A power system external network area equivalence method and apparatus

Abstract

The application discloses a power system external network area equivalence method and device, comprising: accessing an equivalent synchronous generator model at a boundary node between a reserved area and an external network area of a power system; setting a three-phase short-circuit transient fault at the boundary node to obtain a first voltage change curve, and dividing the first voltage change curve into a sub-transient stage, a transient stage and a recovery stage; applying disturbance to each first parameter of the model, setting a three-phase short-circuit transient fault at the model to obtain a second voltage change curve, then analyzing the sensitivity of each first parameter at different stages according to the first voltage change curve and the second voltage change curve, and screening out the first parameter with a sensitivity greater than a preset threshold as a to-be-identified parameter; identifying the to-be-identified parameter at each stage by using a particle swarm algorithm, and constructing an external equivalent circuit according to all identification results. The application improves the accuracy of external equivalence by analyzing the sensitivity of each parameter at different stages.

Description

Technical Field

[0001] This invention relates to the field of power system equivalence, and more particularly to a method and apparatus for equivalence of external network areas of a power system. Background Technology

[0002] With the continuous development of new energy technologies and DC transmission technologies, the power system is gradually transforming from a traditional AC grid to a system that emphasizes both AC and DC, with rapid development of new energy sources and microgrids. This significant change in the power system has brought new problems and challenges. The development of new technologies has enabled researchers to gain new insights into system stability issues such as voltage and frequency stability, creating an urgent need to construct corresponding models for stability analysis. For actual power systems, the entire grid typically contains thousands of generators and thousands or even tens of thousands of lines. However, researchers generally only focus on a small portion of the entire power system. Due to limitations in current simulation software or the complexity of mathematical calculations, simplification of other parts of the power system is necessary. Traditional equivalent methods for coherence require a large amount of grid information and may increase the complexity of simplification calculations. Another simplification approach is to use an estimated equivalent method, which typically uses several equivalent generators to represent the external network areas outside the study area of ​​the power system.

[0003] In the equivalent estimation method, the core step is to obtain the equivalent generator parameters, ensuring that the system dynamic response at the boundary nodes remains essentially consistent before and after equivalence. However, for the same disturbance, the impact of different parameters on the disturbance varies at different time scales. Currently, in the process of identifying equivalent generator parameters, it is impossible to accurately identify equivalent generator parameters that exhibit different responses to disturbances at different time scales. Summary of the Invention

[0004] This invention provides an equivalent method and apparatus for an external network area of ​​a power system. By applying a disturbance, the sensitivity of various parameters to the active power flow change curve of a first line is analyzed during the subtransient, transient, and recovery phases. Based on the sensitivity, parameters to be identified for constructing an external equivalent circuit are selected, ensuring consistency in the dynamic response of the power system before and after equivalence.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide an equivalent method for external network areas of a power system, comprising:

[0006] An equivalent synchronous generator model is connected at the boundary node between the reserved area of ​​the power system and the external network area.

[0007] A three-phase short-circuit instantaneous fault is set at the boundary node to obtain the first voltage change curve and the first line active power flow change curve at the boundary node. The time series data of the first voltage change curve is divided into a subtransient stage, a transient stage and a recovery stage.

[0008] Disturbances are applied to each of the first parameters of the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each of the first parameters to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed. And the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified.

[0009] The particle swarm optimization algorithm is used to identify the parameters to be identified at each stage, and based on all the identification results, the external equivalent circuit of the external network region is constructed to complete the external equivalence of the power system.

[0010] In implementing this embodiment of the invention, an equivalent synchronous generator model is connected at the boundary node between the reserved area and the external network area of ​​the power system. Then, a three-phase short-circuit transient fault is set at the boundary node to obtain the first voltage change curve and the first line active power flow change curve at the boundary node. The time series data of the first voltage change curve is divided into a subtransient stage, a transient stage, and a recovery stage. Next, perturbations are applied to each first parameter of the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the equivalent synchronous generator model to obtain the second voltage change curve. Then, based on the comparison of the first voltage change curve and the second voltage change curve, the sensitivity of each parameter to the first line active power flow change curve in the subtransient stage, transient stage, and recovery stage is analyzed to characterize the magnitude of the influence of each parameter under the same perturbation at different time scales. Then, the first parameter with a sensitivity greater than a preset threshold is selected as the identification parameter for constructing the external equivalent circuit of the external network area, so that the dynamic response of the power system before and after equivalence is consistent, and the accuracy of the equivalence results of the external network area of ​​the power system is improved.

[0011] As a preferred embodiment, the step of using a particle swarm optimization algorithm to identify the parameters to be identified at each stage is specifically as follows:

[0012] Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each parameter to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each parameter to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified. A three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve is calculated based on the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the subtransient stage.

[0013] Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each parameter to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each parameter to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified. A three-phase short-circuit transient fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve is calculated based on the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the transient stage.

[0014] Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each of the parameters to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each of the parameters to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve is calculated. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameters to be identified in the recovery phase.

[0015] In a preferred embodiment of the present invention, a particle swarm optimization algorithm is used to iteratively update the parameters to be identified in the subtransient, transient, and recovery stages, gradually reducing the objective function value to obtain the globally optimal particle position value. This enables the identification results of the parameters to be identified in the subtransient, transient, and recovery stages to be identified quickly and accurately.

[0016] As a preferred embodiment, after connecting an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area, the following further steps are included:

[0017] Based on the power flow results of the power system, adjust the output and load values ​​of the equivalent synchronous generator model.

[0018] In a preferred embodiment of the present invention, after connecting an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area, the output and load values ​​of the equivalent synchronous generator model are adjusted according to the power flow results of the power system. This can simulate the power supply conditions and power supply situation of different external network areas, which helps to evaluate the power system's supply and demand balance, power flow distribution and loss situation, and provides a reference for the planning and optimization of the power system.

[0019] As a preferred embodiment, disturbances are applied to each of the first parameters of the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each of the first parameters to the active power flow change curve of the first line in the sub-transient stage, transient stage, and recovery stage is analyzed. First parameters with sensitivity greater than a preset threshold are selected from all the first parameters as parameters to be identified. Specifically:

[0020] Each first parameter of the equivalent synchronous generator model is traversed, and a perturbation is applied to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter. Then, the second parameter corresponding to the current first parameter is input into the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to obtain the sensitivity of the current first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage.

[0021] Based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the subtransient stage, the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified in the subtransient stage.

[0022] Based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the transient phase, the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified during the transient phase.

[0023] Based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the recovery phase, the first parameter with a sensitivity greater than a preset threshold is selected from all the first parameters as the parameter to be identified during the recovery phase.

[0024] In a preferred embodiment of the present invention, each first parameter of the equivalent synchronous generator model is traversed, and a perturbation is applied to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter. Then, the second parameter corresponding to the current first parameter is input into the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to evaluate the impact of the current parameter on the transient stability of the system. In this way, the sensitivity of the current first parameter to the active power flow change curve of the first line in the sub-transient stage, transient stage and recovery stage can be obtained. Based on the sensitivity of all first parameters in different stages, the first parameters with sensitivity greater than a preset threshold in different stages are selected as the parameters to be identified in different stages, so that further analysis and optimization can be carried out on the first parameters that have a significant impact on the stability of the system during the transient process.

[0025] As a preferred embodiment, the equivalent synchronous generator model is a combination model of a sixth-order generator and a controller;

[0026] The controller includes an excitation system, a PSS, a prime mover, and a speed governor.

[0027] In a preferred embodiment of the present invention, an excitation system, a PSS, a prime mover, and a speed governor are added to the sixth-order generator to improve the adaptability and accuracy of the equivalent synchronous generator model, thereby optimizing the equivalent effect in the external network area of ​​the power system.

[0028] To address the same technical problem, embodiments of the present invention also provide an equivalent measurement device for external network areas of a power system, comprising:

[0029] The model access module is used to access an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area.

[0030] The step-by-step module is used to set a three-phase short-circuit instantaneous fault at the boundary node, obtain the first voltage change curve and the first line active power flow change curve at the boundary node, and divide the time series data of the first voltage change curve into a subtransient stage, a transient stage and a recovery stage.

[0031] The sensitivity analysis module is used to apply disturbances to each of the first parameters of the equivalent synchronous generator model, and to set a three-phase short-circuit instantaneous fault in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each of the first parameters to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed, and the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as parameters to be identified.

[0032] The identification equivalence module is used to identify the parameters to be identified at each stage using the particle swarm optimization algorithm, and to construct the external equivalence circuit of the external network region based on all identification results, so as to complete the external equivalence of the power system.

[0033] As a preferred embodiment, the equivalence identification module specifically includes:

[0034] The first identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve is calculated based on the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the subtransient stage.

[0035] The second identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve is calculated based on the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the transient stage.

[0036] The third identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve is calculated based on the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameters to be identified in the recovery phase.

[0037] The equivalent unit is used to construct the external equivalent circuit of the external network area based on the identification results of all stages, so as to complete the external equivalence of the power system.

[0038] As a preferred embodiment, the aforementioned power system external network area equivalent device further includes:

[0039] The model adjustment module is used to adjust the output and load values ​​of the equivalent synchronous generator model based on the power flow results of the power system after an equivalent synchronous generator model is connected at the boundary node between the reserved area and the external network area of ​​the power system.

[0040] As a preferred embodiment, the sensitivity analysis module specifically includes:

[0041] The sensitivity analysis unit is used to traverse each first parameter of the equivalent synchronous generator model, apply a disturbance to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter, then input the second parameter corresponding to the current first parameter into the equivalent synchronous generator model, and set a three-phase short-circuit instantaneous fault in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to obtain the sensitivity of the current first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage.

[0042] The first parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the subtransient stage, based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the subtransient stage.

[0043] The second parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the transient phase, based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the transient phase.

[0044] The third parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the recovery phase, based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the recovery phase.

[0045] As a preferred embodiment, the equivalent synchronous generator model of the power system external network area is a combination model of a sixth-order generator and a controller.

[0046] The controller includes an excitation system, a PSS, a prime mover, and a speed governor. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an equivalent method for external network areas of a power system provided in Embodiment 1 of the present invention.

[0048] Figure 2 This is a schematic diagram of the structure of an equivalent device for an external network area of ​​a power system provided in Embodiment 1 of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1:

[0051] Please refer to Figure 1 This invention provides an equivalent method for external network areas of a power system, comprising steps S1 to S4, each step as follows:

[0052] Step S1: Connect an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area.

[0053] As a preferred option, the equivalent synchronous generator model is a combination model of a sixth-order generator and a controller. The controller includes an excitation system, a PSS (Power Supply System), a prime mover, and a speed governor.

[0054] In this embodiment, the regions and components to be retained are determined based on the researchers' choices, and all of them are retained to form the retained region of the power system. Then, at the boundary node edge_bus between the retained region of the power system and the external network region, an equivalent synchronous generator model is connected. The external network region is the region to be simplified.

[0055] As a preferred embodiment, after performing step S1, step S01 is also included, which is as follows:

[0056] Step S01: Adjust the output and load values ​​of the equivalent synchronous generator model based on the power flow results of the power system.

[0057] In this embodiment, power flow calculations are performed on the power system, recording the voltage value v of the boundary node, the active power flow p, and the reactive power flow q of the connecting lines between the boundary node and the external region. Next, all information about the external network region is deleted, and the boundary node edge_bus is set as an equivalent generator node with a PV node type, and the voltage value of the equivalent generator node is set to v. The line power flow p is then determined. n With q n The direction (n represents the nth route), if p n If the positive direction is from the boundary node to the external network region, then the active load of the equivalent generator node is set to p. n Conversely, the active power output of the equivalent generator is set to p. n ; no reactive current q n Settings and pn same.

[0058] Step S2: Set a three-phase short-circuit instantaneous fault at the boundary node to obtain the first voltage change curve and the first line active power flow change curve at the boundary node, and divide the time series data of the first voltage change curve into a subtransient stage, a transient stage and a recovery stage.

[0059] In this embodiment, step S2 specifically includes steps S21 to S23, and each step is as follows:

[0060] Step S21: Set the initial subtransient reactance parameters and time constant of the equivalent generator using typical values, where X″ d =X″ q =0.1~0.4, the base capacity is the equivalent generator's own capacity T″ d =T″ q =0.05. Next, please refer to equations (1), (2), and (3) to set the initial transient reactance, synchronous reactance, and initial transient time constant of the equivalent synchronous generator model, and set the initial parameters of the controller of the equivalent generator.

[0061]

[0062]

[0063]

[0064] In the formula, X′ d and X′ q X represents the initial transient reactance of the equivalent synchronous generator model. d and X q T′ represents the synchronization reactance of the equivalent synchronous generator model. d and T′ q This represents the initial transient time constant of the equivalent synchronous generator model.

[0065] Step S22: Set a three-phase short-circuit instantaneous fault at the boundary node of the power system to obtain the first voltage change curve and the first line active power flow change curve at the boundary node.

[0066] Among them, the time series data of the first voltage change curve at the boundary node is V=[v1,v2,...,v k The time series data of the active power flow variation curve of the first line at the boundary node is P = [p1, p2, ..., p]. k ].

[0067] Step S23: Divide the time series data of the first voltage change curve into a subtransient stage, a transient stage, and a recovery stage.

[0068] In this embodiment, specifically, according to the BPA data model, the period from the moment of the fault to the first cycle (t0, t0+Δt) is divided into a subtransient stage, the period from the first cycle of the fault to the start of fault recovery (t0+Δt, t1) is divided into a transient stage, and the period from the start of fault recovery (t1) to the attainment of steady state is divided into a fault recovery stage.

[0069] Step S3: Perturbations are applied to each of the first parameters of the equivalent synchronous generator model, and a three-phase short-circuit instantaneous fault is set in the equivalent synchronous generator model to obtain the second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed. The first parameters with sensitivity greater than the preset threshold are selected from all the first parameters as the parameters to be identified.

[0070] As a preferred embodiment, step S3 includes steps S31 to S34, and the specific details of each step are as follows:

[0071] Step S31: Iterate through each first parameter x of the equivalent synchronous generator model, apply a disturbance Δx to the initial value of the current first parameter x, obtain the second parameter x' corresponding to the current first parameter x, then input the second parameter corresponding to the current first parameter x into the equivalent synchronous generator model, and set a three-phase short-circuit instantaneous fault in the current equivalent synchronous generator model to obtain the time series data V″ of the second voltage change curve. Then compare and analyze the time series data V′ of the first voltage change curve and the time series data V″ of the second voltage change curve to obtain the sensitivity of the current first parameter x to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage.

[0072] In this embodiment, the first parameter includes, but is not limited to, generator reactance parameters, time constant, and control parameters of the excitation system and PSS. Here, x′+Δx only needs to satisfy its own physical constraints. For example, for the subtransient reactance and transient reactance of a synchronous motor, the subtransient reactance is smaller than the transient reactance; after setting the new parameter, the subtransient reactance should still be smaller than the transient reactance. Other constraints can be referenced from empirical values.

[0073] Step S32: Based on the sensitivity of all first parameters to the active power flow change curve of the first line in the subtransient stage, select the first parameters whose sensitivity is greater than the preset threshold from all the first parameters as the parameters to be identified in the subtransient stage, and form a set Θ0.

[0074] Step S33: Based on the sensitivity of all first parameters to the active power flow change curve of the first line during the transient phase, select the first parameters whose sensitivity is greater than the preset threshold from all the first parameters as the parameters to be identified during the transient phase, and form a set Θ1.

[0075] Step S34: Based on the sensitivity of all first parameters to the active power flow change curve of the first line during the recovery phase, select first parameters with sensitivity greater than a preset threshold from all first parameters as the parameters to be identified during the recovery phase, forming a set Θ2.

[0076] In this embodiment, the same method as the selection method of the parameters to be identified in the subtransient stage is adopted to analyze the degree of influence of the inertial constant, prime mover and governor related parameters of the equivalent synchronous generator model on the change of the active power flow curve of the first line, and screen out the parameters with greater influence, that is, analyze the sensitivity of the parameters and screen out the parameters with sensitivity greater than the preset threshold, forming a set Θ3.

[0077] Step S4: The particle swarm optimization algorithm is used to identify the parameters to be identified at each stage, and based on all the identification results, the external equivalent circuit of the external network region is constructed to complete the external equivalent of the power system.

[0078] As a preferred embodiment, step S4 includes steps S41 to S43, each of which is detailed below:

[0079] Step S41: Based on the initial values ​​of all first parameters of the pre-set equivalent synchronous generator model, set the initial values ​​of each parameter to be identified. Then, use the particle swarm optimization algorithm to iteratively update each parameter to be identified. During each iteration, update the equivalent synchronous generator model according to the updated parameters to be identified, and set a three-phase short-circuit transient fault in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, calculate the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. Update the global optimal particle position value according to the updated similarity value until the number of iterations meets the preset number. Use the current global optimal particle position value as the identification result of the parameter to be identified in the subtransient stage.

[0080] In this embodiment, step S41 includes steps S411 to S413, and each step is as follows:

[0081] Step S411: Based on the initial values ​​of all first parameters of the pre-set equivalent synchronous generator model, set the initial values ​​of each parameter to be identified. For the similarity between time series data x and y, the Spearman correlation coefficient is used for quantification, and this value is used as the fitness value of the particle swarm optimization objective function. That is, according to the pre-set Spearman algorithm, based on the time series data in the subtransient stage of the first voltage change curve and the updated time series data in the subtransient stage of the second voltage change curve, the similarity ρ between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the second voltage change curve is calculated. For details of the pre-set Spearman algorithm, please refer to equation (4).

[0082]

[0083] Taking the set of parameters to be identified Θ0 as an example, we set the number of parameter particle groups n and the number of iterations m in the particle swarm optimization algorithm, and set the upper and lower limits for each parameter to be identified, R(x i ) and R(y i These are the values ​​and registration of the observed value i, respectively. and Let x and y be the average ranks of variables x and y, respectively. Set the inertia weight ω, individual learning coefficient c1, overall learning coefficient c2, and maximum update rate v in the particle swarm optimization algorithm. max Initialize particle group position values ​​using random numbers. And initialize the update speed v 0 .

[0084] Step S412: Take the data of the original system corresponding to set Θ0, i.e., the subtransient voltage data V1. Substitute each set of particle position value parameters into the model to be identified, set the same fault as the original system, and take the subtransient voltage data corresponding to set Θ0. Initialize fitness value ρ (i0) ,i=1,2,...,n.

[0085] Step S413, set p = [p1, p2, ..., p i ,...,p n ] and g are used to record the optimal position values ​​of their respective particle groups and the global optimal particle group, using p=N 0 Perform initialization, and according to The global optimal position value g is initialized, and finally the local optimal fitness value F = [f1, f2, ..., f n ], And the global optimal value G = max(F). Where p = [p1, p2, ..., p i ,...,p n[] represents the optimal position value of each particle group.

[0086] Step S414, in the k-th iteration, for each group of particles Substituting its parameters into the fault conditions set in the generator to be identified, the same fault conditions as in the original system, yields the subtransient voltage curve data of the boundary nodes. And finally obtain the data corresponding to all groups of particles.

[0087] Step S415, calculate Spearman coefficient ρ with respect to V0 (ik) and constitute when At that time, And update the global optimal particle position value g.

[0088] Step S416, see Equation (5) to update the particle velocity, see Equation (6) to update the particle group position parameters.

[0089] v k+1 =w*v k +c1*r1*(px k )+c1*r1*(gx k (5)

[0090] N (k+1) =v (k) +N (k) (6)

[0091] Step S417: Repeat steps S411 to S416 based on the updated parameters and velocity until the number of iterations reaches n*m, then end the iteration and take the current global optimal particle position value g as the identification result of the parameters to be identified in the subtransient stage.

[0092] Step S42: Based on the initial values ​​of all first parameters of the pre-set equivalent synchronous generator model, set the initial values ​​of each parameter to be identified. Then, use the particle swarm optimization algorithm to iteratively update each parameter to be identified. During each iteration, update the equivalent synchronous generator model according to the updated parameters to be identified, and set a three-phase short-circuit transient fault in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, calculate the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve. Update the global optimal particle position value according to the updated similarity value until the number of iterations meets the preset number. Use the current global optimal particle position value as the identification result of the parameter to be identified in the transient stage.

[0093] Step S43: Based on the initial values ​​of all first parameters of the pre-set equivalent synchronous generator model, set the initial values ​​of each parameter to be identified. Then, use the particle swarm optimization algorithm to iteratively update each parameter to be identified. During each iteration, update the equivalent synchronous generator model according to the updated parameters to be identified, and set a three-phase short-circuit instantaneous fault in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, calculate the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve. Update the global optimal particle position value according to the updated similarity value until the number of iterations meets the preset number. Use the current global optimal particle position value as the identification result of the parameter to be identified in the recovery phase.

[0094] It should be noted that the specific implementation process of steps S42 and S43 can refer to steps S411 to S417 to identify the parameters to be identified in the transient and recovery stages. The data used is the voltage curve data of the transient and recovery stages. The parameters of set Θ3 can also be identified using the same method, using the active power flow change curve of the first line.

[0095] Please refer to Figure 2 This is a schematic diagram of the structure of an equivalent device for an external network area of ​​a power system provided in an embodiment of the present invention. The device includes a model access module M1, a hierarchical module M2, a sensitivity analysis module M3, and an equivalent identification module M4. The specific details of each module are as follows:

[0096] The model access module M1 is used to access an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area.

[0097] The step-by-step module M2 is used to set a three-phase short-circuit instantaneous fault at the boundary node, obtain the first voltage change curve and the first line active power flow change curve at the boundary node, and divide the time series data of the first voltage change curve into the subtransient stage, the transient stage and the recovery stage.

[0098] Sensitivity analysis module M3 is used to apply disturbances to each of the first parameters of the equivalent synchronous generator model, and to set a three-phase short-circuit instantaneous fault in the equivalent synchronous generator model to obtain the second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed, and the first parameter with a sensitivity greater than a preset threshold is selected from all the first parameters as the parameter to be identified.

[0099] The identification equivalence module M4 is used to identify the parameters to be identified at each stage using the particle swarm optimization algorithm, and to construct the external equivalent circuit of the external network region based on all identification results, so as to complete the external equivalence of the power system.

[0100] As a preferred embodiment, the equivalence identification module M4 specifically includes a first identification unit 41, a second identification unit 42, a third identification unit 43, and an equivalence identification unit 44, with each unit described in detail below:

[0101] The first identification unit 41 is used to set the initial values ​​of each parameter to be identified based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each parameter to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve is calculated based on the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the subtransient stage.

[0102] The second identification unit 42 is used to set the initial values ​​of each parameter to be identified based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each parameter to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve is calculated based on the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number of iterations. The current global optimal particle position value is used as the identification result of the parameter to be identified in the transient stage.

[0103] The third identification unit 43 is used to set the initial values ​​of each parameter to be identified based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each parameter to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve is calculated based on the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number of iterations. The current global optimal particle position value is used as the identification result of the parameter to be identified in the recovery phase.

[0104] Equivalence unit 44 is used to construct an external equivalent circuit for the external network area based on the identification results of all stages, so as to complete the external equivalence of the power system.

[0105] As a preferred embodiment, the power system external network area equivalent device provided in this invention further includes a model adjustment module M5, which is specifically as follows:

[0106] The model adjustment module M5 is used to adjust the output and load values ​​of an equivalent synchronous generator model based on the power flow results of the power system after connecting an equivalent synchronous generator model at the boundary node between the reserved area and the external network area of ​​the power system.

[0107] As a preferred embodiment, the sensitivity analysis module M3 specifically includes a sensitivity analysis unit 31, a first parameter screening unit 32, a second parameter screening unit 33, and a third parameter screening unit 34, with each unit described in detail below:

[0108] Sensitivity analysis unit 31 is used to traverse each first parameter of the equivalent synchronous generator model, apply a disturbance to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter, then input the second parameter corresponding to the current first parameter into the equivalent synchronous generator model, and set a three-phase short-circuit instantaneous fault in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to obtain the sensitivity of the current first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage.

[0109] The first parameter filtering unit 32 is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the subtransient stage, and to use them as the parameters to be identified in the subtransient stage.

[0110] The second parameter filtering unit 33 is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the transient stage, and to use them as the parameters to be identified in the transient stage.

[0111] The third parameter filtering unit 34 is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the recovery phase, and to use them as the parameters to be identified during the recovery phase.

[0112] As a preferred embodiment, the equivalent synchronous generator model in the equivalent device of the external network area of ​​the power system provided by the present invention is a combination model of a sixth-order generator and a controller.

[0113] The controller includes an excitation system, a PSS (Power Supply System), a prime mover, and a speed governor.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0116] This invention provides an equivalent method and apparatus for an external network region of a power system. An equivalent synchronous generator model is connected at the boundary node between the reserved area of ​​the power system and the external network region. A three-phase short-circuit transient fault is then set at the boundary node to obtain a first voltage change curve and a first line active power flow change curve. The time series data of the first voltage change curve is divided into a subtransient stage, a transient stage, and a recovery stage. Next, perturbations are applied to each first parameter of the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the comparison between the first and second voltage change curves, the sensitivity of each parameter to the first line active power flow change curve in the subtransient, transient, and recovery stages is analyzed to characterize the magnitude of the influence of each parameter under the same perturbation at different time scales. Finally, first parameters with sensitivity greater than a preset threshold are selected as the parameters to be identified for constructing the external equivalent circuit of the external network region, ensuring consistency in the dynamic response of the power system before and after equivalence.

[0117] Furthermore, by employing the particle swarm optimization algorithm, the parameters to be identified in the subtransient, transient, and recovery stages are iteratively updated, and the objective function value is gradually reduced to obtain the globally optimal particle position value. This enables the identification results of the parameters to be identified in the subtransient, transient, and recovery stages to be identified quickly and accurately.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for equivalence of external network areas in a power system, characterized in that, include: An equivalent synchronous generator model is connected at the boundary node between the reserved area of ​​the power system and the external network area. A three-phase short-circuit instantaneous fault is set at the boundary node to obtain the first voltage change curve and the first line active power flow change curve at the boundary node. The time series data of the first voltage change curve is divided into a subtransient stage, a transient stage and a recovery stage. Disturbances are applied to each of the first parameters of the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each of the first parameters to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed. And the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified. The particle swarm optimization algorithm is used to identify the parameters to be identified at each stage, and based on all the identification results, the external equivalent circuit of the external network region is constructed to complete the external equivalence of the power system.

2. The method for equivalence of external network areas in a power system according to claim 1, characterized in that, The particle swarm optimization algorithm is used to identify the parameters to be identified at each stage, specifically as follows: Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each parameter to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each parameter to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified. A three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve is calculated based on the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the subtransient stage. Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each parameter to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each parameter to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified. A three-phase short-circuit transient fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve is calculated based on the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the transient stage. Based on the initial values ​​of all the first parameters of the pre-set equivalent synchronous generator model, the initial values ​​of each of the parameters to be identified are set. Then, the particle swarm optimization algorithm is used to iteratively update each of the parameters to be identified. During each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve is calculated. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameters to be identified in the recovery phase.

3. The method for equivalence of external network areas in a power system according to claim 1, characterized in that, After connecting an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area, the following is also included: Based on the power flow results of the power system, adjust the output and load values ​​of the equivalent synchronous generator model.

4. The method for equivalence of external network areas in a power system according to claim 1, characterized in that, The process involves applying perturbations to each of the first parameters of the equivalent synchronous generator model, setting a three-phase short-circuit transient fault in the equivalent synchronous generator model, obtaining a second voltage change curve, and then analyzing the sensitivity of each of the first parameters to the active power flow change curve of the first line in the subtransient stage, transient stage, and recovery stage based on the first voltage change curve and the second voltage change curve. Specifically, the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as parameters to be identified. Each first parameter of the equivalent synchronous generator model is traversed, and a perturbation is applied to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter. Then, the second parameter corresponding to the current first parameter is input into the equivalent synchronous generator model, and a three-phase short-circuit transient fault is set in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to obtain the sensitivity of the current first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage. Based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the subtransient stage, the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified in the subtransient stage. Based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the transient phase, the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as the parameters to be identified during the transient phase. Based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the recovery phase, the first parameter with a sensitivity greater than a preset threshold is selected from all the first parameters as the parameter to be identified during the recovery phase.

5. The method for equivalence of external network areas in a power system according to claim 1, characterized in that, The equivalent synchronous generator model is a combined model of a sixth-order generator and a controller; The controller includes an excitation system, a PSS, a prime mover, and a speed governor.

6. An equivalent measurement device for an external network area of ​​a power system, characterized in that, include: The model access module is used to access an equivalent synchronous generator model at the boundary node between the reserved area of ​​the power system and the external network area. The step-by-step module is used to set a three-phase short-circuit instantaneous fault at the boundary node, obtain the first voltage change curve and the first line active power flow change curve at the boundary node, and divide the time series data of the first voltage change curve into a subtransient stage, a transient stage and a recovery stage. The sensitivity analysis module is used to apply disturbances to each of the first parameters of the equivalent synchronous generator model, and to set a three-phase short-circuit instantaneous fault in the equivalent synchronous generator model to obtain a second voltage change curve. Then, based on the first voltage change curve and the second voltage change curve, the sensitivity of each of the first parameters to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage is analyzed, and the first parameters with sensitivity greater than a preset threshold are selected from all the first parameters as parameters to be identified. The identification equivalence module is used to identify the parameters to be identified at each stage using the particle swarm optimization algorithm, and to construct the external equivalence circuit of the external network region based on all identification results, so as to complete the external equivalence of the power system.

7. The power system external network area equivalent device according to claim 6, characterized in that, The equivalent identification module specifically includes: The first identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve is calculated based on the time series data in the subtransient stage of the first voltage change curve and the time series data in the subtransient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the subtransient stage. The second identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve is calculated based on the time series data in the transient stage of the first voltage change curve and the time series data in the transient stage of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameter to be identified in the transient stage. The third identification unit is used to set the initial values ​​of each of the parameters to be identified based on the initial values ​​of all the first parameters of the equivalent synchronous generator model, and then use the particle swarm optimization algorithm to iteratively update each of the parameters to be identified. In each iteration, the equivalent synchronous generator model is updated according to the updated parameters to be identified, and a three-phase short-circuit instantaneous fault is set in the updated equivalent synchronous generator model to update the second voltage change curve. Then, according to the preset Spearman algorithm, the similarity between the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve is calculated based on the time series data in the recovery phase of the first voltage change curve and the time series data in the recovery phase of the updated second voltage change curve. The global optimal particle position value is updated according to the updated similarity value until the number of iterations meets the preset number. The current global optimal particle position value is used as the identification result of the parameters to be identified in the recovery phase. The equivalent unit is used to construct the external equivalent circuit of the external network area based on the identification results of all stages, so as to complete the external equivalence of the power system.

8. The power system external network area equivalent device according to claim 6, characterized in that, Also includes: The model adjustment module is used to adjust the output and load values ​​of the equivalent synchronous generator model based on the power flow results of the power system after an equivalent synchronous generator model is connected at the boundary node between the reserved area and the external network area of ​​the power system.

9. The power system external network area equivalent device as described in claim 6, characterized in that, The sensitivity analysis module specifically includes: The sensitivity analysis unit is used to traverse each first parameter of the equivalent synchronous generator model, apply a disturbance to the initial value of the current first parameter to obtain the second parameter corresponding to the current first parameter, then input the second parameter corresponding to the current first parameter into the equivalent synchronous generator model, and set a three-phase short-circuit instantaneous fault in the current equivalent synchronous generator model to obtain the second voltage change curve. Then, the first voltage change curve and the second voltage change curve are compared and analyzed to obtain the sensitivity of the current first parameter to the active power flow change curve of the first line in the subtransient stage, transient stage and recovery stage. The first parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the subtransient stage, based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the subtransient stage. The second parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the transient phase, based on the sensitivity of all the first parameters to the active power flow change curve of the first line in the transient phase. The third parameter filtering unit is used to filter out the first parameters whose sensitivity is greater than a preset threshold from all the first parameters as the parameters to be identified in the recovery phase, based on the sensitivity of all the first parameters to the active power flow change curve of the first line during the recovery phase.

10. The power system external network area equivalent device as described in claim 6, characterized in that, The equivalent synchronous generator model is a combined model of a sixth-order generator and a controller; The controller includes an excitation system, a PSS, a prime mover, and a speed governor.

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