A method for estimating a feeder harmonic trajectory and a terminal

Abstract

The application discloses a kind of feeder harmonic trajectory estimation method and terminal, obtain the fundamental impedance data of feeder and estimate harmonic impedance data based on harmonic number;Equivalent PCC point in feeder is calculated using harmonic impedance data to obtain the approximate value of state variable;With state variable approximate value as initial value, and with each harmonic imaginary part estimation error of equivalent PCC point, equivalent PCC point total harmonic estimation error and the total effective value error of node to be solved as constraint, a multi-objective function is established, and the harmonic current content rate and phase angle of distribution transformer approximating true value are obtained by solving the multi-objective function.The application realizes the observation of harmonic state using approximate estimation method without modeling analysis or high-precision measurement of harmonic state, improves the detection capability of distribution network harmonic, and avoids the estimation result from deviating from actual value through multiple constraints, improves the observation accuracy of harmonic state.

Description

Technical Field

[0001] This invention relates to the field of harmonic tracking, and in particular to a method and terminal for estimating the harmonic trajectory of a feeder. Background Technology

[0002] Nowadays, power electronic devices account for an increasingly large proportion of residential loads. These household appliances containing power electronic devices also generate a certain degree of harmonics during operation. Furthermore, due to the large-scale integration of distributed renewable energy generation by residents, harmonic pollution has also begun to exhibit distributed characteristics.

[0003] Currently, the main methods used for harmonic estimation include least squares method and improved least squares method, Kalman filter model, etc.

[0004] The least squares method involves establishing system observation equations that account for measurement errors, and then solving for the harmonic state of the entire network based on known network parameters and partial measurement data. Improved least squares methods include robust least squares, which can yield more accurate estimates when measurement data contains errors.

[0005] The Kalman filter model is an effective method applied in the time domain. It is a state estimation algorithm that combines prior experience with measurement updates. The basic idea of ​​Kalman filtering is to comprehensively utilize the previous state and measurement values ​​to predict and estimate the state of a physical quantity.

[0006] These methods are primarily based on physical models for modeling and analysis, describing the relationships between nodes and estimating the state of unknown nodes using the harmonic states of monitoring points. However, the insufficient number of monitoring devices in the power grid leads to the harmonic states not being observable across the entire network, making it difficult to establish measurement equations. Furthermore, only relatively accurate harmonic current and voltage phase data will yield good results. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a method and terminal for estimating the harmonic trajectory of a feeder, thereby solving the problem of the difficulty in observing the harmonic state.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A method for estimating feeder harmonic trajectories includes the following steps:

[0010] S1. Obtain the fundamental impedance data of the feeder and estimate the harmonic impedance data based on the harmonic order;

[0011] S2. Calculate the approximate values ​​of the state variables of the equivalent PCC point in the feeder using the harmonic impedance data.

[0012] S3. Using the approximate values ​​of the state variables as initial values, and establishing a multi-objective function with constraints such as the estimation error of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be determined, the multi-objective function is solved to obtain the transformer harmonic current content and phase angle that approximate the true values.

[0013] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0014] A feeder harmonic trajectory estimation terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: including the following steps:

[0015] S1. Obtain the fundamental impedance data of the feeder and estimate the harmonic impedance data based on the harmonic order;

[0016] S2. Calculate the approximate values ​​of the state variables of the equivalent PCC point in the feeder using the harmonic impedance data.

[0017] S3. Using the approximate values ​​of the state variables as initial values, and establishing a multi-objective function with constraints such as the estimation error of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be determined, the multi-objective function is solved to obtain the transformer harmonic current content and phase angle that approximate the true values.

[0018] The beneficial effects of this invention are as follows: Due to the nonlinearity and instability of feeder harmonics, this invention provides a method and terminal for estimating feeder harmonic trajectories. By using the fundamental impedance to approximate the harmonic impedance, the approximate values ​​of the state variables at the equivalent PCC point in the feeder are calculated as the initial values ​​of the multi-objective equation. The multi-objective equation is constrained by the estimation errors of the imaginary and real parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be solved. During the solution process, it gradually approximates the true values ​​of the transformer harmonic current content and phase angle. That is, without modeling analysis or high-precision measurement of the harmonic state, the harmonic state can be observed using an approximate estimation method, which improves the detection capability of the distribution network harmonics. At the same time, through multiple constraints, the estimation results are avoided from deviating from the actual values, thus improving the observation accuracy of the harmonic state. Attached Figure Description

[0019] Figure 1 This is a flowchart of a feeder harmonic trajectory estimation method in an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the distribution network feeder in an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of a feeder harmonic trajectory estimation terminal in an embodiment of the present invention;

[0022] Label Explanation:

[0023] 1. A feeder harmonic trajectory estimation terminal; 2. Memory; 3. Processor. Detailed Implementation

[0024] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0025] Please refer to Figure 1 and Figure 2 A method for estimating harmonic trajectories of a feeder, comprising the following steps:

[0026] S1. Obtain the fundamental impedance data of the feeder and estimate the harmonic impedance data based on the harmonic order;

[0027] S2. Calculate the approximate values ​​of the state variables of the equivalent PCC point in the feeder using the harmonic impedance data.

[0028] S3. Using the approximate values ​​of the state variables as initial values, and establishing a multi-objective function with constraints such as the estimation error of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be determined, the multi-objective function is solved to obtain the transformer harmonic current content and phase angle that approximate the true values.

[0029] Understandably, in distribution networks, due to the difficulty in accurately obtaining harmonic impedance, this method uses fundamental impedance to approximate harmonic impedance. The fundamental impedance data of feeders can be obtained through the power grid's SCADA system. This invention provides a method and terminal for estimating feeder harmonic trajectories. By approximating harmonic impedance using fundamental impedance, the approximate state variables of the equivalent PCC point in the feeder are calculated as initial values ​​for the multi-objective equation. The multi-objective equation is constrained by the estimation errors of the imaginary and real parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be solved. During the solution process, it gradually approximates the true values ​​of the transformer harmonic current content and phase angle. That is, without modeling analysis or high-precision measurement of the harmonic state, the method of approximate estimation is used to achieve the observation of the harmonic state, improving the detection capability of harmonics in the distribution network. Simultaneously, through multiple constraints, the estimation results are prevented from deviating from the actual values, thus improving the observation accuracy of the harmonic state.

[0030] In the harmonic trajectory estimation method described in this invention, the equivalent PCC point is a simplified designation used for harmonic analysis. Specifically, the equivalent PCC point is considered as the common connection point of the entire feeder outlet, used to simplify the harmonic propagation path of complex nodes in the distribution network. By obtaining the state variables (such as the imaginary and real parts of the harmonic current) of the equivalent PCC point, the harmonic state of different nodes in the network can be inferred. The introduction of the equivalent PCC point simplifies the analysis of the harmonic condition of the entire distribution network and avoids the complexity of measuring each node individually.

[0031] In an embodiment of the present invention, step S1 specifically includes the following steps:

[0032] To obtain the fundamental impedance data of the feeder, specifically, obtain the system topology, harmonic measurement data in the distribution network, and the fundamental impedance of the line, using the following formula:

[0033] ;

[0034] In the formula, z L The total impedance of the line; r L This is the fundamental impedance of the line; x L denoted as the fundamental reactance of the line; j is the complex unit.

[0035] The harmonic impedance data is estimated based on the harmonic order, using the following formula:

[0036] ;

[0037] In the formula, h This represents the harmonic order.

[0038] As can be seen from the above description, the harmonic impedance can be estimated by the relationship between the fundamental impedance and the harmonic order, which simplifies the calculation method of harmonic impedance, reduces the dependence on harmonic measurement equipment, and lowers the implementation cost of the system.

[0039] In an embodiment of the present invention, step S2 specifically includes the following steps:

[0040] A two-port network is established using the harmonic impedance data to obtain a parameter set. An impedance parameter matrix is ​​then constructed based on this parameter set, representing the measurement vector and state vector. Based on this impedance parameter matrix, approximate values ​​of the state variables at the equivalent PCC point in the feeder are obtained, as shown in the following formula:

[0041] ;

[0042] In the formula, U x , I x These are the harmonic voltage vector and the injected harmonic current vector of the measurement node, respectively;U z , I z These are the harmonic voltage vector and the injected harmonic current vector of the state node, respectively;

[0043] K is the measurement vector Z With state vector X The relationship between them is characterized by m*n The impedance parameter matrix of dimension 1 is represented as follows:

[0044] ;

[0045] In the formula, k 11 … k mn The parameter set is obtained by using the harmonic impedance data to establish a two-port network.

[0046] As can be seen from the above description, by establishing a two-port network and using the impedance parameter matrix, the relationship between the measurement vector and the state vector can be effectively described, further improving the accuracy of the approximation of the harmonic state quantities. By representing the relationship between measurement and state through the impedance matrix, the algorithm is applicable to complex power distribution network structures, enhancing the system's versatility and applicability.

[0047] In an embodiment of the present invention, step S3 specifically includes the following steps:

[0048] S31. Using the approximate value of the state variable as the initial value, calculate the phasor of the branch harmonic current measured at the equivalent PCC point, as shown in the following formula:

[0049] ;

[0050] In the formula, The first point measured for the equivalent PCC point h Secondary branch harmonic current phasor; This is the number to which the feeder sub-diagram belongs. m The first distribution transformer h The phasor of the injected harmonic current, M represents the number of transformers in the feeder diagram;

[0051] S32. Based on the phase angle of the injected harmonic current, the phasor of the branch harmonic current measured at the equivalent PCC point is converted into real and imaginary parts, as shown in the following formula:

[0052] ;

[0053] In the formula, and The number of points measured at the equivalent PCC points are respectively The real and imaginary parts of the harmonic current in the secondary branch; The feeder topology diagram belongs to the [number]th [section]. m The first distribution transformer The phase angle of the injected harmonic current has a period of 2π and a range of -π to π.

[0054] To study the harmonic spectrum emitted by each distribution transformer to the power grid and to facilitate the analysis of harmonic injection into the power grid, the harmonic current content rate is introduced, as shown in the following formula:

[0055] ;

[0056] In the formula, The feeder sub-diagram belongs to the 1st The effective value of the fundamental current of each transformer; The feeder sub-diagram belongs to the 1st The first distribution transformer Subharmonic current content;

[0057] However, considering that the base current monitoring of the distribution transformer is usually the total effective value of the current, it is also necessary to convert the fundamental current effective value to the total effective value of the current. That is, to calculate the fundamental current effective value in the total effective value of the current and obtain the relationship between the branch harmonic current phasor measured at the equivalent PCC point and the total effective value of the current, as shown in the following formula:

[0058] ;

[0059] In the formula, This represents the total effective value of the current. Let be the effective value of the fundamental current; substituting this value, we obtain the following equation:

[0060] ;

[0061] In the formula, The feeder sub-diagram belongs to the 1st The total effective value of the current in each transformer; The feeder sub-diagram belongs to the 1st The first distribution transformer Harmonic current content of the first order;

[0062] S33. Establish a multi-objective function with the estimation error of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be determined as constraints. Solve the multi-objective function to obtain the transformer harmonic current content and phase angle that approximate the true value.

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] In the formula, F The objective function is... For the equivalent PCC point RMS value of subharmonics; This represents the total effective value of harmonics at the equivalent PCC point; z Represents the variable to be determined. Z Represents the set of variables to be determined. η for f A weight of 4; , The result is a random number, ranging from 0.5 to 1. and The approximate value of the state variable calculated in step S2; Let z be the effective value of the primary current of the variable to be determined; The feeder sub-diagram belongs to the 1st The first distribution transformer The content of harmonic currents.

[0069] As can be seen from the above description, the estimation errors of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonic at the equivalent PCC point, and the error of the total effective value of the node to be determined are used as multi-objective functions to constrain the harmonic amplitude and phase angle of the particle within a certain range of approximate values, thereby reducing the gap between the calculated results and the actual values.

[0070] In an embodiment of the present invention, step S3 further includes:

[0071] The inertia weight, cognitive coefficient, and social learning coefficient of the particle swarm optimization algorithm are dynamically adjusted, and the objective function is optimized using the adjusted particle swarm optimization algorithm to minimize the difference between the calculated harmonic current and phase angle and the corresponding actual values.

[0072] Understandably, the Particle Swarm Algorithm (PSO) is a swarm intelligence-based optimization algorithm. Its essence is random search based on swarm collaboration, possessing advantages such as global heuristics, simple structure, maturity, efficiency, and ease of implementation. It is widely used to solve constrained optimization, multi-objective optimization, and dynamic optimization problems. However, because traditional PSO algorithms are prone to getting trapped in local optima, this invention improves upon it by dynamically adjusting inertia weights, cognitive coefficients, and social learning coefficients. This gradually reduces the learning of local optima by particles and increases their learning of global optima, allowing particles to escape the interference of local extremes and improving the optimality of the solution. The key formulas of the PSO algorithm are as follows:

[0073] ;

[0074] ;

[0075] In the formula: 、 These represent the particles at the k-th iteration. The corresponding speed and position; P ibest , g best They represent particles respectively The historical best position and the historical best position of all particles; and A random number between 0 and 1; n gen Indicates the total number of iterations; Inertial weight; 、 These are the cognitive coefficient and the social learning coefficient, respectively.

[0076] As described above, the approximate values ​​of the state variables are used as the initial values ​​for the particle swarm optimization algorithm. Then, the inertia weight, cognitive coefficient, and social learning coefficient are dynamically adjusted based on the number of iterations. A linearly decreasing inertia weight is used to improve the inertia weight. The value of is related to the number of iterations. In the early stages of the search, Larger size enhances global search capabilities, especially in the later stages of the search. The rapid reduction enhances local search capabilities, increasing the likelihood of locking onto the optimal solution. The individual cognitive coefficient and social learning coefficient exhibit decreasing and increasing characteristics, respectively, causing particles to gradually learn less from local optima and more from the global optimum. This helps particles minimize interference from local extrema, thereby improving solution accuracy.

[0077] Please refer to Figure 3 A feeder harmonic trajectory estimation terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it completes the steps in the feeder harmonic trajectory estimation method.

[0078] This invention provides a method and terminal for estimating harmonic trajectories of a feeder, mainly applied to estimating the state of nonlinearly unstable harmonics. The following is a detailed description with reference to specific embodiments:

[0079] Embodiment 1 of the present invention is as follows:

[0080] A method for estimating feeder harmonic trajectories includes the following steps:

[0081] S1. Obtain the fundamental impedance data of the feeder and estimate the harmonic impedance data based on the harmonic order;

[0082] S2. Calculate the approximate values ​​of the state variables at the equivalent PCC point in the feeder using harmonic impedance data.

[0083] S3. Using the approximate values ​​of the state variables as initial values, and with the estimation errors of the imaginary and real parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the total effective value error of the node to be determined as constraints, a multi-objective function is established. Solving the multi-objective function yields the transformer harmonic current content and phase angle that approximate the true values.

[0084] In this embodiment, the harmonic impedance is approximated using the fundamental impedance, and the approximate values ​​of the state variables at the equivalent PCC point in the feeder are calculated as the initial values ​​of the multi-objective equation. The multi-objective equation is constrained by the estimation errors of the imaginary and real parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the error of the total effective value of the node to be solved. During the solution process, the transformer harmonic current content and phase angle gradually approach the true values. That is, without modeling analysis or high-precision measurement of the harmonic state, the observation of the harmonic state is achieved by using the approximate estimation method, which improves the detection capability of the distribution network harmonics. At the same time, through multiple constraints, the estimation results are avoided from deviating from the actual values, thus improving the observation accuracy of the harmonic state.

[0085] Please refer to Figure 1 and Figure 2 Embodiment two of the present invention is as follows:

[0086] Based on Example 1, the method specifically includes the following steps:

[0087] S1. Obtain the system topology, harmonic measurement data in the distribution network, and the fundamental impedance of the line, using the following formula:

[0088] ;

[0089] In the formula, z L The total impedance of the line; r L This is the fundamental impedance of the line; x L denoted as the fundamental reactance of the line; j is the complex unit.

[0090] The harmonic impedance data is estimated based on the harmonic order, using the following formula:

[0091] ;

[0092] In the formula, h This represents the harmonic order.

[0093] S2. Establish a two-port network using harmonic impedance data and obtain a parameter set. Construct an impedance parameter matrix of measurement vector and state vector based on the parameter set. Obtain approximate values ​​of the state variables at the equivalent PCC point in the feeder based on the impedance parameter matrix, as shown in the following formula:

[0094] ;

[0095] In the formula, U x , I x These are the harmonic voltage vector and the injected harmonic current vector of the measurement node, respectively; U z , I z These are the harmonic voltage vector and the injected harmonic current vector of the state node, respectively;

[0096] K is the measurement vector Z With state vector X The relationship between them is characterized by m*n The impedance parameter matrix of dimension 1 is represented as follows:

[0097] ;

[0098] In the formula, k ij The parameter set is obtained by using harmonic impedance data to establish a two-port network.

[0099] S3. The multi-objective function is established as follows:

[0100] S31. Using the approximate values ​​of the state variables as initial values, calculate the phasor of the branch harmonic current measured at the equivalent PCC point, as shown in the following formula:

[0101] ;

[0102] In the formula, The first point measured for the equivalent PCC point h Secondary branch harmonic current phasor; This is the number to which the feeder sub-diagram belongs. m The first distribution transformer h The phasor of the injected harmonic current, M represents the number of transformers in the feeder diagram;

[0103] S32. Based on the phase angle of the injected harmonic current, the phasor of the branch harmonic current measured at the equivalent PCC point is converted into real and imaginary parts, as shown in the following formula:

[0104] ;

[0105] In the formula, and The number of points measured at the equivalent PCC points are respectively The real and imaginary parts of the harmonic current in the secondary branch; The feeder topology diagram belongs to the [number]th [section]. m The first distribution transformer The phase angle of the injected harmonic current has a period of 2π and a range of -π to π.

[0106] To study the harmonic spectrum emitted by each distribution transformer to the power grid and to facilitate the analysis of harmonic injection into the power grid, the harmonic current content rate is introduced, as shown in the following formula:

[0107] ;

[0108] In the formula, The feeder sub-diagram belongs to the 1st The effective value of the fundamental current of each transformer; The feeder sub-diagram belongs to the 1st The first distribution transformer Subharmonic current content;

[0109] However, considering that the base current monitoring of the distribution transformer is usually the total effective value of the current, it is also necessary to convert the fundamental current effective value to the total effective value of the current. That is, to calculate the fundamental current effective value in the total effective value of the current and obtain the relationship between the branch harmonic current phasor measured at the equivalent PCC point and the total effective value of the current, as shown in the following formula:

[0110] ;

[0111] In the formula, This represents the total effective value of the current. This represents the effective value of the fundamental current wave. For the first Harmonic current content of the first order;

[0112] After substituting, we get the following formula:

[0113] ;

[0114] In the formula, The feeder sub-diagram belongs to the 1st The total effective value of the current in each transformer; The feeder sub-diagram belongs to the 1st The first distribution transformer Harmonic current content of the first order;

[0115] S33. Establish a multi-objective function with the estimation error of the imaginary and real parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the total effective value error of the node to be determined as constraints. Solve the multi-objective function to obtain the transformer harmonic current content and phase angle that approximate the true value.

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] In the formula, The objective function is... For the equivalent PCC point RMS value of subharmonics; This represents the total effective value of harmonics at the equivalent PCC point; z Represents the variable to be determined. Z Represents the set of variables to be determined. η for f A weight of 4; , The result is a random number, ranging from 0.5 to 1. and The approximate value of the state variable calculated in step S2; Let z be the effective value of the primary current of the variable to be determined; The feeder sub-diagram belongs to the 1st The first distribution transformer The content of harmonic currents.

[0122] In this embodiment, the estimation errors of the real and virtual parts of each harmonic at the equivalent PCC point, the estimation error of the total harmonics at the equivalent PCC point, and the total effective value error of the node to be determined are used as multiple objective functions to constrain the harmonic amplitude and phase angle of the particle within a certain range of approximate values, thereby reducing the gap between the calculated results and the actual values.

[0123] Embodiment 3 of the present invention is as follows:

[0124] Based on Example 2, step S3 further includes:

[0125] The inertia weight, cognitive coefficient, and social learning coefficient of the particle swarm optimization algorithm are dynamically adjusted, and the objective function is optimized using the adjusted particle swarm optimization algorithm to minimize the difference between the calculated harmonic current and phase angle and the corresponding actual values.

[0126] The key formulas for the particle swarm optimization algorithm are as follows:

[0127] ;

[0128] ;

[0129] In the formula: 、 These represent the particles at the k-th iteration. The corresponding speed and position; P ibest , g best They represent particles respectively The historical best position and the historical best position of all particles; and A random number between 0 and 1; n gen Indicates the total number of iterations; Inertial weight; 、 These are the cognitive coefficient and the social learning coefficient, respectively.

[0130] In this embodiment, the approximate value of the state variable is used as the initial value of the particle swarm optimization algorithm, and then the inertia weight, cognitive coefficient, and social learning coefficient are dynamically adjusted according to the number of iterations. A linearly decreasing inertia weight is used to improve the inertia weight. The value of is related to the number of iterations. In the early stages of the search, Larger size enhances global search capabilities, especially in the later stages of the search. The rapid reduction enhances local search capabilities, increasing the likelihood of locking onto the optimal solution. The individual cognitive coefficient and social learning coefficient exhibit decreasing and increasing characteristics, respectively, causing particles to gradually learn less from local optima and more from the global optimum. This helps particles minimize interference from local extrema, thereby improving solution accuracy.

[0131] Please refer to Figure 2 Embodiment four of the present invention is as follows:

[0132] A feeder harmonic trajectory estimation terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it completes the steps in any one of the feeder harmonic trajectory estimation methods in Embodiments 1 to 3.

[0133] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method of feeder harmonic trajectory estimation, the method comprising: The method comprises the steps of: S1, obtaining fundamental impedance data of the feeder and estimating harmonic impedance data based on harmonic order; S2, calculating an approximate value of a state variable of an equivalent PCC point in the feeder by using the harmonic impedance data; S3, establishing a multi-objective function by taking the approximate value of the state variable as an initial value and taking the estimation error of each harmonic of the equivalent PCC point, the total harmonic estimation error of the equivalent PCC point and the total effective value error of the node to be solved as constraints, and solving the multi-objective function to obtain the harmonic current content ratio and phase angle of the distribution transformer approximating to the real value; The step S1 specifically comprises the steps of: Obtaining the fundamental impedance data of the feeder, and the formula is as follows: ； where z L is the total impedance of the line; r L is the fundamental impedance of the line; x L is the fundamental line reactance; j is the complex unit; Estimating the harmonic impedance data based on the harmonic order, and the formula is as follows: ； In the formula, h is the harmonic number; The step S2 specifically comprises the steps of: Establishing a two-port network by using the harmonic impedance data and obtaining a parameter set, and constructing an impedance parameter matrix of a measurement vector and a state vector based on the parameter set, and obtaining the approximate value of the state variable of the equivalent PCC point in the feeder based on the impedance parameter matrix, and the formula is as follows: ； wherein, U x , I x are the harmonic voltage vector and the injected harmonic current vector of the measurement node, respectively; U z , I z are the harmonic voltage vector and the injected harmonic current vector of the state node, respectively. K is the measurement vector Z The relationship between the state vector X The relationship between the state vector m*n The impedance parameter matrix of dimension N x N is represented as follows: ； wherein k 11 … k mn establishing a parameter set for a two-port network computation using the harmonic impedance data; The step S3 specifically comprises the steps of: S31, taking the approximate value of the state variable as the initial value, and calculating the equivalent PCC point measured branch harmonic current phasor, and the formula is as follows: ； In the formula, The first point measured for the equivalent PCC point h Secondary branch harmonic current phasor; This is the number to which the feeder sub-diagram belongs. m The first distribution transformer h The phasor of the injected harmonic current, M represents the number of transformers in the feeder diagram; S32, converting the equivalent PCC point measured branch harmonic current phasor into the form of real part and imaginary part according to the phase angle of the injected harmonic current, and the formula is as follows: ； In the formula, and are the real part and the imaginary part of the equivalent PCC point measured first branch harmonic current, respectively; is the phase angle of the first m th injected harmonic current of the th distribution transformer belonging to the feeder topology graph, and the phase angle is periodic with 2π and ranges from [-π, π]. Introducing the harmonic current content ratio, and the formula is as follows: ； In the formula, is the effective value of the fundamental current of the i-th distribution; is the i-th harmonic current content of the i-th distribution; is the i-th harmonic current content of the i-th distribution;​​ Calculating the current fundamental effective value in the total effective value of the current, and obtaining the relationship between the equivalent PCC point measured branch harmonic current phasor and the total effective value of the current, and the formula is as follows: ； wherein is the total effective value of the current; is the fundamental effective value of the current; ； In the formula, The feeder sub-diagram belongs to the 1st The total effective value of the current in each transformer; The feeder sub-diagram belongs to the 1st The first distribution transformer Harmonic current content of the first order; S33, establishing a multi-objective function by taking the estimation error of each harmonic of the equivalent PCC point, the total harmonic estimation error of the equivalent PCC point and the total effective value error of the node to be solved as constraints, and solving the multi-objective function to obtain the harmonic current content ratio and phase angle of the distribution transformer approximating to the real value; ； ； ； ； ； In the formula, F The objective function is... For the equivalent PCC point RMS value of subharmonics; This represents the total effective value of the equivalent PCC point harmonics; z Represents the variable to be determined. Z Represents the set of variables to be determined. The step S3 further comprises: for f A weight of 4; , The result is a random number, ranging from 0.5 to 1. and The approximate values ​​of the state variables calculated in step S2; Let z be the effective value of the primary current of the variable to be determined; The feeder sub-diagram belongs to the 1st The first distribution transformer The content of harmonic currents.

2. The method of claim 1, wherein: Dynamically adjusting the inertia weight, the cognitive coefficient and the social learning coefficient of the particle swarm algorithm, and optimizing the target function by using the adjusted particle swarm algorithm, so as to minimize the difference between the calculated harmonic current and phase angle and the corresponding actual value. A computer program product comprising a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to complete the steps of the feeder harmonic trajectory estimation method according to any one of claims 1-2.

3. A feeder harmonic trajectory estimation terminal, characterized by: ​

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