the system measurement equation, calculate the measurement estimated values in the modal domain, and calculate the residual estimation between the measurement estimated values and the actual measurement values. Perform normalization processing on the residual estimation to obtain the normalized residuals, and calculate the sum of squares of the normalized residuals based on the total number of actual measurement values to obtain the normalized sum of squares of residuals. Determine the degrees of freedom based on the difference between the total number of actual measurement values and the number of state variables in the system measurement equation, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table. Compare the calculated normalized sum of squares of residuals with the chi-square distribution threshold. If the normalized sum of squares of residuals is greater than the chi-square distribution threshold, it is determined that a line-to-line fault has occurred within the zone, and the faulty line within the zone is screened out; otherwise, the line is operating normally.

[0057] In some embodiments, the faults within the zone include single-phase-to-ground faults within the zone, two-phase short-circuit faults within the zone, and three-phase-to-ground faults within the zone.

[0058] In some embodiments, the multi-mode phase-mode transformation combination includes standard phase sequence transformation, cyclic phase sequence transformation, and reverse phase sequence transformation.

[0059] For the specific limitations of an AC transmission line state estimation fault phase selection system, reference can be made to the above limitations for an AC transmission line state estimation fault phase selection method, which will not be elaborated here. Those of ordinary skill in the art can realize that, combined with the various modules and steps described in the embodiments disclosed in the present application, they can be implemented in hardware, software, or a combination of both. Whether these functions are executed in hardware or software depends on the specific application and design constraints 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 application.

[0060] An embodiment of the present invention provides an AC transmission line state estimation fault phase selection system, wherein the system maps the mutually coupled three-phase voltage and current components in the three-phase AC transmission line from the phase domain to the module domain space through a phase module mapping module, and obtains the module domain voltage and current components on the new energy side and the power grid side; the measurement construction module converts the uniform lossy transmission line into two sections of lossless transmission line in series with four concentrated resistors based on the Berelon transmission line model, and uses the module domain voltage and current components as inputs to construct a system measurement equation through state variables; the optimal estimation module uses a weighted least squares estimation model to solve the system measurement equation to obtain the optimal estimate of the state variables; therefore The fault analysis module calculates the residual normalized sum of squares of the measured estimated value and the actual measured value in the mode domain according to the optimal estimated value, and screens out the fault line in the area by comparing the residual normalized sum of squares with the chi-square distribution threshold; the secondary decoupling module uses the multi-mode phase mode transformation combination to perform secondary decoupling on the same fault current data of the fault line in the area, and generates the residual normalized sum of squares of each mode domain component under the multi-mode phase mode transformation combination; the fault phase selection module determines the fault phase according to the ratio of the residual normalized sum of squares of each mode domain component under the multi-mode phase mode transformation combination, and generates the corresponding tripping command according to the fault phase, and controls the circuit breaker action to complete the fault isolation. Compared with the existing technology, this system realizes the rapid and accurate phase selection of AC transmission line faults through the state estimation scheme combining phase mode transformation with the Berelon model, and automatically generates tripping commands to isolate the fault area, effectively improving the rapid detection and precise positioning of faults in the AC transmission line area, and ensuring the stable operation of the power grid.

[0061] Figure 6 A computer device provided by an embodiment of the present invention includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor, and the processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0062] The memory may include a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example but not limitation, the programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0063] Additionally, the memory may be a physically separate unit or may be integrated with the processor.

[0064] It can be understood by those skilled in the art that Figure 6The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have the same component layout.

[0065] In one embodiment, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0066] The embodiment of the present invention provides a method, system, device and medium for fault phase selection in AC transmission line state estimation. A method for fault phase selection in AC transmission line state estimation realizes rapid detection and accurate positioning of in-zone faults in AC transmission lines through techniques such as phase-mode transformation, can accurately identify the fault phase and generate a tripping command, effectively isolate the fault area, and ensure the stable operation of the power grid.

[0067] In the above embodiment, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, an SSD), etc.

[0068] Those skilled in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods.

[0069] The above-described embodiments merely represent several preferred embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A method for fault phase selection in the state estimation of an AC transmission line, characterized in that It includes the following steps: Using the phase-mode transformation matrix, map the mutually coupled three-phase voltage and current components in the three-phase AC transmission line from the phase domain to the modal domain space to obtain the modal domain voltage and current components on the new energy side and the grid side; Based on the Berreman transmission line model, equivalent the uniform lossy transmission line to two lossless transmission lines in series with four lumped resistors, and use the modal domain voltage and current components as inputs to construct a system measurement equation through state variables; Adopt a weighted least squares estimation model to solve the system measurement equation to obtain the optimal estimated values of the state variables; According to the optimal estimated values, calculate the residual normalized sum of squares between the measurement estimated values and the actual measurement values in the modal domain, and screen out the in-zone fault lines by comparing the residual normalized sum of squares with the chi-square distribution threshold; Use a multi-mode phase-mode transformation combination to decouple the same fault current data of the in-zone fault line for the second time to generate the residual normalized sum of squares of each modal domain component under the multi-mode phase-mode transformation combination; According to the ratio of the residual normalized sum of squares of each modal domain component under the multi-mode phase-mode transformation combination, determine the fault phase, and generate a corresponding tripping command according to the fault phase to control the circuit breaker to operate to complete fault isolation.

2. The method for fault phase selection in state estimation of an AC transmission line according to claim 1, characterized in that, The step of using the phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in the three-phase AC transmission line from the phase domain to the modal domain space to obtain the modal domain voltage and current components on the new energy side and the grid side includes: Real-time collect the instantaneous sampling values of the three-phase voltage and the instantaneous sampling values of the three-phase current on both sides of the AC transmission line to obtain the three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the grid side of the AC transmission line; Map the three-phase voltage and current components from the phase domain to the modal domain space to obtain the modal domain voltage and current components on the new energy side and the grid side; the modal domain voltage and current components include zero-mode components, one-mode components, and two-mode components.

3. The phase selection method for fault in the state estimation of an AC transmission line according to claim 2, characterized in that, The step of based on the Berreman transmission line model, equivalent the uniform lossy transmission line to two lossless transmission lines in series with four lumped resistors, and use the modal domain voltage and current components as inputs to construct a system measurement equation through state variables includes: Obtain the AC transmission line parameters; the AC transmission line parameters include the resistance per unit length, the inductance per unit length, the capacitance per unit length, and the total line length; Based on the AC transmission line parameters, use the Berreman transmission line model to equivalent the uniform lossy AC transmission line to the form of two lossless AC transmission lines in series with four lumped resistors to obtain the equivalent converted lossless transmission line model; According to the equivalent converted lossless transmission line model, calculate the key parameters of the lossless transmission line; the key parameters of the lossless transmission line include characteristic impedance, propagation time, and attenuation coefficient; According to the key parameters of the lossless transmission line and the modal domain voltage and current components, establish a dynamic voltage-current relationship equation between the new energy side and the grid side of the AC transmission line; Define the modal domain voltage estimated values and modal domain current estimated values on both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

4. A method for fault phase selection in the state estimation of an AC transmission line according to claim 1, characterized in that, The steps of using the weighted least squares estimation model to solve the system measurement equation and obtain the optimal estimated value of the state variable include: Construct a residual vector based on the difference between the true measurement value in the system measurement equation and the virtual measurement value estimated based on the current state variable; the true measurement value is the modulus domain voltage and current components on the new energy side and the grid side. Construct an objective function for minimizing the weighted sum of squares of residuals with the goal of minimizing the weighted sum of squares of the residual vector. Use the Jacobian matrix to relate the state variable to the actual measurement value, and use the weighted least squares method to solve the objective function for minimizing the weighted sum of squares of residuals to obtain the optimal estimated value of the state variable; the actual measurement value includes the true measurement value and the virtual measurement value.

5. The phase selection method for fault in the state estimation of an AC transmission line according to claim 1, characterized in that, The steps of calculating the normalized sum of squares of residuals between the measurement estimated value and the actual measurement value in the modulus domain according to the optimal estimated value, and screening out the in-zone fault line by comparing the normalized sum of squares of residuals with the chi-square distribution threshold include: Input the optimal estimated value into the system measurement equation, calculate the measurement estimated value in the modulus domain, and calculate the residual estimate between the measurement estimated value and the actual measurement value. Perform normalization processing on the residual estimate to obtain the normalized residual, and calculate the sum of squares of the normalized residual according to the total number of actual measurement values to obtain the normalized sum of squares of residuals. Determine the degrees of freedom based on the difference between the total number of actual measurement values and the number of state variables in the system measurement equation, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table. Compare the calculated normalized sum of squares of residuals with the chi-square distribution threshold. If the normalized sum of squares of residuals is greater than the chi-square distribution threshold, it is determined that the line has an in-zone fault, and the in-zone fault line is screened out; otherwise, the line is operating normally.

6. The fault phase selection method for AC transmission line state estimation according to claim 5, characterized in that: The in-zone faults include in-zone single-phase grounding faults, in-zone two-phase short-circuit faults, and in-zone three-phase grounding faults.

7. The fault phase selection method for the state estimation of an AC transmission line according to claim 1, characterized in that: The multi-mode phase-mode transformation combination includes standard phase sequence transformation, cyclic phase sequence transformation, and inverse phase sequence transformation.

8. A fault phase selection system for state estimation of AC transmission lines, characterized in that, The system includes: A phase-mode mapping module for using a phase-mode transformation matrix to map the mutually coupled three-phase voltage and current components in a three-phase AC transmission line from the phase domain to the modulus domain space to obtain the modulus domain voltage and current components on the new energy side and the grid side. A measurement construction module for equivalenting a uniform lossy transmission line to two lossless transmission lines in series with four lumped resistors based on the Bergeon transmission line model, and constructing a system measurement equation with the modulus domain voltage and current components as inputs through state variables. An optimal estimation module for using a weighted least squares estimation model to solve the system measurement equation and obtain the optimal estimated value of the state variable. A fault analysis module for calculating the normalized sum of squares of residuals between the measurement estimated value and the actual measurement value in the modulus domain according to the optimal estimated value, and screening out the in-zone fault line by comparing the normalized sum of squares of residuals with the chi-square distribution threshold. A secondary decoupling module for using the multi-mode phase-mode transformation combination to perform secondary decoupling on the same fault current data of the in-zone fault line to generate the normalized sum of squares of residuals of each modulus domain component under the multi-mode phase-mode transformation combination. The fault phase selection module is used to determine the fault phase according to the ratio of the sum of the normalized squares of the residuals of each mode domain component under the multi-mode phase-mode transformation combination, and generate a corresponding tripping command according to the fault phase to control the circuit breaker to act to complete fault isolation.

9. The fault phase selection system for the state estimation of an AC transmission line according to claim 8, characterized in that, The phase-mode mapping module is specifically used for: Real-time collect the instantaneous sampling values of the three-phase voltages and the instantaneous sampling values of the three-phase currents on both sides of the AC transmission line to obtain the three-phase voltage and current components; both sides of the AC transmission line include the new energy side of the AC transmission line and the power grid side of the AC transmission line; Map the three-phase voltage and current components from the phase domain to the mode domain space to obtain the mode domain voltage and current components on the new energy side and the power grid side; The mode domain voltage and current components include zero-mode components, one-mode components, and two-mode components.

10. A fault phase selection system for AC transmission line state estimation according to claim 9, characterized in that, The measurement construction module is specifically used for: Obtain the AC transmission line parameters; the AC transmission line parameters include the resistance per unit length, the inductance per unit length, the capacitance per unit length, and the total line length; Based on the AC transmission line parameters, use the Berreman transmission line model to equivalent the uniform lossy AC transmission line into a form of two lossless AC transmission lines connected in series with four concentrated resistors to obtain the equivalent converted lossless transmission line model; According to the equivalent converted lossless transmission line model, calculate the key parameters of the lossless transmission line; the key parameters of the lossless transmission line include characteristic impedance, propagation time, and attenuation coefficient; According to the key parameters of the lossless transmission line and the mode domain voltage and current components, establish a dynamic voltage-current relationship equation between the new energy side and the power grid side of the AC transmission line; Define the mode domain voltage estimate value and the mode domain current estimate value on both sides of the AC transmission line at the current moment as state variables, and combine the dynamic voltage-current relationship equation with the state variables to construct a system measurement equation.

11. A fault phase selection system for AC transmission line state estimation according to claim 8, characterized in that, The optimal estimation module is specifically used for: According to the difference between the true measurement value in the system measurement equation and the virtual measurement value estimated based on the current state variable, construct a residual vector; the true measurement value is the mode domain voltage and current components on the new energy side and the power grid side; Taking the minimization of the weighted sum of squares of the residual vector as the optimization goal, construct a residual weighted sum of squares minimization objective function; Use the Jacobian matrix to associate the state variable with the actual measurement value, and use the weighted least squares method to solve the residual weighted sum of squares minimization objective function to obtain the optimal estimated value of the state variable; the actual measurement value includes the true measurement value and the virtual measurement value.

12. A fault phase selection system for AC transmission line state estimation according to claim 8, characterized in that, The fault analysis module is specifically used for: Input the optimal estimated value into the system measurement equation, calculate the measurement estimated value in the mode domain, and calculate the residual estimate between the measurement estimated value and the actual measurement value; Perform normalization processing on the residual estimate to obtain a normalized residual, and calculate the sum of squares of the normalized residual according to the total number of actual measurement values to obtain a residual normalized sum of squares; According to the difference between the total number of actual measurement values and the number of state variables in the system measurement equation, determine the degrees of freedom, and use the degrees of freedom to query the chi-square distribution threshold from the chi-square distribution table; Compare the calculated normalized sum of squared residuals with the chi-square distribution threshold. If the normalized sum of squared residuals is greater than the chi-square distribution threshold, it is determined that a line has a fault within the zone, and the faulty line within the zone is screened out; otherwise, the line is operating normally.

13. A fault phase selection system for AC transmission line state estimation according to claim 12, characterized in that: The faults within the zone include single-phase grounding faults within the zone, two-phase short-circuit faults within the zone, and three-phase grounding faults within the zone.

14. A fault phase selection system for AC transmission line state estimation according to claim 8, characterized in that: The multi-mode phase-mode transformation combination includes standard phase sequence transformation, cyclic phase sequence transformation, and reverse phase sequence transformation.

15. A computer device, characterized in that: It includes a processor and a memory. The processor is connected to the memory. The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device executes the method described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: A computer program is stored in the computer-readable storage medium. When the computer program is run, the method described in any one of claims 1 to 7 is implemented.

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