A method and system for online assessment of broadband oscillation risk in new energy power systems

Abstract

The present invention provides a method and system for online assessment of broadband oscillation risk of a new energy power system, including: S100, adopting a neural network-based new energy power generation system impedance model identification method, offline training broadband impedance or admittance identification models of key equipment; S200, online acquisition of port voltage and current data of each key equipment, input into the corresponding broadband impedance or admittance identification model, online acquisition of real-time broadband impedance or admittance data of each key equipment; and other steps. The present invention can perform real-time online assessment and weak point location of broadband oscillation risk of a new energy power system only by online measurement of port voltage and current data of key electrical equipment; it does not need to obtain the control structure and parameters of the equipment, and is applicable to "black / gray box" systems; it does not need to inject disturbances into the system, and has no effect on the normal operation of the system; the method has high accuracy, applicability and practicality, and has high engineering application value.

Description

Technical Field

[0001] The present invention relates to the field of new energy power systems, and in particular to a method and system for online assessment of broadband oscillation risk in new energy power systems. Background Art

[0002] With the massive access to high proportions of new energy and power electronic equipment, the oscillation stability problem of new power systems, which is different from traditional power systems, has become prominent, bringing challenges to the stable operation of power systems. Frequency domain impedance stability analysis method is the mainstream method for studying the oscillation stability problem of new energy power systems, but the theoretical impedance model established offline has bottlenecks when applied to actual complex systems, making it difficult to directly guide engineering practice, so that actual new energy systems still face a high risk of oscillation instability during operation.

[0003] For new energy units with prominent "black / gray box" problems, existing studies use impedance measurement methods to obtain their impedance characteristics for online oscillation stability assessment, which is time-consuming and difficult to implement; when using curve fitting methods to obtain impedance transfer functions based on the s domain for online oscillation stability assessment, there are limitations such as difficulty in determining the order and unclear physical meaning. The method combined with artificial intelligence is helpful for online evaluation of the oscillation stability of new energy power systems, but existing studies directly identify the oscillation stability margin of the entire system through the system operation mode, which has extremely high requirements for computing resources and the accuracy of neural networks, and is only applicable to specific systems, with poor applicability and practicality; some studies also use parameter identification methods to establish a real-time state space model of the system for oscillation stability research, which has the problem of too high order and difficulty in accurately analyzing stability. In addition, existing online oscillation stability research based on broadband impedance identification is usually based on the similarity assumption, and highly simplifies the new energy grid-connected system into a simple interconnected system of "single aggregated new energy unit-AC / DC power grid", which is quite different from the actual complex system and cannot be used to accurately evaluate the oscillation stability of the actual new energy grid-connected system.

[0004] In the process of further exploration, the application number 202210289911.5 of "Impedance model identification method and system of new energy power generation system based on neural network" was proposed; the application number 202210991337.8 of "Data and knowledge jointly driven new energy station impedance identification method and system" was proposed. In this way, the broadband impedance of each new energy unit and the real-time broadband impedance of station-level systems such as new energy stations and energy storage power stations can be obtained online. However, the broadband oscillation stability of large-scale new energy power systems was not further explored. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a method and system for online assessment of broadband oscillation risk in a new energy power system.

[0006] According to one aspect of the present invention, there is provided a method for online assessment of broadband oscillation risk of a new energy power system, comprising:

[0007] S100, adopting a neural network-based impedance model identification method for a new energy power generation system, offline training a broadband impedance or admittance identification model of key equipment; the key equipment includes a new energy unit, an energy storage unit, a flexible direct current converter, and a synchronous generator;

[0008] S200, online acquisition of port voltage and current data of each key device, input into the corresponding broadband impedance or admittance identification model, and online acquisition of real-time broadband impedance or admittance data of each key device;

[0009] S300, based on the real-time broadband impedance or admittance data of key equipment, adopts a new energy station impedance identification method driven by data and knowledge, and obtains the real-time broadband impedance or admittance data of each new energy station and energy storage power station online;

[0010] S400, determining a high proportion of power electronic equipment access points as oscillation risk nodes of the system, and numbering all oscillation risk nodes as 1, 2, ..., n; the high proportion of power electronic equipment includes a new energy station, a new energy cluster consisting of a plurality of adjacent new energy stations, a flexible direct current converter station, and an energy storage power station;

[0011] S500, for the oscillation risk node i, dividing the new energy power system into a new energy subsystem and a remaining subsystem at the node, and calculating the real-time broadband impedance or admittance of the two subsystems;

[0012] S600, online evaluation of the potential oscillation frequency and stability margin of the oscillation risk node i using a frequency domain stability criterion;

[0013] S700, judging whether the stability margin is lower than a set threshold value. If the stability margin of the oscillation risk node i is lower than the set threshold value, calculating the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, comparing the size of the real part of the impedance, and ranking the top k stations in the order of the real part of the impedance from small to large as the oscillation weak point, and outputting the potential oscillation frequency, stability margin and oscillation weak point;

[0014] S800, for any value of i among 1, 2, ..., n, repeat steps S500-S700;

[0015] S900, return to S200, and perform a rolling online oscillation risk assessment on the system.

[0016] Preferably, S100, adopting a neural network-based impedance model identification method for a new energy power generation system, offline training a broadband impedance or admittance identification model of key equipment, including:

[0017] Obtain simulation models or impedance data of each key device and build a training data set;

[0018] The impedance model identification method of renewable energy power generation system based on neural network is adopted to train the broadband impedance identification model of key equipment offline.

[0019] Preferably, S200, online acquisition of port voltage and current data of each key device, inputting the data into a corresponding broadband impedance or admittance identification model, and online acquisition of real-time broadband impedance or admittance data of each key device, includes:

[0020] Measure the voltage and current data of each key equipment port in each station to obtain the operating status of each key equipment;

[0021] The voltage and current data of each key device port are input into the broadband impedance or admittance identification model of the corresponding key device to obtain the real-time broadband impedance or admittance data of each key device in its own reference coordinate system.

[0022] Preferably, S300 adopts a new energy station impedance identification method jointly driven by data and knowledge to obtain online real-time broadband impedance or admittance data of each new energy station and energy storage power station, including:

[0023] Adopting the impedance identification method of new energy stations driven by data and knowledge, the real-time broadband impedance or admittance data of each key device in its own reference coordinate system is converted to the global reference coordinate system;

[0024] Combined with the internal topology and connection line parameters of the new energy station, an impedance network model of the new energy station is established. Through impedance network calculation, the real-time broadband impedance or admittance of the new energy station is obtained;

[0025] Combined with the internal topology and connection line parameters of the energy storage power station, the real-time broadband impedance or admittance of the energy storage power station is obtained through impedance network calculation.

[0026] Preferably, S500, for the oscillation risk node i, divides the system into a new energy subsystem and a remaining subsystem at the node, and calculates the real-time broadband impedance or admittance of the two subsystems, including:

[0027] For the oscillation risk node i, the entire new energy power system is divided into a new energy subsystem and a remaining subsystem at the node;

[0028] Among them, the new energy subsystem is a new energy cluster, a new energy station, a DC converter station or an energy storage station;

[0029] Combining the topology and connection line parameters of the new energy subsystem and the remaining subsystem, the real-time broadband impedance or admittance of the new energy subsystem and the remaining subsystem is obtained respectively by impedance network calculation.

[0030] Preferably, S600, online evaluation of the potential oscillation frequency and stability margin of the oscillation risk node i by using a frequency domain stability criterion includes:

[0031] According to the real-time broadband impedance Z of the new energy subsystem and the remaining subsystems N and Z R or admittance, and obtain the impedance and Z of the new energy subsystem and the remaining subsystem N +Z R The Nyquist curve of

[0032] If the Nyquist curve surrounds the (0,0) point, the system is unstable, otherwise, the system is stable;

[0033] Get the impedance ratio Z of the new energy subsystem and the remaining subsystem N / Z R Nyquist curve, determine the shortest distance γ from the Nyquist curve to (-1,0) min ;

[0034] For the intersection point i of the impedance ratio Nyquist curve and the unit circle, i = 1, 2, ..., s, s is the number of intersection points, calculate the distance γ from the intersection point i to (-1, 0) i ;

[0035] Will be with γ min Closest γ i The frequency of the corresponding intersection point is determined as the potential oscillation frequency f of the system i , the system stability margin PM(f i )=2arcsin(γ i / 2).

[0036] Preferably, S700 determines whether the stability margin is lower than a set threshold value. If the stability margin of the oscillation risk node i is lower than the set threshold value, the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency is calculated, and the size of the real part of the impedance is compared. The station with a smaller real part of the impedance is an oscillation weak point, and the potential oscillation frequency, stability margin and oscillation weak point information are output, including:

[0037] Set the threshold of system stability margin PM to θ th ; Determine the calculated system stability margin PM(f i ) is lower than the set threshold θ th ;

[0038] If the stability margin PM(f i) is lower than the set threshold θ th , calculate the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, compare the size of the real part of the impedance, and define the first k stations with the largest real part of the impedance as the oscillation weak point. Output the potential oscillation frequency, stability margin and oscillation weak point information of the oscillation risk node i.

[0039] According to a second aspect of the present invention, there is provided a new energy power system broadband oscillation risk online assessment system, comprising:

[0040] The key equipment broadband impedance identification module adopts the impedance model identification method of the new energy power generation system based on neural network to train the broadband impedance or admittance identification model of the key equipment offline; collects the port voltage and current data of each key equipment online, inputs them into the corresponding broadband impedance or admittance identification model, and obtains the real-time broadband impedance or admittance data of each key equipment online;

[0041] New energy station broadband impedance identification module, which uses a data and knowledge-driven new energy station impedance identification method to obtain broadband impedance or admittance of new energy stations and energy storage power stations online;

[0042] A broadband impedance acquisition module for the new energy subsystem and the remaining subsystem, which uses an impedance network aggregation method to obtain the broadband impedance or admittance of the new energy subsystem and the remaining subsystem online;

[0043] A stability margin evaluation module, which evaluates the potential oscillation frequency and stability margin of the system online using a frequency domain stability criterion based on the broadband impedance or admittance of the new energy subsystem and the remaining subsystems;

[0044] A weak point location module, which, when the system stability margin is lower than a set threshold, calculates the real part of the impedance of each station in the new energy subsystem at the potential oscillation frequency, compares the size of the real part of the impedance, and determines the station with the smaller real part of the impedance as the weak point;

[0045] Stability information output module, which outputs the potential oscillation frequency, stability margin and weak points of the new energy power system in real time;

[0046] The timing module sets the interval time and repeatedly starts the broadband impedance identification module of key equipment, the broadband impedance identification module of new energy stations, the broadband impedance acquisition module of new energy subsystems and remaining subsystems, the stability margin assessment module, the weak point location module and the stability information output module to perform a rolling online assessment of the system's broadband oscillation risk.

[0047] According to a third aspect of the present invention, there is provided a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute the method described, or to run the system described, when executing the program.

[0048] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute the method described, or to run the system described.

[0049] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:

[0050] The online assessment method and system for broadband oscillation risk of new energy power system in the embodiment of the present invention can conduct real-time online assessment and weak point location of broadband oscillation risk of new energy power system by only measuring the port voltage and current data of key electrical equipment such as new energy units online; it is not necessary to obtain the control structure and parameters of the equipment, and is applicable to "black / gray box" systems; it is not necessary to inject disturbances into the system, and it has no effect on the normal operation of the system; the stability assessment idea driven by data-knowledge fusion can greatly reduce the strong dependence on the data sample size and improve the interpretability of stability assessment; the model order used in online oscillation stability analysis is low, the amount of calculation is small, and it is easy to apply online. This method has high accuracy, applicability and practicality, and has high engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0052] Figure 1 It is a schematic diagram of the composition structure of the new energy power system in the present invention.

[0053] In the figure, 1-AC power grid, 2-flexible DC system, 3-thermal power plant, 4-grid-type wind farm, 5-photovoltaic power station, 6-energy storage power station, 7-grid-type wind farm, 8-550KV-substation, 9-220KV substation;

[0054] Figure 2 The figure is a flowchart of an online assessment method for broadband oscillation risk of a new energy power system in one embodiment of the present invention.

[0055] Figure 3 The figure is a schematic diagram of the components of an online assessment system for broadband oscillation risk of a new energy power system in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0057] In order to further understand the technical solution of the present invention, the following introduction is made to the mentioned nouns:

[0058] New energy power system: Figure 1 As shown, it consists of new energy stations, thermal power plants, hydropower plants, nuclear power plants, energy storage power stations, flexible direct current systems, loads, transmission lines, transformers, etc.

[0059] Key equipment: including wind power, photovoltaic and other new energy units, synchronous generators, energy storage units, flexible direct current converters, etc.;

[0060] New energy stations are composed of new energy units such as wind power or photovoltaic power, reactive power compensation devices, energy storage systems, transformers, and collection lines;

[0061] Energy storage power stations are mainly composed of energy storage units, including energy storage batteries, PCS converters, transformers, lines, etc.

[0062] A high proportion of power electronic equipment refers to a station or cluster containing a large number of power electronic equipment, mainly including new energy stations, new energy clusters composed of multiple adjacent new energy stations, energy storage power stations or flexible direct current converter stations.

[0063] like Figure 2 As shown, the present invention provides an embodiment, a method for online assessment of broadband oscillation risk of a new energy power system, which specifically includes the following steps:

[0064] S100 uses a neural network-based impedance model identification method for renewable energy power generation systems to offline train broadband impedance or admittance identification models for key equipment, including renewable energy units, energy storage units, flexible direct current converters, and synchronous generators.

[0065] S200, online acquisition of port voltage and current data of each key device, input into the corresponding broadband impedance or admittance identification model, and online acquisition of real-time broadband impedance or admittance data of each key device;

[0066] S300, based on the real-time broadband impedance or admittance data of key equipment, adopts a new energy station impedance identification method driven by data and knowledge, and obtains the real-time broadband impedance or admittance data of each new energy station and energy storage power station online;

[0067] S400, determining a high-proportion power electronic equipment access point as an oscillation risk node of the system, and numbering all oscillation risk nodes as 1, 2, ..., n; the high-proportion power electronic equipment includes a new energy station, a new energy cluster consisting of multiple adjacent new energy stations, a flexible direct current converter station, and an energy storage power station;

[0068] S500, for the oscillation risk node i, dividing the new energy power system into a new energy subsystem and a remaining subsystem at the node, and calculating the real-time broadband impedance or admittance of the two subsystems;

[0069] S600, online evaluating the potential oscillation frequency and stability margin of the oscillation risk node i by using a frequency domain stability criterion;

[0070] S700, judging whether the stability margin is lower than a set threshold value. If the stability margin of the oscillation risk node i is lower than the set threshold value, calculating the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, comparing the size of the real part of the impedance, and ranking the top k stations in the order of the real part of the impedance from small to large as the oscillation weak point, and outputting the potential oscillation frequency, stability margin and oscillation weak point;

[0071] S800, for any value of i among 1, 2, ..., n, repeat steps S500-S700;

[0072] S900, return to S200, and perform a rolling online oscillation risk assessment on the system.

[0073] In the above embodiment, based on S100 to S300, S400 determines the access points of power electronic equipment with a high proportion in the new energy power system as the oscillation risk nodes of the system. Compared with the existing method of directly performing broadband stability assessment on the new energy power system, it can reduce the amount of calculation and improve the efficiency of online stability assessment.

[0074] On the basis of S100-S500, by obtaining the real-time broadband impedance or admittance of the new energy subsystem and the remaining subsystems online, S600 and S700 can be used to accurately obtain the oscillation stability margin, potential oscillation frequency and oscillation weak points of each oscillation risk node online. Compared with the existing broadband oscillation stability assessment method, the present invention does not need to obtain the detailed control structure and parameters of power electronic equipment such as new energy units, is suitable for "black / gray box" problems, and can accurately evaluate the broadband oscillation stability of the system online, and has high engineering application value.

[0075] In a preferred embodiment of the present invention, S100 is implemented, and a neural network-based new energy power generation system impedance model identification method is used to offline train broadband impedance or admittance identification models of key equipment such as new energy units, energy storage units, flexible direct current converters, and synchronous generators, specifically including the following steps:

[0076] S101, constructing a training data set using the "black box" simulation model or impedance data of each key device provided by the device manufacturer;

[0077] S102, adopts the impedance model identification method of new energy power generation system based on neural network, and offline trains the broadband impedance identification model of key equipment such as new energy units, energy storage units, flexible DC converters, synchronous generators, etc.

[0078] In a preferred embodiment of the present invention, S200 is implemented, and the port voltage and current data of each key device such as each new energy generator set, energy storage unit, flexible DC converter, synchronous generator, etc. are collected online and input into the corresponding broadband impedance or admittance identification model to obtain the real-time broadband impedance or admittance data of each key device online, which specifically includes the following steps:

[0079] S201, measure the voltage and current data of each device port in each station (new energy station, flexible direct current converter station, conventional power plant (thermal power plant, hydropower plant, nuclear power plant), energy storage power station, etc.) to obtain the operating status of each key equipment;

[0080] S202, input the voltage and current data of each key device port into the corresponding neural network impedance identification model (the broadband impedance or admittance identification model of the key device) to obtain the real-time broadband impedance or admittance data of each key device in its own reference coordinate system.

[0081] In a preferred embodiment of the present invention, S300 is implemented, and a new energy station impedance identification method driven by data and knowledge is adopted to obtain online real-time broadband impedance or admittance data of each new energy station and energy storage power station, which specifically includes the following steps:

[0082] S301, adopts the impedance identification method and system of new energy stations driven by data and knowledge, converts the real-time broadband impedance or admittance data of each key device of S200 in its own reference coordinate system to the global reference coordinate system;

[0083] S302, combining the internal topology and connection line parameters of the new energy station, and obtaining the real-time broadband impedance or admittance of the new energy station through impedance network calculation (series-parallel operation of impedance);

[0084] S303, combining the internal topology and connection line parameters of the energy storage power station, and obtaining the real-time broadband impedance or admittance of the energy storage power station through impedance network calculation.

[0085] In a preferred embodiment of the present invention, S400 is implemented, for the oscillation risk node i, the system is divided into a new energy subsystem and a remaining subsystem at the node, and the real-time broadband impedance or admittance of the two subsystems is calculated, which specifically includes the following steps:

[0086] S401, the new energy power system includes the new energy system and the traditional power network. The traditional power network includes AC power grid, synchronous machine, transmission line, transformer, load, etc. Before the access of new energy, the risk of oscillation instability of the traditional power network is relatively small. With the access of new energy, the system gradually generates the risk of oscillation instability. Therefore, the new energy cluster, new energy station, flexible direct current converter station, energy storage station and other high-proportion power electronic equipment access points in the new energy power system are identified as the system's oscillation risk nodes, and all oscillation risk nodes are numbered 1, 2, ..., n.

[0087] S402, for the oscillation risk node i, the entire new energy power system is divided into a new energy subsystem and a remaining subsystem at the node (at a certain oscillation risk node i, the new energy subsystem refers to the new energy cluster, new energy site, DC converter station or energy storage power station connected to the oscillation risk node, and all other parts are remaining subsystems, including the new energy cluster, new energy site, line, transformer, thermal power plant, hydropower plant, energy storage power station, flexible direct current, etc. of other oscillation risk nodes);

[0088] S403, the new energy subsystem may be a new energy cluster, a new energy station, a DC converter station or an energy storage power station.

[0089] S404, combining the topology and connection line parameters of the new energy subsystem and the remaining subsystem, and using impedance network calculation to obtain the real-time broadband impedance or admittance of the new energy subsystem and the remaining subsystem.

[0090] In a preferred embodiment of the present invention, S500 is implemented to evaluate the potential oscillation frequency and stability margin of the oscillation risk node i online using the frequency domain stability criterion, which specifically includes the following steps:

[0091] S501, based on the real-time broadband impedance (Z) of the new energy subsystem and the remaining subsystems obtained in S404 N and Z R ) or admittance, and the impedance and (Z N +Z R ), if the Nyquist curve surrounds (0,0), the system is unstable, otherwise, the system is stable.

[0092] S502, further, obtaining the impedance ratio (Z N / Z R ) and determine the shortest distance γ from the Nyquist curve to (-1,0) min ;

[0093] S503, for the intersection point i (i=1, 2, ..., s, s is the number of intersection points) between the impedance ratio Nyquist curve and the unit circle, calculate the distance γ from the intersection point i to (-1, 0) i ; will be with γ min Closest γ i The frequency of the corresponding intersection point is determined as the potential oscillation frequency f of the system i , the system stability margin PM(f i )=2arcsin(γ i / 2).

[0094] In a preferred embodiment of the present invention, S600 is implemented to determine whether the stability margin is lower than a set threshold. If the stability margin of the oscillation risk node i is lower than the set threshold, the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency is calculated, and the size of the real part of the impedance is compared. The station with a smaller real part of the impedance is an oscillation weak point, and the potential oscillation frequency, stability margin and oscillation weak point information are output, which specifically includes the following steps:

[0095] S601, setting the threshold of the system stability margin PM to θ th (generally 15 to 45 degrees), according to the S500 stability margin PM (f i ), determine the system stability margin PM(f i ) is lower than the set threshold θ th .

[0096] S602, if the stability margin PM (f i ) is lower than the set threshold θ th , calculate the real part of the impedance of each renewable energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, compare the size of the real part of the impedance, and define the station with a smaller real part as the oscillation weak point. Output the potential oscillation frequency, stability margin and oscillation weak point information of the oscillation risk node i.

[0097] In a preferred embodiment of the present invention, S700 is implemented, and steps S500 to S700 are repeated for any value of i among 1, 2, ..., n, which specifically includes the following steps:

[0098] S701, for any value of i among 1, 2, ..., n, repeat steps S500 to S700.

[0099] In a preferred embodiment of the present invention, S800 is implemented, and the process returns to S200, and a rolling online oscillation risk assessment is performed on the system at intervals of 15 minutes, specifically including the following steps:

[0100] S801: If the oscillation stability risk assessment of all oscillation risk nodes is completed, the potential oscillation frequency, stability margin and oscillation weak point information of all oscillation risk nodes are output, and the process returns to S200 to perform rolling online oscillation risk assessment on the system at intervals of 15 minutes.

[0101] Based on the same technical concept, in another embodiment of the present invention, a new energy power system broadband oscillation risk online assessment system is provided, such as Figure 3 As shown, including:

[0102] The key equipment broadband impedance identification module adopts the impedance model identification method of the new energy power generation system based on neural network to train the broadband impedance or admittance identification model of the key equipment offline; collects the port voltage and current data of each key equipment online, inputs them into the corresponding broadband impedance or admittance identification model, and obtains the real-time broadband impedance or admittance data of each key equipment online;

[0103] New energy station broadband impedance identification module, which uses a data and knowledge-driven new energy station impedance identification method to obtain broadband impedance or admittance of new energy stations and energy storage power stations online;

[0104] A broadband impedance acquisition module for the new energy subsystem and the remaining subsystem, which uses an impedance network aggregation method to obtain the broadband impedance or admittance of the new energy subsystem and the remaining subsystem online;

[0105] The stability margin assessment module, which uses the frequency domain stability criterion to online assess the potential oscillation frequency and stability margin of the system based on the broadband impedance or admittance of the new energy subsystem and the remaining subsystem;

[0106] Weak point location module, when the system stability margin is lower than the set threshold, this module calculates the real part of the impedance of each station in the new energy subsystem at the potential oscillation frequency, compares the size of the real part of the impedance, and determines the station with the smaller real part of the impedance as the weak point;

[0107] Stability information output module, which outputs the potential oscillation frequency, stability margin and weak points of the new energy power system in real time;

[0108] The timing module sets the interval time and repeatedly starts the broadband impedance identification module of key equipment, the broadband impedance identification module of new energy stations, the broadband impedance acquisition module of new energy subsystems and remaining subsystems, the stability margin assessment module, the weak point location module and the stability information output module to perform a rolling online assessment of the system's broadband oscillation risk.

[0109] The modules / units in the above examples of the present invention may specifically refer to the implementation techniques of the corresponding steps of the impedance model identification method of a new energy power generation system based on a neural network in the above embodiment, which will not be described in detail here.

[0110] Based on the same inventive concept, in other embodiments of the present invention, a terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute the method described, or to run the system described, when executing the program.

[0111] Based on the same inventive concept, in other embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, it can be used to execute the method described, or to run the system described.

[0112] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A method for online assessment of broadband oscillation risk in a new energy power system, characterized in that: include: S100, adopting a neural network-based impedance model identification method for a new energy power generation system, offline training a broadband impedance or admittance identification model of key equipment; the key equipment includes a new energy unit, an energy storage unit, a flexible direct current converter, and a synchronous generator; S200, online acquisition of port voltage and current data of each key device, input into the corresponding broadband impedance or admittance identification model, and online acquisition of real-time broadband impedance or admittance data of each key device; S300, based on the real-time broadband impedance or admittance data of key equipment, adopts a new energy station impedance identification method driven by data and knowledge, and obtains the real-time broadband impedance or admittance data of each new energy station and energy storage power station online; S400, determining a high proportion of power electronic equipment access points as oscillation risk nodes of the system, and numbering all oscillation risk nodes as 1, 2, ..., n; the high proportion of power electronic equipment includes a new energy station, a new energy cluster consisting of a plurality of adjacent new energy stations, a flexible direct current converter station, and an energy storage power station; S500, for the oscillation risk node i, dividing the new energy power system into a new energy subsystem and a remaining subsystem at the node, and calculating the real-time broadband impedance or admittance of the two subsystems; S600, online evaluation of the potential oscillation frequency and stability margin of the oscillation risk node i using a frequency domain stability criterion; S700, judging whether the stability margin is lower than a set threshold value. If the stability margin of the oscillation risk node i is lower than the set threshold value, calculating the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, comparing the size of the real part of the impedance, and ranking the top k stations in the order of the real part of the impedance from small to large as the oscillation weak point, and outputting the potential oscillation frequency, stability margin and oscillation weak point; S800, for any value of i among 1, 2, ..., n, repeat steps S500-S700; S900, return to S200, and perform rolling online oscillation risk assessment on the system; In S500, for the oscillation risk node i, the system is divided into a new energy subsystem and a remaining subsystem at the node, and the real-time broadband impedance or admittance of the two subsystems is calculated, including: For the oscillation risk node i, the entire new energy power system is divided into a new energy subsystem and a remaining subsystem at the node; Among them, the new energy subsystem is a new energy cluster, a new energy station, a DC converter station or an energy storage station; Combined with the topology and connection line parameters of the new energy subsystem and the remaining subsystem, the real-time broadband impedance or admittance of the new energy subsystem and the remaining subsystem is obtained by impedance network calculation. In S600, the potential oscillation frequency and stability margin of the oscillation risk node i are evaluated online using the frequency domain stability criterion, including: According to the real-time broadband impedance Z of the new energy subsystem and the remaining subsystems N and Z R or admittance, and obtain the impedance and Z of the new energy subsystem and the remaining subsystem N +Z R The Nyquist curve of If the Nyquist curve surrounds the (0,0) point, the system is unstable, otherwise, the system is stable; Get the impedance ratio Z of the new energy subsystem and the remaining subsystem N / Z R Nyquist curve, determine the shortest distance γ from the Nyquist curve to (-1,0) min ; For the intersection point i of the impedance ratio Nyquist curve and the unit circle, i = 1, 2, ..., s, s is the number of intersection points, calculate the distance γ from the intersection point i to (-1, 0) i ; Will be with γ min Closest γ i The frequency of the corresponding intersection point is determined as the potential oscillation frequency f of the system i , the stability margin PM(f i )=2arcsin(γ i / 2).

2. According to claim 1, a new energy power system broadband oscillation risk online assessment method is characterized in that: S100 uses a neural network-based impedance model identification method for renewable energy power generation systems to offline train broadband impedance or admittance identification models for key equipment, including: Obtain simulation models or impedance data of each key device and build a training data set; The impedance model identification method of renewable energy power generation system based on neural network is adopted to train the broadband impedance identification model of key equipment offline.

3. According to claim 1, a new energy power system broadband oscillation risk online assessment method is characterized in that: S200 collects the port voltage and current data of each key device online, inputs it into the corresponding broadband impedance or admittance identification model, and obtains the real-time broadband impedance or admittance data of each key device online, including: Measure the voltage and current data of each key equipment port in each station to obtain the operating status of each key equipment; The voltage and current data of each key device port are input into the broadband impedance or admittance identification model of the corresponding key device to obtain the real-time broadband impedance or admittance data of each key device in its own reference coordinate system.

4. According to claim 1, a new energy power system broadband oscillation risk online assessment method is characterized in that: S300 uses a data and knowledge-driven impedance identification method for new energy stations to obtain real-time broadband impedance or admittance data of each new energy station and energy storage power station online, including: Adopting the impedance identification method of new energy stations driven by data and knowledge, the real-time broadband impedance or admittance data of each key device in its own reference coordinate system is converted to the global reference coordinate system; Combined with the internal topology and connection line parameters of the new energy station, an impedance network model of the new energy station is established. Through impedance network calculation, the real-time broadband impedance or admittance of the new energy station is obtained; Combined with the internal topology and connection line parameters of the energy storage power station, the real-time broadband impedance or admittance of the energy storage power station is obtained through impedance network calculation.

5. The method for online assessment of broadband oscillation risk of a new energy power system according to claim 1 is characterized in that: S700, judging whether the stability margin is lower than a set threshold value. If the stability margin of the oscillation risk node i is lower than the set threshold value, calculating the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, comparing the size of the real part of the impedance, the station with a smaller real part of the impedance is the oscillation weak point, and outputting the potential oscillation frequency, stability margin and oscillation weak point information, including: Set the threshold of system stability margin PM to θ th ; Determine the calculated system stability margin PM(f i ) is lower than the set threshold θ th； If the stability margin PM(f i ) is lower than the set threshold θ th , calculate the real part of the impedance of each new energy station, flexible DC converter station and energy storage station at the potential oscillation frequency, compare the size of the real part of the impedance, and define the first k stations with the largest real part of the impedance as the oscillation weak point. Output the potential oscillation frequency, stability margin and oscillation weak point information of the oscillation risk node i.

6. An online assessment system for broadband oscillation risk of new energy power system, characterized in that: include: The key equipment broadband impedance identification module uses a neural network-based new energy power generation system impedance model identification method to train the broadband impedance or admittance identification model of key equipment offline; Collect the port voltage and current data of each key device online, input them into the corresponding broadband impedance or admittance identification model, and obtain the real-time broadband impedance or admittance data of each key device online; New energy station broadband impedance identification module, which uses a data and knowledge-driven new energy station impedance identification method to obtain broadband impedance or admittance of new energy stations and energy storage power stations online; The broadband impedance acquisition module of the new energy subsystem and the remaining subsystem uses the impedance network aggregation method to obtain the broadband impedance or admittance of the new energy subsystem and the remaining subsystem online, including: For the oscillation risk node i, the entire new energy power system is divided into a new energy subsystem and a remaining subsystem at the node; Among them, the new energy subsystem is a new energy cluster, a new energy station, a DC converter station or an energy storage station; Combined with the topology and connection line parameters of the new energy subsystem and the remaining subsystem, the real-time broadband impedance or admittance of the new energy subsystem and the remaining subsystem is obtained by impedance network calculation. A stability margin evaluation module, which is based on the broadband impedance or admittance of the new energy subsystem and the remaining subsystems and uses a frequency domain stability criterion to online evaluate the potential oscillation frequency and stability margin of the system, including: According to the real-time broadband impedance Z of the new energy subsystem and the remaining subsystems N and Z R or admittance, and obtain the impedance and Z of the new energy subsystem and the remaining subsystem N +Z R The Nyquist curve of If the Nyquist curve surrounds the (0,0) point, the system is unstable, otherwise, the system is stable; Get the impedance ratio Z of the new energy subsystem and the remaining subsystem N / Z R Nyquist curve, determine the shortest distance γ from the Nyquist curve to (-1,0) min ; For the intersection point i of the impedance ratio Nyquist curve and the unit circle, i = 1, 2, ..., s, s is the number of intersection points, calculate the distance γ from the intersection point i to (-1, 0) i ; Will be with γ min Closest γ i The frequency of the corresponding intersection point is determined as the potential oscillation frequency f of the system i , the stability margin PM(f i )=2arcsin(γ i / 2); A weak point location module, which, when the system stability margin is lower than a set threshold, calculates the real part of the impedance of each station in the new energy subsystem at the potential oscillation frequency, compares the magnitude of the real part of the impedance, and determines the weak point of the station; Stability information output module, which outputs the potential oscillation frequency, stability margin and weak point information of the new energy power system in real time; The timing module sets the interval time and repeatedly starts the broadband impedance identification module of key equipment, the broadband impedance identification module of new energy stations, the broadband impedance acquisition module of new energy subsystems and remaining subsystems, the stability margin assessment module, the weak point location module and the stability information output module to perform a rolling online assessment of the system's broadband oscillation risk.

7. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it can be used to execute the method described in any one of claims 1 to 5, or to run the system described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it can be used to execute the method described in any one of claims 1 to 5, or to run the system described in claim 6.

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