Method, system and device for identifying weak line of voltage-dominated cascading failure of power system

Abstract

The invention provides a power system voltage-dominated cascading fault weak line identification method, system and device, and the method comprises the steps: constructing a voltage estimation knowledge graph according to a topological structure, fault historical data and simulation data of a power system; establishing a fault scene set, sequentially selecting each fault scene, determining a corresponding white box dendritic network model by using the knowledge graph, and calculating node voltage in the scene; judging whether a voltage response event occurs or not according to a calculation result, and if a condition is met, identifying the event and judging whether equipment locking or off-network is involved or not so as to determine whether the line is a weak line or not; and if the condition is not met, continuing to search other fault scenes. According to the method, the weak line in the power system can be accurately identified, and a reliable basis is provided for design, operation control and protection measure optimization of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system protection and fault analysis, and particularly to a method, system and device for identifying weak lines of voltage-dominated cascading faults in a power system. Background Art

[0002] With the rapid development of renewable energy, power electronic equipment such as wind power generation, photovoltaic power generation and HVDC transmission has been widely connected to the power system, promoting the transformation of the power system towards the direction of power electronics. Power electronic equipment has high sensitivity and strong coupling, which makes the fault evolution process in a power electronic power system more complex. After a fault occurs in the power grid, the change of system voltage will cause the switching of the operation mode and control strategy of power electronic equipment, or trigger protection actions, which will further lead to the change of grid voltage. The voltage change will affect the output characteristics of power electronic equipment again, forming a cyclic feedback process. In a power electronic power system, the influence range of a single fault may expand rapidly, resulting in the disconnection of power electronic equipment and even causing system stability problems, presenting a new form of cascading faults dominated by voltage.

[0003] According to different inducing factors of cascading faults, existing research mainly divides cascading faults into three categories: overload-dominated, structure-dominated and coordination-dominated. Overload-dominated cascading faults refer to the transfer of power flow due to a fault, resulting in overload of other lines and equipment, triggering cascading tripping. Structure-dominated cascading faults are caused by the failure of key nodes or lines, which changes the topological structure of the system, and then triggers cascading faults. Coordination-dominated cascading faults are caused by improper setting or slow response of protection devices, triggering faults of other equipment. In addition, some research has also focused on the cascading reaction caused by abnormal frequency fluctuations. However, the generation mechanism and manifestation of voltage-dominated cascading faults are significantly different from those of overload, structure, coordination-dominated or frequency-dominated cascading faults, and existing research is still difficult to explain the evolution law of voltage-dominated cascading faults.

[0004] With the wide connection of power electronic equipment, the characteristics of fault conduction through voltage have gradually attracted attention. Existing research mainly focuses on the influence of voltage change on a single power electronic equipment or the response of multiple power electronic equipment through voltage coupling, and has not fully considered the secondary response of power electronic equipment to voltage and its conduction effect. Therefore, the research on voltage-dominated cascading faults is still in its infancy, and the relevant mechanisms and laws are not yet clear.

[0005] To effectively defend against cascading failures, it is necessary to optimize the power grid topology by identifying the key lines that cause fault propagation and expansion. Currently, the identification of weak lines in cascading failures mainly uses methods such as mathematical modeling and simulation, complex network theory, data-driven methods, and reliability assessment and optimization. Mathematical modeling analyzes the dynamic behavior of the system by establishing a dynamic model or load flow model of the power system, and identifies weak lines through key evaluation indicators and sensitivity analysis; complex network theory uses graph theory and network analysis techniques to study the relationships between nodes and edges in the power system and identify key nodes and weak lines; data-driven methods combine historical and real-time monitoring data and use machine learning and data mining techniques to identify weak lines in cascading failures; reliability assessment methods evaluate the performance of lines under different fault scenarios by calculating the reliability indicators and failure rates of lines, and then identify weak lines that may trigger cascading failures. However, the existing methods for identifying weak lines in cascading failures mainly focus on the power flow transfer characteristics of overload-dominated cascading failures and do not fully consider the voltage change process caused by faults and the changes in the operating modes of power electronic equipment. Therefore, they are not suitable for identifying weak lines in voltage-dominated cascading failures.

[0006] Based on the above background, there is an urgent need to develop a method for identifying weak lines in voltage-dominated cascading failures to solve the deficiencies of the existing technologies in identifying voltage-dominated cascading failures and improve the stability and reliability of the power system. Summary of the Invention

[0007] Aiming at the deficiencies of the above existing technologies, the present invention provides a method for identifying weak lines in voltage-dominated cascading failures of a power system. This method constructs a voltage estimation model using the topology of the power system, historical fault data, and simulation data. For each fault scenario, it calculates the node voltage and determines whether a voltage response event occurs, and then determines whether it is a weak line based on the voltage response events that occur. The present invention effectively solves the problem of identifying weak lines in voltage-dominated cascading failures in a power system, providing technical support for the operation and maintenance of power electronic power systems.

[0008] To solve the above technical problems, the present invention adopts the following technical solutions:

[0009] A method for identifying weak lines in voltage-dominated cascading failures of a power system, characterized by comprising the following steps:

[0010] S1. Establish a set of fault scenarios to be searched for the power system, and construct a knowledge graph for voltage estimation of the power system according to the topology of the power system and historical fault data; the historical fault data includes the active power, reactive power, and voltage of the access node output by power electronic equipment under short-circuit faults of the power system.

[0011] S2. Select a fault scenario from the set of fault scenarios, and use the power system voltage estimation knowledge graph to determine the white-box dendritic network voltage estimation model for this fault scenario;

[0012] S3. Use the white-box dendritic network voltage estimation model to estimate the power system node voltages for this fault scenario;

[0013] S4. Based on the estimated power system node voltages, determine whether a voltage response event will occur; if so, identify the voltage response event that will occur and execute step S5; otherwise, return to step S2;

[0014] S5. Based on the active power and reactive power output by the power electronic equipment after the voltage response event occurs, determine whether the voltage response event that occurs is the locking or disconnection of the power electronic equipment; if so, execute step S7; otherwise, execute step S6;

[0015] S6. Based on the active power and reactive power output by the power electronic equipment after the voltage response event occurs, use the white-box dendritic network voltage estimation model for this fault scenario to re-estimate the power system node voltages for this fault scenario after the above voltage response event occurs, and return to step S4;

[0016] S7. Determine that the line where the fault point is located in the current fault scenario is a weak line; then, determine whether every fault scenario in the set of fault scenarios has been traversed and searched; if so, return to step S2 to select a new fault scenario; otherwise, end the search;

[0017] In this way, traverse and search every fault scenario in the set of fault scenarios, complete the identification operation of the weak lines for each fault scenario, and obtain the identification result of the weak lines for cascading faults in the power system.

[0018] Specifically, in step S1, the set of fault scenarios is constructed as follows:

[0019] Select any transmission line in the power system, select a position from the preset specified positions on the transmission line to set a short-circuit fault point, the fault condition of the short-circuit fault point is one of three-phase short circuit, two-phase short circuit or single-phase short circuit, and set the fault transition impedance value of the short-circuit fault point within the preset fault transition impedance value range, thereby forming a fault scenario;

[0020] Selecting different transmission lines in the power system, or selecting different positions from the preset specified positions on the transmission line to set short-circuit fault points, or setting different fault conditions for the short-circuit fault points, or setting different fault transition impedance values for the short-circuit fault points are all regarded as forming different fault scenarios;

[0021] Thus, a number of different fault scenarios are formed to constitute a fault scenario set.

[0022] Specifically, in step S1, the power system voltage estimation knowledge graph is constructed as follows:

[0023] Take the initial network structure of the power system as the first-level entity of the knowledge graph; form the basic fault network framework with different transmission line faults in the power system, and take the basic fault network framework as the second-level entity of the knowledge graph; form the detailed fault network framework with faults at different positions of each transmission line, and take the detailed fault network framework as the third-level entity of the knowledge graph; form specific fault scenarios with different fault transition impedance values at each fault position, and take the specific fault scenarios as the fourth-level entities of the knowledge graph; take the white-box dendritic network voltage estimation model under the specific fault scenario as the attribute of the fourth-level entity of the knowledge graph; the relationship between the first and second-level entities is the fault line, the relationship between the second and third-level entities is the fault position, and the relationship between the third and fourth-level entities is the fault transition impedance; thus, the power system voltage estimation knowledge graph is constructed.

[0024] Specifically, in step S2, the white-box dendritic network voltage estimation model is determined as follows:

[0025] Utilize the power system voltage estimation knowledge graph, determine the corresponding second-level entity according to the fault line of the fault scenario; determine the corresponding third-level entity according to the fault position corresponding to the fault scenario; determine the corresponding fourth-level entity according to the fault transition impedance corresponding to the fault scenario; determine the white-box dendritic network voltage estimation model of the fault scenario according to the relationship between the fourth-level entity and the attribute.

[0026] Specifically, in step S3, the specific method for estimating the power system node voltage of the fault scenario by using the white-box dendritic network voltage estimation model is as follows:

[0027] U = A k ;

[0028]

[0029] In the formula, U represents the node voltage of the power system of the fault scenario; A l and A l-1 are respectively the non-linear outputs of the l-th layer and the (l - 1)-th layer of the white-box dendritic network voltage estimation model, l = 2, 3,..., k, k represents the number of dendritic layers of the white-box dendritic network voltage estimation model, and A k represents the non-linear output of the final layer of the white-box dendritic network voltage estimation model; × represents matrix multiplication operation; represents Hadamard product operation; W l / (l-1) is the weight parameter of the l-th layer of the white-box dendritic network voltage estimation model; PQ inis the injection power vector of the power system nodes, PQ in =[P 1,in , Q 1,in , P 2,in , Q 2,in , …, P p,in , Q p,in , …, P n,in , Q n,in , where P p,in , Q p,in are the active power and reactive power injected into node p respectively, p = 2, 3, …, n, and n is the number of nodes in the power system;

[0030] At the moment of fault occurrence, the power of the power electronic equipment remains unchanged. According to the active power and reactive power output by the power electronic equipment before the fault, the injection power vector PQ of the power system nodes is determined in , so as to estimate the node voltage after the fault; after the voltage response event occurs, according to the active and reactive power output by the power electronic equipment after the event, the injection power vector PQ of the power system nodes is determined in , so as to estimate the node voltage after the voltage response event occurs.

[0031] Specifically, in step S4, the specific method for judging whether a voltage response event will occur is as follows: when the access node voltage of any power electronic equipment satisfies the starting voltage range of the voltage response event of this power electronic equipment, it is judged that a voltage response event will occur;

[0032] The specific method for identifying the voltage response event that will occur is as follows: if only the starting voltage range of one voltage response event is satisfied, it is determined that this voltage response event is the voltage response event that will occur; if the starting voltage ranges of multiple voltage response events are satisfied, it is determined that the voltage response event with the shortest remaining action time is the voltage response event that will occur;

[0033] The remaining action time of the voltage response event is calculated as follows:

[0034]

[0035] In the formula, τ i represents the remaining action time of voltage response event i; represents the trigger action time or termination action time of voltage response event i, represents the moment when the voltage satisfies the trigger or termination voltage range of voltage response event i, a represents the action time variable of the voltage response event, a = trigger represents the trigger action time, a = end represents the termination action time; t is the current moment.

[0036] Specifically, in step S5, after the voltage response event occurs, the active and reactive powers output by the power electronic equipment are determined as follows:

[0037]

[0038] In the formula, P p and Q p are respectively the active power and reactive power output by the power electronic equipment connected to node p after the voltage response event occurs; U p represents the voltage of node p; represents the starting voltage range or holding voltage range of voltage response event i, and T i x represents the duration during which the voltage at the equipment connection point satisfies the starting voltage range or holding voltage range of voltage response event i. x represents the voltage range variable of the voltage response event. When x = start, it represents the starting voltage range, and when x = maintain, it represents the holding voltage range; f i,p (·) and g i,p (·) respectively represent the mathematical relationships of the active power P p and reactive power Q p output by the power electronic equipment connected to node p after voltage response event i occurs with respect to the voltage U p of node p;

[0039] If the power electronic equipment is disconnected / locked out, f i,p (·) and g i,p (·) are zero; if the voltage support control or high-voltage ride-through control of the power electronic equipment is started, f i,p (·) and g i,p (·) are determined by the control strategy; if the crowbar of the VSC-type power electronic equipment is put in or the commutation failure occurs in the LCC-type power electronic equipment, f i,p (·) and g i,p (·) are obtained from the state equation of the power electronic equipment or by fitting the power-voltage external characteristic curve of the power electronic equipment.

[0040] Specifically, in step S6, according to the active power and reactive power output by the power electronic equipment after the voltage response event occurs, the specific method for re-estimating the node voltage of the power system in this fault scenario after the above voltage response event occurs by using the white-box dendritic network voltage estimation model of this fault scenario is as follows:

[0041] U = A k ;

[0042]

[0043] In the formula, U represents the node voltage of the power system in this fault scenario; Al and A l-1 are the non - linear outputs of the l - th layer and the (l - 1)-th layer of the white - box dendritic network voltage estimation model respectively, where l = 2, 3, …, k, and k represents the number of dendritic layers of the white - box dendritic network voltage estimation model. A k represents the non - linear output of the final layer of the white - box dendritic network voltage estimation model; × represents matrix multiplication operation; represents Hadamard product operation; W l / (l-1_ is the weight parameter of the l - th layer of the white - box dendritic network voltage estimation model; PQ is the power vector output by the power electronic equipment after the voltage response event occurs, PQ = [P 1 , Q 1 , P 2 , Q 2 , …, P p , Q p , …, P n , Q n , where P p and Q p are the active power and reactive power output by the power electronic equipment connected to node p after the voltage response event occurs respectively, where p = 2, 3, …, n, and n is the number of nodes in the power system.

[0044] In a second aspect, the present invention also provides a weak line identification system for voltage - dominated cascading faults in a power system, including a data acquisition module, a display module, a first storage module, a second storage module, a first calculation module, a second calculation module, a third calculation module, a fourth calculation module, a first comparison module, a second comparison module, and a third comparison module;

[0045] The data acquisition module is used to collect the active power, reactive power output by the power electronic equipment in the power system, and the voltage of the access node;

[0046] The display module is used to display the identification result of the weak line of the cascading fault in the power system;

[0047] The first storage module is used to store the voltage estimation knowledge graph of the power system;

[0048] The second storage module is used to store the starting voltage range of the voltage response event and the action time of the voltage response event;

[0049] The first calculation module estimates and calculates the node voltage of the power system according to the active power and reactive power output by the electronic equipment collected by the data acquisition module at the moment of the fault, or the active power and reactive power output by the power electronic equipment after the voltage response event calculated by the third calculation module;

[0050] The second calculation module is used to calculate the remaining action time of the voltage response event;

[0051] The third calculation module is used to calculate the active power and reactive power output by the power electronic equipment after the voltage response event occurs;

[0052] The fourth calculation module trains the weight parameters of the white-box dendritic network voltage estimation model based on the historical data of the active power, reactive power, and access node voltage collected by the data acquisition module, and obtains the white-box dendritic network voltage estimation model with determined weight parameters;

[0053] The first comparison module is used to compare whether the node voltage estimation value satisfies the starting voltage range of the voltage response event in the termination state or the holding voltage range of the voltage response event in the trigger state; if so, the second comparison module is called; otherwise, the first calculation module and the second calculation module are called for recalculation;

[0054] The second comparison module is used to compare whether the remaining action time of the voltage response event is 0; if so, the third comparison module is called; otherwise, the third calculation module is called for recalculation;

[0055] The third comparison module is used to compare and judge whether the occurring voltage response event is the locking or disconnection of the power electronic equipment; if so, it is determined that the line where the fault point is located in the current fault scenario is the weak line, and the display module is called to display the identification result of the weak line of the power system cascading fault; otherwise, the third calculation module is called for recalculation.

[0056] In a third aspect, the present invention also provides a power system voltage-dominated cascading fault weak line identification device, including a processor and a storage medium; the storage medium is used to store a computer program; the processor is connected to the storage medium and is used to execute the computer program stored in the storage medium, so that the power system voltage-dominated cascading fault weak line identification device executes the above-mentioned power system voltage-dominated cascading fault weak line identification method.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The prior art focuses on power system cascading faults dominated by overload or topological failure, and cannot reflect the process of secondary response of power electronic equipment due to voltage disturbance, and cannot be used for voltage-dominated cascading faults; the present invention identifies the influence of node voltage changes on power electronic devices and the role of power electronic device control or protection responses on node voltage, and can identify the problems of voltage-dominated weak lines.

[0059] 2. The prior art evaluates the lines most likely to trigger large-scale power outages in various scenarios through probabilistic methods such as failure rate and Monte Carlo simulation. The present invention models the dynamic evolution process of cascading failures, depicts the voltage response mode of power electronic equipment, and identifies voltage-dominated weak lines through a combined physical and data-driven approach, which has the advantages of high efficiency and accuracy.

[0060] 3. The prior art simulates the dynamic process of equipment after a line is disconnected through simulation to identify the key lines or fault modes that lead to system collapse. However, it is extremely difficult to perform detailed modeling of a power electronic power system, and voltage-dominated cascading failures are electromagnetic transient processes with a large amount of calculation, making simulation difficult to apply. The present invention constructs a knowledge graph for power system voltage estimation and uses a white-box dendritic network to quickly infer the node voltage, enabling rapid identification of the weak lines of the most voltage-dominated cascading failures in a short time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to make the objectives, technical solutions, and advantages of the invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings, where:

[0062] Figure 1 is a flowchart of the method for identifying weak lines of voltage-dominated cascading failures in a power system according to the present invention;

[0063] Figure 2 is a schematic diagram of the structure of a power system with power electronic equipment connected;

[0064] Figure 3 is for line L 10-11 simulation waveforms of the grid connection point voltage, active power, and reactive power of power electronic equipment during a line L fault. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0067] The present invention provides a method for identifying weak lines in voltage-dominated cascading faults in a power system. This method constructs a voltage estimation model using the topological structure, historical fault data, and simulation data of the power system. For each fault scenario, it calculates the node voltage and determines whether a voltage response event occurs, and then determines whether it is a weak line based on the voltage response event that occurs. The present invention effectively solves the problem of identifying weak lines in voltage-dominated cascading faults in a power system, providing technical support for the operation and maintenance of power electronic power systems.

[0068] Based on the above technical design ideas, the present invention provides a method for identifying weak lines in voltage-dominated cascading faults in a power system, and its specific process is as Figure 1 shown, including the following steps:

[0069] S1. Establish a set of fault scenarios to be searched for the power system, and construct a voltage estimation knowledge graph of the power system according to the topological structure of the power system and historical fault data; the historical fault data includes the active power, reactive power, and voltage of the access node output by power electronic equipment under short-circuit faults in the power system.

[0070] Specifically, the power electronic equipment on the lines in the power system includes wind farms, photovoltaic power stations, energy storage power stations, DC converter stations, etc.

[0071] S2. Select a fault scenario from the set of fault scenarios, and use the voltage estimation knowledge graph of the power system to determine the white-box dendritic network voltage estimation model for this fault scenario.

[0072] S3. Use the white-box dendritic network voltage estimation model to estimate the node voltage of the power system for this fault scenario.

[0073] S4. According to the estimated node voltage of the power system, determine whether a voltage response event will occur; if so, identify the voltage response event that will occur and execute step S5, otherwise, return to step S2.

[0074] S5. According to the active power and reactive power output by the power electronic equipment after the voltage response event occurs, determine whether the voltage response event that occurs is the locking or disconnection of the power electronic equipment; if so, execute step S7; otherwise, execute step S6.

[0075] S6. According to the active power and reactive power output by the power electronic equipment after the voltage response event occurs, use the white-box dendritic network voltage estimation model of this fault scenario to re-estimate the power system node voltage of this fault scenario after the above voltage response event occurs, and return to step S4;

[0076] S7. Determine that the line where the fault point is located in the current fault scenario is a weak line; then, determine whether each fault scenario in the fault scenario set has been traversed and searched; if so, return to step S2 to re-select a fault scenario; otherwise, end the search;

[0077] Thus, traverse and search each fault scenario in the fault scenario set, complete the identification operation of the weak lines of each fault scenario, and obtain the identification result of the weak lines of the cascading faults of the power system.

[0078] In specific implementation, in step S1, the fault scenario set is constructed in the following manner:

[0079] Select any transmission line in the power system, select a position among each preset specified position on the transmission line to set a short-circuit fault point, the fault condition of the short-circuit fault point is one of three-phase short circuit, two-phase short circuit or single-phase short circuit, and set the fault transition impedance value of the short-circuit fault point within the preset fault transition impedance value range, thus forming a fault scenario;

[0080] Selecting different transmission lines in the power system, or selecting different positions among the preset specified positions on the transmission line to set short-circuit fault points, or setting different fault conditions for the short-circuit fault points, or setting different fault transition impedance values for the short-circuit fault points, are all regarded as forming different fault scenarios respectively;

[0081] Thus, a number of different fault scenarios are formed to constitute the fault scenario set.

[0082] For example, if a specified position is preset every 5% length on the transmission line, the impedance value range is from 0 ohm to 1000 ohm, and integers are taken; then when constructing the fault scenario set, each transmission line in the power system can be selected in turn, a short-circuit fault point is selected and set at a specified position every 5% length of the transmission line, and three-phase short circuit, two-phase short circuit and single-phase short circuit are set in turn to form three forms of fault conditions, and the fault transition impedance is set once every 1 ohm from 0 ohm to 1000 ohm, and 1000 different fault situations can be set for each fault condition; any one of the fault situations thus formed is used as a fault scenario, and the set of all fault situations constitutes the fault scenario set.

[0083] In specific implementation, in step S1, the power system voltage estimation knowledge graph is constructed in the following manner:

[0084] Take the initial network structure of the power system as the first-level entity of the knowledge graph; form the basic fault grid by different transmission line faults in the power system, and take the basic fault grid as the second-level entity of the knowledge graph; form the detailed fault grid by faults at different positions of each transmission line, and take the detailed fault grid as the third-level entity of the knowledge graph; form specific fault scenarios by different fault transition impedance values at each fault position, and take the specific fault scenarios as the fourth-level entities of the knowledge graph; take the white-box dendritic network voltage estimation model under the specific fault scenario as the attribute of the fourth-level entity of the knowledge graph; the relationship between the first- and second-level entities is the fault line, the relationship between the second- and third-level entities is the fault position, and the relationship between the third- and fourth-level entities is the fault transition impedance; thus, a power system voltage estimation knowledge graph is constructed.

[0085] In the specific implementation, in step S2, determine the white-box dendritic network voltage estimation model in the following manner:

[0086] Utilize the power system voltage estimation knowledge graph. According to the fault line of the fault scenario, determine the corresponding second-level entity; according to the fault position corresponding to the fault scenario, determine the corresponding third-level entity; according to the fault transition impedance corresponding to the fault scenario, determine the corresponding fourth-level entity; according to the relationship between the fourth-level entity and the attribute, determine the white-box dendritic network voltage estimation model of the fault scenario.

[0087] In the specific implementation, in step S4, adopt the white-box dendritic network voltage estimation model to calculate the node voltage of the power system in the following manner for this fault scenario:

[0088]

[0089] In the formula, U represents the node voltage of the power system in this fault scenario; A l and A l-1 are respectively the non-linear outputs of the l-th layer and the (l - 1)-th layer of the white-box dendritic network voltage estimation model, l = 2, 3, …, k, where k represents the number of dendritic layers of the white-box dendritic network voltage estimation model, and A k represents the non-linear output of the final layer of the white-box dendritic network voltage estimation model; × represents matrix multiplication operation; represents the Hadamard product operation; W l / (l-1) is the weight parameter of the l-th layer of the white-box dendritic network voltage estimation model; PQ in is the injection power vector of the power system node, PQ in = [P 1,in , Q 1,in , P 2,in , Q 2,in , …, P p,in , Q p,in , …, P n,in , Qn,in , where P p,in , Q p,in are the active power and reactive power injected into node p respectively, p = 2, 3, …, n, and n is the number of nodes in the power system;

[0090] At the moment of fault occurrence, the power of the power electronic equipment remains unchanged. The injection power vector PQ of the power system nodes is determined according to the active power and reactive power output by the power electronic equipment before the fault in , so as to estimate the node voltage after the fault; after the voltage response event occurs, the injection power vector PQ of the power system nodes is determined according to the active and reactive power output by the power electronic equipment after the event in , so as to estimate the node voltage after the voltage response event occurs.

[0091] In specific implementation, the weight parameters of each layer in the white-box dendritic network voltage estimation model are determined as follows:

[0092] Using the injection power vector PQ of the power system nodes in = [P 1,in , Q 1,in , P 2,in , Q 2,in , …, P p,in , Q p,in , …, P n,in , Q n,in and the node voltage U = [U 1 , U 2 , …, U p , …, U n to form a sample pair. U p represents the voltage of node p. Calculate the power system node voltage through forward operation, and then update the weight parameters through the backpropagation algorithm according to the estimated value and the actual value; repeat this process until the weight parameters converge to form the weight parameters of the white-box dendritic network voltage estimation model under the current fault scenario;

[0093] Among them, the forward operation is implemented as follows;

[0094]

[0095] The backpropagation algorithm updates the weight parameters as follows:

[0096]

[0097] In the formula, M is the number of samples used for a single training.

[0098] In specific implementation, in step S5, it is judged whether a voltage response event occurs as follows:

[0099] When the voltage at the access node of any power electronic equipment satisfies the starting voltage range of the voltage response event of this power electronic equipment, it is determined that a voltage response event will occur.

[0100] Identify the occurring voltage response event in the following manner:

[0101] If only the starting voltage range of one voltage response event is satisfied, then this voltage response event is the voltage response event that will occur; if the starting voltage ranges of multiple voltage response events are satisfied, then the voltage response event with the shortest remaining action time is the voltage response event that will occur.

[0102] In specific implementation, the remaining action time of the voltage response event is calculated as follows:

[0103]

[0104] In the formula, τ i represents the remaining action time of voltage response event i; represents the trigger action time or termination action time of voltage response event i, represents the moment when the voltage satisfies the trigger or termination voltage range of voltage response event i, a represents the action time variable of the voltage response event, a = trigger represents the trigger action time, a = end represents the termination action time; t is the current moment.

[0105] In specific implementation, in step S5, the active and reactive powers output by the power electronic equipment after the voltage response event occurs are determined as follows:

[0106]

[0107] In the formula, P p and Q p are respectively the active power and reactive power output by the power electronic equipment at the access node p after the voltage response event occurs; U p represents the voltage of node p; represents the starting voltage range or holding voltage range of voltage response event i, T i x represents the duration during which the voltage at the equipment connection point satisfies the starting voltage range or holding voltage range of voltage response event i, x represents the voltage range variable of the voltage response event, x = start represents the starting voltage range, x = maintain represents the holding voltage range; f i,p (·) and g i,p (·) respectively represent the active power P p output by the power electronic equipment at the access node p after the voltage response event i occurs p and the reactive power Qp The mathematical relationship;

[0108] If the power electronic equipment is disconnected from the grid / blocked, f i,p (·) and g i,p (·) are zero; if the voltage support control and high voltage ride-through control of the power electronic equipment are started, f i,p (·) and g i,p (·) are determined by the control strategy; if the crowbar of the VSC type power electronic equipment is put in and the commutation failure occurs in the LCC type power electronic equipment, f i,p (·) and g i,p (·) are obtained from the state equation of the power electronic equipment or by fitting according to the power-voltage external characteristic curve of the power electronic equipment.

[0109] In the specific implementation, in step S6, according to the active power and reactive power output by the power electronic equipment after the voltage response event occurs, the specific method for re-estimating the node voltage of the power system in this fault scenario after the above voltage response event occurs by using the white-box dendritic network voltage estimation model of this fault scenario is as follows:

[0110]

[0111] Similarly, in the formula, U represents the node voltage of the power system in this fault scenario; A l and A l-1 are the non-linear outputs of the l-th layer and the (l-1)-th layer of the white-box dendritic network voltage estimation model respectively, l = 2, 3,..., k, k represents the number of dendritic layers of the white-box dendritic network voltage estimation model, A k represents the non-linear output of the final layer of the white-box dendritic network voltage estimation model; × represents matrix multiplication operation; represents Hadamard product operation; W l / (l-1) is the weight parameter of the l-th layer of the white-box dendritic network voltage estimation model; PQ is the power vector output by the power electronic equipment after the voltage response event occurs, PQ = [P 1 , Q 1 , P 2 , Q 2 , …, P p , Q p , …, P n , Q n , P p and Q p are the active power and reactive power output by the power electronic equipment connected to node p after the voltage response event occurs respectively, p = 2, 3,..., n, n is the number of nodes in the power system.

[0112] Compared with the prior art, the present invention has the following technical advantages:

[0113] 1. Existing technologies focus on power system cascading failures dominated by overload or topological failures, unable to reflect the secondary response process of power electronic equipment due to voltage disturbances, and not applicable to voltage-dominated cascading failures. The present invention identifies the impact of node voltage changes on power electronic devices and the effect of the control or protection response of power electronic devices on node voltage, and can identify the problems of voltage-dominated weak lines.

[0114] 2. Existing technologies evaluate the lines most likely to trigger large-scale power outages in various scenarios through probabilistic methods such as failure rates and Monte Carlo simulations. The present invention models the dynamic evolution process of cascading failures, depicts the voltage response mode of power electronic equipment, and identifies voltage-dominated weak lines through a combined physical and data-driven approach, with the advantages of high efficiency and accuracy.

[0115] 3. Existing technologies identify the key lines or fault modes leading to system collapse by simulating the dynamic process of equipment after line disconnection. However, the detailed modeling of power electronic power systems is extremely difficult, and voltage-dominated cascading failures are electromagnetic transient processes with a large amount of calculation, making simulation difficult to apply. The present invention constructs a knowledge graph for power system voltage estimation and uses a white-box dendritic network to quickly infer node voltages, enabling rapid identification of weak lines in the most voltage-dominated cascading failures within a short time.

[0116] In a second aspect, the present invention also provides a system for identifying weak lines in voltage-dominated cascading failures of a power system, including a data acquisition module, a display module, a first storage module, a second storage module, a first calculation module, a second calculation module, a third calculation module, a fourth calculation module, a first comparison module, a second comparison module, and a third comparison module;

[0117] The data acquisition module is used to collect the active power, reactive power output by power electronic equipment in the power system, and the voltage of the access node;

[0118] The display module is used to display the identification results of weak lines in cascading failures of the power system;

[0119] The first storage module is used to store the knowledge graph for power system voltage estimation;

[0120] The second storage module is used to store the starting voltage range of voltage response events and the action time of voltage response events;

[0121] The first calculation module estimates the node voltage of the power system based on the active power and reactive power output by the electronic equipment collected by the data acquisition module at the moment of failure, or the active power and reactive power output by the power electronic equipment after the voltage response event calculated by the third calculation module.

[0122] The second calculation module is used to calculate the remaining action time of the voltage response event;

[0123] The third calculation module is used to calculate the active power and reactive power output by the power electronic equipment after the voltage response event occurs;

[0124] The fourth calculation module trains the weight parameters of the white box dendritic network voltage estimation model according to the historical data of the active power, reactive power and access node voltage collected by the data acquisition module, and obtains the white box dendritic network voltage estimation model with determined weight parameters;

[0125] The first comparison module is used to compare whether the node voltage estimation value meets the starting voltage range of the voltage response event in the termination state or the holding voltage range of the voltage response event in the triggering state; if so, call the second comparison module; otherwise, call the first calculation module and the second calculation module to perform recalculation;

[0126] The second comparison module is used to compare whether the remaining action time of the voltage response event is 0; if so, call the third comparison module; otherwise, call the third calculation module to perform recalculation;

[0127] The third comparison module is used to compare and determine whether the occurring voltage response event is the locking or disconnection of the power electronic equipment; if so, determine that the line where the fault point is located in the current fault scenario is the weak line, and call the display module to display the identification result of the weak line of the power system cascading fault; otherwise, call the third calculation module to perform recalculation.

[0128] This power system voltage-dominated cascading fault weak line identification system is designed to execute the power system voltage-dominated cascading fault weak line identification method provided above in the present invention, and has the corresponding technical advantages of the above method of the present invention.

[0129] In a third aspect, the present invention also provides a power system voltage-dominated cascading fault weak line identification device, which is characterized in that it includes a processor and a storage medium; the storage medium is used to store a computer program; the processor is connected to the storage medium and is used to execute the computer program stored in the storage medium, so that the power system voltage-dominated cascading fault weak line identification device executes the power system voltage-dominated cascading fault weak line identification method provided above in the present invention.

[0130] Among them, the storage medium may adopt any combination of one or more computer-readable media. The computer-readable media may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program may be used by or in combination with an instruction execution system, apparatus, or device.

[0131] The code of the computer program for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, ++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network or a wide area network, or may be connected to an external computer.

[0132] Embodiment:

[0133] To verify the effectiveness of the present invention, take the schematic diagram of the power system structure with the access of power electronic equipment as shown in Figure 2 as an example for analysis. In Figure 2 the present embodiment shown, Bus 30, 31, 34, 36, 37 are synchronous machine nodes, with rated powers of 250MW, 520MW, 508MW, 560MW, 540MW respectively; Bus 32, 33, 38 are AC grid-connected nodes of doubly-fed wind farms, with rated powers of 650MW, 632MW, and 830MW respectively; Bus 39 is the access point of a conventional DC transmission system (LCC-HVDC), with a rated power of 1000MW; Bus 35 is the access point of a flexible DC transmission system (VSC-HVDC), with a rated power of 650MW; Bus1, 3, 4, 7~9, 12, 15, 16, 18, 20, 21, 23~29, 39 are load nodes, and the total active load is 6254.23MW, and the total reactive load is 1387Mvar.

[0134] The blocking of the wind farm and VSC-HVDC is determined according to the grid guidelines, that is, when the undervoltage (<0.9 p.u.) or overvoltage (>1.1 p.u.) at the connection point of the wind farm or VSC-HVDC lasts for more than the setting time, and the setting time refers to the Technical Regulations for Connecting Wind Farms to the Power System. LCC-HVDC will be blocked when there are 3 consecutive commutation failures or the low voltage lasts too long. The low voltage threshold is selected as 0.75 p.u., and the maximum allowable value of the low voltage duration is selected as the typical value of 0.5 s.

[0135] In this embodiment, a three-phase solid grounding fault occurs in the power system, the fault transition impedance is 1 Ω, the fault duration is 1 s, the fault location is set incrementally at intervals of 10%, and all lines are traversed. Figure 2 The system shown has 46 lines, 9 fault locations are set for each line, and the fault transition impedance is 1 Ω, resulting in 414 fault scenarios. Under each fault scenario, the power injected into the PEE node is changed, and power and voltage data are collected to form training samples for the white-box dendritic network voltage estimation model, and the training weight parameters of the white-box dendritic network voltage estimation model are obtained. Therefore, a Figure 2 power system voltage estimation knowledge graph can be constructed with the shown grid structure as the first-level entity, 46 second-level entities subordinate to it according to the differences in fault lines, 9 third-level entities subordinate to each second-level entity according to the differences in fault locations, 1 fourth-level entity subordinate to each third-level entity, and each fourth-level entity with the white-box dendritic network voltage estimation model as the attribute.

[0136] In this embodiment, taking the disconnection of the wind farm, the blocking of VSC-HVDC or the blocking of LCC-HVDC as the key event search, there are 29 weak lines for voltage-dominated cascading faults, which are L 1-2 、L 1-39 、L 2-3 、L 2-25 、L 3-4 、L 3-18 、L 4-5 、L 4-14 、L 5-6 、L 5-8 、L 6-7 、L 6-11 、L 7-8 、L 8-9 、L 9-39 、L 10-11 、L 10-13 、L 13-14 、L 14-15 、L 15-16 、L 16-17 、L 16-21 、L 16-24 、L 17-18 、L 17-27 、L 21-22, L 22-23 , L 25-26 , L 28-29 . The key events of weak lines and the resulting cascading faults are shown in Table 1 specifically.

[0137] Table 1 Key events of cascading faults and weak lines

[0138]

[0139] In this embodiment, for Figure 2 all the lines of the power system shown, simulation is carried out, and the events of power electronic equipment tripping off the grid and locking and the corresponding moments caused by line faults are shown in Table 2. The events of power electronic equipment tripping off the grid and locking caused by faults correspond well to the search results shown in Table 1. Therefore, the weak line search method for voltage-dominated cascading faults in an electrified power system proposed in this paper has good accuracy and can effectively search for weak lines of voltage-dominated cascading faults in the power system.

[0140] Table 2 Events of power electronic equipment tripping off the grid and locking and the corresponding occurrence moments

[0141]

[0142] In this embodiment, Figure 3 is the simulation waveform diagram of the grid-connected point voltage U, active power P, and reactive power Q of the power electronic equipment obtained when line L 10-11 has a fault. It can be seen that after the fault lasts for a period of time, the active power and reactive power of LCC-HVDC 39 and wind farm 32 suddenly change and stabilize at 0, indicating that LCC-HVDC 39 locks and wind farm 32 trips off the grid. According to the power waveforms obtained by simulation, it can be judged whether the line fault will cause the power electronic equipment to lock or trip off the grid.

[0143] To sum up, the present invention provides a method, system and device for identifying weak lines of voltage-dominated cascading faults in a power system, which constructs a voltage estimation knowledge graph according to the topological structure, fault historical data and simulation data of the power system; establishes a set of fault scenarios, sequentially selects each fault scenario, determines the corresponding white-box dendritic network model by using the knowledge graph, and calculates the node voltage under this scenario; judges whether a voltage response event occurs according to the calculation result, if the condition is satisfied, identifies the event and judges whether it involves equipment locking or tripping off the grid, so as to determine whether the line is a weak line; if the condition is not satisfied, continue to search for other fault scenarios. This method can accurately identify weak lines in the power system and provide a reliable basis for the design, operation control and optimization of protection measures of the power system.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than limiting them. Those of ordinary skill in the art should understand that any modifications or equivalent replacements made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions shall be covered by the scope of the claims of the present invention.

Claims

1. A method for identifying weak lines in voltage-dominated cascading faults in a power system, characterized in that: The following steps are involved: S1. Establish a set of fault scenarios to be searched for the power system, and construct a knowledge graph for power system voltage estimation based on the power system topology and historical fault data; the historical fault data includes the active power, reactive power output by the power electronic equipment and the voltage of the access node under the short circuit fault of the power system; S2. Select a fault scenario from the set of fault scenarios, and determine a white box dendrite network voltage estimation model for the fault scenario using the power system voltage estimation knowledge graph; S3. Estimate the node voltage of the power system in the fault scenario by using the white box dendrite network voltage estimation model; S4, judging whether a voltage response event will occur according to the estimated power system node voltage; if so, identifying the voltage response event that will occur and executing step S5, otherwise, returning to step S2; S5, judging whether the voltage response event is a power electronic equipment lockout or grid disconnection according to the active power and reactive power output by the power electronic equipment after the voltage response event occurs; if so, executing step S7; Otherwise, execute step S6; S6. Re-estimate the node voltage of the power system in the fault scenario after the voltage response event occurs, based on the active power and reactive power output by the power electronic equipment after the voltage response event occurs, using the white box dendrite network voltage estimation model of the fault scenario, and return to step S4; S7, determining that the line where the fault point in the current fault scenario is located is a weak line; then, determining whether each fault scenario in the fault scenario set has been traversed and searched; if so, returning to step S2 to reselect a fault scenario; otherwise, ending the search; Thus, each fault scenario in the fault scenario set is traversed and searched, the weak line identification operation for each fault scenario is completed, and the cascading fault weak line identification result of the power system is obtained.

2. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 1, characterized in that: In step S1, the fault scenario set is constructed in the following manner: Select any transmission line in the power system, select a position from various preset designated positions on the transmission line to set a short-circuit fault point, the fault condition of the short-circuit fault point is one of three-phase short circuit, two-phase short circuit or single-phase short circuit, and set the fault transition impedance value of the short-circuit fault point within the preset fault transition impedance value range, thereby forming a fault scenario; Different transmission lines selected in the power system, or different locations selected in the preset designated locations on the transmission line to set short-circuit fault points, or different fault conditions set at the short-circuit fault points, or different fault transition impedance values ​​set at the short-circuit fault points, are regarded as forming different fault scenarios respectively; Thus, several different fault scenarios are formed to constitute a fault scenario set.

3. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 2, characterized in that: In step S1, the knowledge graph for power system voltage estimation is constructed in the following way: The initial network architecture of the power system is used as the first-level entity of the knowledge graph; different transmission line faults in the power system form a basic fault grid, and the basic fault grid is used as the second-level entity of the knowledge graph; faults at different locations of each transmission line form a detailed fault grid, and the detailed fault grid is used as the third-level entity of the knowledge graph; different fault transition impedance values ​​at each fault location form specific fault scenarios, and the specific fault scenarios are used as the fourth-level entities of the knowledge graph; the white box dendritic network voltage estimation model under specific fault scenarios is used as the attribute of the fourth-level entity of the knowledge graph; the first and second level entities are related by the fault line, the second and third level entities are related by the fault location, and the third and fourth level entities are related by the fault transition impedance; thus, a knowledge graph for power system voltage estimation is constructed.

4. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 3, characterized in that: In step S2, the white box dendrite network voltage estimation model is determined in the following manner: By using the knowledge graph of power system voltage estimation, the corresponding secondary entity is determined according to the fault line of the fault scenario; the corresponding third-level entity is determined according to the fault location corresponding to the fault scenario; the corresponding fourth-level entity is determined according to the fault transition impedance corresponding to the fault scenario; based on the relationship between the fourth-level entity and the attribute, the white box dendritic network voltage estimation model of the fault scenario is determined.

5. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 1, characterized in that: In step S3, the specific method of estimating the node voltage of the power system in the fault scenario using the white box dendrite network voltage estimation model is as follows: U=A k ; Where U represents the node voltage of the power system in the fault scenario; A l and A l-1 are the nonlinear outputs of the lth and l-1th layers of the white-box dendritic network voltage estimation model, respectively. l=2,3,…,k, where k represents the number of dendritic layers in the white-box dendritic network voltage estimation model. A k represents the nonlinear output of the final layer of the white-box dendritic network voltage estimation model; × represents matrix multiplication operation; represents the Hadamard product operation; W l / (l-1) is the weight parameter of the first layer of the white box dendritic network voltage estimation model; PQ in is the injected power vector of the power system node, PQ in =[P 1,in ,Q 1,in ,P 2,in ,Q 2,in ,…,P p,in ,Q p,in ,…,P n,in ,Q n,in ], where P p,in ,Q p,in are the active power and reactive power injected into node p, respectively, p = 2, 3, ..., n, n is the number of nodes in the power system; The power of the power electronic equipment remains unchanged at the moment of fault. The injected power vector PQ of the power system node is determined according to the active power and reactive power output by the power electronic equipment before the fault. in , thereby estimating the node voltage after the fault; after the voltage response event occurs, the injected power vector PQ of the power system node is determined according to the active and reactive power output by the power electronic equipment after the event occurs in , thereby estimating the node voltage after the voltage response event occurs.

6. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 1, characterized in that: In step S4, the specific method of determining whether a voltage response event will occur is: when the access node voltage of any power electronic equipment meets the starting voltage range of the voltage response event of the power electronic equipment, it is determined that a voltage response event will occur; The specific method of identifying the voltage response event that will occur is: if only one voltage response event has a start voltage range that is satisfied, then the voltage response event is determined to be the voltage response event that will occur; if multiple voltage response events have a start voltage range that is satisfied, then the voltage response event with the shortest remaining action time is determined to be the voltage response event that will occur; The remaining action time of the voltage response event is calculated as follows: In the formula, τ i represents the remaining action time of voltage response event i; Indicates the trigger action time or termination action time of voltage response event i, It indicates the moment when the voltage meets the trigger or termination voltage range of voltage response event i. a indicates the action time variable of the voltage response event. When a=trigger, it indicates the trigger action time. When a=end, it indicates the termination action time. t is the current moment.

7. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 1, characterized in that: In step S5, the active and reactive power output by the power electronic equipment after the voltage response event occurs is determined as follows: Where P p and Q p are respectively the active power and reactive power output by the power electronic equipment connected to the node p after the voltage response event occurs; U p represents the voltage at node p; Indicates the start voltage range or holding voltage range of voltage response event i, T i x Indicates the duration of the device grid-connected point voltage meeting the start voltage range or the maintenance voltage range of the voltage response event i, x represents the voltage range variable of the voltage response event, x=start represents the start voltage range, and x=maintain represents the maintenance voltage range; represents the trigger action time or end action time of voltage response event i, a represents the action time variable of voltage response event, when a=trigger, it represents the trigger action time, when a=end, it represents the end action time; f i,p (·) and g i,p (·) respectively represent the active power P output of the power electronic equipment connected to node p after voltage response event i occurs p and reactive power Q p Each of them is related to the node p voltage U p The mathematical relationship between If the power electronic equipment is disconnected / locked, f i,p (·) and g i,p (·) is zero; if the voltage support control and high voltage ride through control of power electronic equipment are started, f i,p (·) and g i,p (·) Determined by the control strategy; if the VSC power electronic equipment crowbar is put into operation and the LCC power electronic equipment fails to commutate, f i,p (·) and g i,p (·) Obtained from the state equation of the power electronic equipment or by fitting the power-voltage external characteristic curve of the power electronic equipment.

8. The method for identifying weak lines in voltage-dominated cascading faults in a power system according to claim 1, characterized in that: In step S6, according to the active power and reactive power output by the power electronic equipment after the voltage response event occurs, the specific method of re-estimating the node voltage of the power system in the fault scenario after the voltage response event occurs by using the white box dendrite network voltage estimation model of the fault scenario is as follows: U=A k ; Where U represents the node voltage of the power system in the fault scenario; A l and A l-1 are the nonlinear outputs of the lth and l-1th layers of the white-box dendritic network voltage estimation model, respectively. l=2,3,…,k, where k represents the number of dendritic layers in the white-box dendritic network voltage estimation model. A k represents the nonlinear output of the final layer of the white-box dendritic network voltage estimation model; × represents matrix multiplication operation; represents the Hadamard product operation; W l / (l-1) is the weight parameter of the first layer of the white box dendrite network voltage estimation model; PQ is the power vector output by the power electronic equipment after the voltage response event occurs, PQ=[P1,Q1,P2,Q2,…,P p ,Q p ,…,P n ,Q n ], P p and Q p are the active power and reactive power output by the power electronic equipment connected to node p after the voltage response event occurs, p = 2, 3, ..., n, and n is the number of nodes in the power system.

9. A power system voltage-dominant cascading fault weak line identification system, characterized in that: It includes a data acquisition module, a display module, a first storage module, a second storage module, a first calculation module, a second calculation module, a third calculation module, a fourth calculation module, a first comparison module, a second comparison module and a third comparison module; The data acquisition module is used to collect the active power, reactive power output by the power electronic equipment of the power system and the voltage of the access node; The display module is used to display the identification result of the weak line of the cascading fault of the power system; The first storage module is used to store the knowledge graph of power system voltage estimation; The second storage module is used to store the start voltage range of the voltage response event and the action time of the voltage response event; The first calculation module estimates and calculates the node voltage of the power system based on the active power and reactive power output by the electronic equipment collected by the data collection module at the moment of the fault, or the active power and reactive power output by the power electronic equipment after the voltage response event occurs calculated by the third calculation module; The second calculation module is used to calculate the remaining action time of the voltage response event; The third calculation module is used to calculate the active power and reactive power output by the power electronic equipment after the voltage response event occurs; The fourth calculation module trains the weight parameters of the white box dendritic network voltage estimation model according to the historical data of active power, reactive power and access node voltage collected by the data collection module, and obtains the white box dendritic network voltage estimation model with determined weight parameters; The first comparison module is used to compare whether the node voltage estimation value meets the start voltage range of the voltage response event in the termination state or the hold voltage range of the voltage response event in the trigger state; if so, calling the second comparison module; Otherwise, calling the first calculation module and the second calculation module to recalculate; The second comparison module is used to compare whether the remaining action time of the voltage response event is 0; if so, call the third comparison module; otherwise, call the third calculation module to recalculate; The third comparison module is used to compare and determine whether the voltage response event is a power electronic equipment lockout or disconnection; if so, determine that the line where the fault point in the current fault scenario is a weak line, and call the display module to display the weak line identification result of the cascading fault of the power system; Otherwise, the third calculation module is called to recalculate.

10. A device for identifying weak lines in voltage-dominated cascading faults in a power system, characterized in that: It comprises a processor and a storage medium; the storage medium is used to store a computer program; the processor is connected to the storage medium and is used to execute the computer program stored in the storage medium, so that the power system voltage-dominant cascading failure weak line identification device executes the power system voltage-dominant cascading failure weak line identification method as described in any one of claims 1 to 8.

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