Method and device for identifying negative damped oscillation source of power system and electronic equipment

Abstract

The invention provides an electric power system negative damping oscillation source identification method and device and electronic equipment, and relates to the technical field of electric power systems. The power system negative damped oscillation source identification method comprises the following steps: acquiring monitoring data when a target power system generates negative damped oscillation; determining a quantitative index of negative damped oscillation of the target power system according to the monitoring data; according to the quantitative index, constructing a target function, and representing the intensity of the negative damped oscillation of the target power system based on the target function; utilizing a preset sensitivity analysis algorithm to determine sensitivity indexes of various correlation parameters of the quantitative indexes for the target function; and determining the negative damped oscillation source of the target power system according to the sensitivity indexes of the various correlation parameters for the target function. According to the invention, the negative damped oscillation source of the novel power system can be identified efficiently and accurately.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, apparatus and electronic device for identifying negative damping oscillation sources in power systems. Background Technology

[0002] With the development of new energy power generation technologies and the widespread application of power electronic equipment in power systems, new power systems exhibit the "dual high" characteristics of high-proportion renewable energy integration and high-proportion power electronic equipment penetration. These characteristics make the dynamic properties of power systems increasingly complex, and oscillation problems, especially negatively damped oscillations, have become a significant hidden danger affecting the safe and stable operation of the system. Negatively damped oscillations refer to spontaneous oscillations caused by insufficient or even negative damping in a certain mode of the system. If measures are not taken in time, the oscillations may diverge, or even cause large-scale power outages.

[0003] Accurately identifying and locating the source of negatively damped oscillations is crucial for implementing effective suppression measures and ensuring system safety. Traditional methods for identifying negatively damped oscillation sources typically rely on establishing a state-space model of the power system and using eigenvalue analysis and eigenvalue sensitivity analysis to determine the influence of system parameters on specific oscillation modes. For example, by calculating the eigenvalues ​​of the system state matrix, the presence of eigenvalues ​​with positive real parts indicates the existence of a negatively damped mode. Further calculations of the sensitivity of various system parameters (such as generator excitation parameters, power system stabilizer parameters, and power electronic equipment control parameters) to this eigenvalue indicate the degree of influence of each parameter on the eigenvalue, thus identifying the dominant parameters and their corresponding equipment, locating the oscillation source, and implementing oscillation suppression measures. However, such model-based methods face numerous challenges in practical applications. First, establishing an accurate and complete state-space model for modern power systems containing numerous power electronic devices and complex control logic is inherently difficult; model order reduction and parameter uncertainties can lead to inaccurate analysis results. Second, the calculation of eigenvalue sensitivity is complex, and for large-scale systems, the computational load is enormous, making it difficult to meet the real-time requirements of online applications. Furthermore, these methods are typically local sensitivity analyses, which are difficult to fully reflect the global impact of parameters on system oscillations across the entire range of variation.

[0004] Therefore, how to efficiently and accurately identify the negative damping oscillation source of a new type of power system is an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for identifying negative damped oscillation sources in power systems, in order to overcome the above-mentioned deficiencies in the prior art and to efficiently and accurately identify negative damped oscillation sources in new power systems.

[0006] This invention provides a method for identifying negative damped oscillation sources in a power system, comprising the following steps.

[0007] Acquire monitoring data when a target power system experiences negative damped oscillations; determine a quantitative index of the negative damped oscillations of the target power system based on the monitoring data; construct an objective function based on the quantitative index to characterize the intensity of the negative damped oscillations of the target power system; determine the sensitivity index of various correlation parameters of the quantitative index with respect to the objective function using a preset sensitivity analysis algorithm; and determine the source of the negative damped oscillations of the target power system based on the sensitivity index of the various correlation parameters with respect to the objective function.

[0008] According to the present invention, a method for identifying negative damped oscillation sources in a power system is provided. The quantitative index of the negative damped oscillation of the target power system includes at least one of the active power oscillation amplitude and the voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system. The step of constructing an objective function based on the quantitative index includes: establishing the objective function based on at least one of the active power oscillation amplitude and the voltage oscillation amplitude at the preset frequency at the point of common coupling of the target power system.

[0009] According to the present invention, a method for identifying negatively damped oscillation sources in a power system includes determining the sensitivity index of various correlated parameters of a quantitative indicator to a target function using a preset sensitivity analysis algorithm. This includes: sampling each correlated parameter within its normal range to obtain multiple sets of parameter samples; determining the value of the target function for each set of parameter samples; and determining the sensitivity index of various correlated parameters of the quantitative indicator to the target function using the preset sensitivity analysis algorithm based on each set of parameter samples and its corresponding target function value.

[0010] According to the present invention, a method for identifying negative damped oscillation sources in a power system is provided, wherein the preset sensitivity analysis algorithm is the Sobol sensitivity analysis method; the step of sampling various correlation parameters within their normal value ranges to obtain multiple sets of parameter samples includes: using a Monte Carlo sampling algorithm to sample various correlation parameters within their normal value ranges to obtain the multiple sets of parameter samples; the step of determining the sensitivity index of various correlation parameters of the quantification index with respect to the objective function based on each set of parameter samples and the value of its corresponding objective function using the preset sensitivity analysis algorithm includes: using the Sobol sensitivity analysis method to determine the first-order sensitivity index and the total sensitivity index of various correlation parameters of the quantification index with respect to the objective function based on each set of parameter samples and the value of its corresponding objective function.

[0011] According to the present invention, a method for identifying negative damped oscillation sources in a power system includes a method for identifying the correlation parameters, wherein the correlation parameters include the proportional-integral (PI) control parameters of the static var generator (SVM) and the direct-drive wind turbine controller in the target power system. The method further includes sampling the various correlation parameters within their normal ranges using a Monte Carlo sampling algorithm to obtain multiple sets of parameter samples.

[0012] According to the present invention, a method for identifying negative damped oscillation sources in a power system, wherein determining the negative damped oscillation source of the target power system based on the sensitivity index of various correlation parameters to the objective function includes: identifying the component to which the correlation parameter with the largest total sensitivity index belongs as the negative damped oscillation source of the target power system.

[0013] The present invention also provides a power system negative damping oscillation source identification device, comprising the following modules: The system comprises: an acquisition module for acquiring monitoring data when a target power system experiences negative damped oscillations; a first determination module for determining a quantitative index of the negative damped oscillations of the target power system based on the monitoring data; a construction module for constructing an objective function based on the quantitative index to characterize the intensity of the negative damped oscillations of the target power system; a second determination module for determining the sensitivity index of various correlation parameters of the quantitative index with respect to the objective function using a preset sensitivity analysis algorithm; and a third determination module for determining the source of the negative damped oscillations of the target power system based on the sensitivity index of the various correlation parameters with respect to the objective function.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power system negative damping oscillation source identification method as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power system negative damping oscillation source identification method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the power system negative damping oscillation source identification method as described above.

[0017] The present invention provides a method, device, and electronic equipment for identifying negative damped oscillation sources in power systems. These methods acquire monitoring data when a target power system experiences negative damped oscillations. Since the monitoring data contains various electrical quantities of the target power system under negative damped oscillation conditions, it provides a reliable foundation for subsequent analysis, ensuring an accurate grasp of the oscillation characteristics of the target power system. Based on the monitoring data, quantitative indicators of the negative damped oscillations are determined, transforming complex oscillation phenomena into quantifiable values, thus providing a unified standard for measuring the intensity of the negative damped oscillations in the target power system. An objective function is constructed to characterize the intensity of the negative damped oscillations in the target power system. By rationally selecting and constructing the objective function, the negative damped oscillation characteristics of the target power system can be mathematically expressed, intuitively reflecting the severity of the negative damped oscillations, making subsequent sensitivity analysis more targeted and accurate. A preset sensitivity analysis algorithm is used to determine the sensitivity index of various correlation parameters of the quantitative indicators relative to the objective function. Since the new power system contains a large number of power electronic devices with numerous parameters and complex interrelationships, by using the sensitivity index of various correlation parameters of quantitative indicators to the objective function, we can more efficiently and accurately identify the correlation parameters that have a significant impact on the negative damping oscillation of the target power system, and thus accurately locate the oscillation source of the target power system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for identifying negative damping oscillation sources in power systems provided by the present invention.

[0020] Figure 2 This is a flowchart illustrating the method provided by the present invention for determining the sensitivity index of various correlation parameters of a quantitative index relative to a target function.

[0021] Figure 3 This is a schematic diagram of the total sensitivity index of multiple parameter samples when the active power oscillation amplitude of the common connection node is the objective function, provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of the power system negative damping oscillation source identification device provided by the present invention.

[0023] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] The following is combined Figures 1-3 The present invention describes a method for identifying negative damped oscillation sources in power systems.

[0026] Figure 1 This is a flowchart illustrating the method for identifying negatively damped oscillation sources in power systems provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 101: Obtain monitoring data when the target power system experiences negative damped oscillation.

[0027] The target power system refers to the power network to be analyzed that is prone to negatively damped oscillations. The components of the target power system may include: traditional power generation equipment (such as generators), power electronic equipment (such as static var generators (SVG) and direct-drive wind turbines (PMSG), loads, and transmission lines.

[0028] In some embodiments, the target power system is a novel power system, namely a power grid with "dual high" characteristics (high proportion of renewable energy integration and high proportion of power electronic equipment penetration). Such systems exhibit complex dynamic characteristics and a significantly increased risk of oscillations due to the intermittency of renewable energy generation and the rapid dynamic response of power electronic equipment.

[0029] Negative damped oscillations are a spontaneous and unstable dynamic phenomenon in new power systems. They are caused by insufficient damping in a certain mode of the power system (negative damping coefficient), resulting in continuous divergent oscillations. If not suppressed in time, they may lead to drastic voltage / power fluctuations and even large-scale power outages.

[0030] In practice, the causes of negative damping oscillations include: improper control parameters of power electronic equipment, such as unreasonable settings of proportional-integral parameters (Kp, Ki) of the PI controller of SVG and PMSG, which leads to deterioration of system damping characteristics; the impact of new energy access, the intermittent output of wind power / photovoltaic changes the system power balance, which may trigger low-frequency oscillation modes; and the dynamic interaction between power electronic equipment and traditional generator sets, which may form new oscillation paths.

[0031] Negative damped oscillations in power systems are typically low-frequency oscillations (0.1~2.5Hz), but wider-bandwidth oscillations may occur in new power systems.

[0032] In the specific implementation process, monitoring data of the target power system when negative damping oscillation occurs can be obtained through the following methods: real-time measurement systems, such as wide area measurement systems (WAMS) and SCADA systems, provide high time resolution (millisecond level) synchronous phasor data; offline simulation calculations, through simulation platforms (such as CloudPSS, PSCAD, Matlab / Simulink) to generate dynamic response data of the system under specific operating conditions.

[0033] Monitoring data may include, but is not limited to: Electrical quantity waveforms: Voltage, amplitude and phase of three-phase voltage at the point of common coupling (PCC); Current, amplitude and phase of three-phase current at the PCC or equipment outlet; Power, waveforms of active power (P) and reactive power (Q) at the PCC as a function of time.

[0034] Device status variables: Controller output signals (such as reference voltage and reference current) of power electronic devices (SVG, PMSG).

[0035] Excitation voltage / current of the generator excitation system.

[0036] Oscillation characteristic quantity: Oscillation amplitude at a specific frequency, such as the active power oscillation amplitude or voltage oscillation amplitude at the dominant oscillation mode frequency at the PCC (which can be determined through spectrum analysis).

[0037] For illustrative purposes only, the target power system is a grid-connected system containing two SVG devices (SVG1 and SVG2). The power source supplies power to the load, and the SVG provides reactive power compensation. When the system experiences negatively damped oscillations, monitoring data such as voltage, current, power, and the output power of the SVG devices at the point of common coupling are obtained through simulation platforms (such as CloudPSS, PSCAD, Matlab / Simulink, etc.) or monitoring devices of the actual system.

[0038] Step 102: Based on the monitoring data, determine the quantitative index of the negative damping oscillation of the target power system.

[0039] Quantitative indicators are numerical parameters used to measure the severity of negative damped oscillations in a power system. Their core function is to transform the physical characteristics of power system oscillations (such as amplitude, frequency, and duration) into comparable and analyzable mathematical expressions. In some embodiments, quantitative indicators include at least one of the active power oscillation amplitude and voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system, which can reflect the energy intensity of the negative damped oscillations in the target power system.

[0040] In the specific implementation process, numerical parameters that can be used to measure the severity of negative damping oscillations in the power system can be selected from the monitoring data as quantitative indicators of negative damping oscillations in the target power system.

[0041] Step 103: Based on the quantitative indicators, construct the objective function to characterize the intensity of the negative damping oscillation of the target power system.

[0042] In some embodiments, the quantitative index of the negative damping oscillation of the target power system includes at least one of the active power oscillation amplitude and the voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system. An objective function can be established based on at least one of the active power oscillation amplitude and the voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system.

[0043] As an example only, the objective function Y, constructed based on the active power oscillation amplitude of the common connection node at a preset oscillation frequency, is as follows: (1) in, It refers to the oscillation component of the active power of the common connection node at the dominant frequency (the frequency component with the most concentrated energy and the most significant oscillation amplitude when the power system oscillates, reflecting the core mode of dynamic instability of the system); This refers to the analysis period following the occurrence of the oscillation.

[0044] The objective function described above reflects the oscillation energy intensity of the common connection node at a preset frequency. The larger the amplitude, the more severe the negative damping oscillation of the power system.

[0045] Step 104: Using a preset sensitivity analysis algorithm, determine the sensitivity index of various correlation parameters of the quantitative index with respect to the objective function.

[0046] Sensitivity analysis algorithms are used to quantify the influence of correlation parameters on the value of the objective function. In practice, various sensitivity analysis algorithms can be used, such as the Sobol sensitivity analysis method (Sobol sensitivity index algorithm), and are not limited to the descriptions in this specification.

[0047] Correlation parameters refer to parameters that have a direct or indirect causal relationship with the negative damping oscillation characteristics of the power system, and whose changes may significantly affect the objective function (such as the oscillation amplitude at the common connection node PCC).

[0048] As an example only, considering the target power system in the example of step 101, and taking into account the significant impact of SVG control parameters on the dynamic characteristics of the target power system, the control parameters of SVG1 and SVG2 are selected as the associated parameters for the quantification index. It is assumed that each SVG uses dual closed-loop PI control, including a voltage outer loop (or reactive power outer loop) and a current inner loop. The reactive power outer loop PI parameter (proportional coefficient) of each SVG is selected. Integral time constant proportionality coefficient Integral time constant ) and the inner loop PI parameter (proportional coefficient) Integral time constant proportionality coefficient Integral time constant As a related parameter for quantitative indicators: , ,..., .

[0049] The various related parameters of quantitative indicators may include, but are not limited to: The controller parameters of power electronic devices (such as SVG and direct-drive wind turbines) directly affect the dynamic response characteristics of the equipment. For example, the outer loop PI parameters of SVG reactive power control include: the proportional coefficient, which controls the speed of reactive power regulation; and the integral time constant, which affects the steady-state reactive power accuracy. Another example is the inner loop PI parameters of a direct-drive wind turbine (PMSG) current control: the proportional coefficient, which adjusts the current tracking speed; and the integral time constant, which suppresses current steady-state error.

[0050] The adjustment parameters of the synchronous generator excitation controller affect the electromagnetic torque characteristics of the generator. For example, the excitation voltage gain adjusts the response speed of the generator terminal voltage to the excitation current; the excitation time constant reflects the inertia of the excitation system.

[0051] Power system stabilizer (PSS) parameters are added to the controller in the generator excitation system to provide damping torque to suppress low-frequency oscillations. For example, PSS gain adjusts the damping torque intensity; DC blocking time constant filters out DC components to prevent controller saturation.

[0052] For a specific implementation of using a preset sensitivity analysis algorithm to determine the sensitivity index of various correlation parameters of a quantification index relative to the objective function, please refer to [link to relevant documentation]. Figure 2 The relevant content will not be repeated here.

[0053] Step 105: Determine the negative damping oscillation source of the target power system based on the sensitivity index of various correlation parameters to the objective function.

[0054] In practical implementation, the component to which the correlation parameter with the largest total sensitivity index belongs can be used as the negative damping oscillation source of the target power system.

[0055] The component to which the associated parameter belongs is the physical carrier of the associated parameter, such as the equipment or control loop to which the associated parameter belongs. For example, if the associated parameter is the outer loop PI parameter of the SVG reactive power, then its associated component is a Static Var Generator (SVG) or the converter controller of the Static Var Generator (SVG). As another example, if the associated parameter is the inner loop PI parameter of the PMSG current, then its component is a Direct Drive Wind Turbine (PMSG) or the converter controller of the Direct Drive Wind Turbine (PMSG).

[0056] As an example only, the overall sensitivity index of the various correlation parameters obtained in step 203 with respect to the objective function (detailed description can be found in the relevant content of step 203, and will not be repeated here) is as follows: Figure 3 As shown, the associated parameters The total sensitivity index corresponding to the reactive power outer loop integral time constant of SVG2 It is the highest, significantly higher than all other parameters. This indicates that, within the range of variation of the selected correlation parameters, The changes in [parameter name] have the greatest impact on the active power oscillation amplitude of the target power system, including its independent influence and the interaction with other parameters. Therefore, based on this result, the relevant parameters can be determined. It is the dominant parameter affecting the negative damped oscillation of the target power system, and The device SVG2 was identified as the negative damped oscillation source of the target power system.

[0057] In practice, after identifying the source of negative damping oscillation, one or more of the most influential related parameters can be adjusted to suppress the negative damping oscillation of the target power system.

[0058] As an example only, for the above example, when SVG2 is identified as a negatively damped oscillation source and After setting it as the dominant parameter, further targeting can be performed. Adjustments can be made (e.g., increase the size appropriately). The value of is adjusted to reduce the response speed of the integral action, in order to observe whether the negative damping oscillation of the target power system can be effectively suppressed.

[0059] In the embodiments provided by this invention, it is not necessary to establish a precise state-space model of the power system. Instead, a data-driven approach is used to identify the key parameters and their associated equipment that have the most significant impact on negatively damped oscillations in the power system. This achieves accurate location of the negatively damped oscillation source and effectively improves engineering practicality. Specifically, this invention employs a data-driven Sobol global sensitivity analysis method, avoiding the need to establish a complex and precise state-space model of the power system, thus reducing the requirements for model accuracy. The Sobol sensitivity analysis algorithm can assess the impact of associated parameters across the entire range of variation and considers the interactions between associated parameters, making it more comprehensive and accurate than traditional local sensitivity analysis.

[0060] The embodiments provided by this invention rely on system monitoring data and simulation / testing capabilities, making them easy to deploy and apply in engineering projects, and are particularly suitable for analyzing novel power systems containing a large number of power electronic devices. After identifying the correlation parameters that have the greatest impact on the negative damping oscillations of the target power system using the embodiments provided by this invention, clear directions can be provided for subsequent strategies to suppress the negative damping oscillations of the target power system (such as parameter optimization and controller tuning).

[0061] Figure 2 This is a flowchart illustrating the method provided by the present invention for determining the sensitivity index of various correlation parameters of a quantitative index relative to a target function, as shown below. Figure 2 As shown, the method includes the following: Step 201: Within the normal range of various correlation parameters, sample each correlation parameter separately to obtain multiple sets of parameter samples.

[0062] In some embodiments, the preset sensitivity analysis algorithm is the Sobol sensitivity analysis method; the Monte Carlo sampling algorithm can be used to sample various correlation parameters within the normal range of various correlation parameters to obtain multiple sets of parameter samples.

[0063] For example, the associated parameters include the proportional-integral control parameters of the static var generator and the direct-drive wind turbine controller in the target power system. The Monte Carlo sampling algorithm can be used to sample the proportional-integral control parameters of the static var generator and the direct-drive wind turbine controller in the target power system within the normal range of their values ​​to obtain multiple sets of parameter samples.

[0064] Step 202: Determine the value of the objective function for each set of parameter samples.

[0065] In the specific implementation process, each set of parameter samples can be used as input variables to simulate or measure the target power system and obtain the value of the objective function for each set of parameter samples.

[0066] For example, each set of parameter samples can be substituted into the simulation model of the target power system. The simulation model can accurately reflect the topology, equipment characteristics, and control strategies of the target power system. The simulation model is run to simulate the operation of the target power system under given parameters, and the objective function is calculated after the simulation reaches a steady state or a specific oscillation state. For example, for each set of parameter samples, the oscillation amplitude of the active power at the common connection node at the dominant oscillation frequency is obtained after running the simulation.

[0067] For example, if the measurement conditions of the target power system are available, disturbance tests can also be performed on the target power system. The control parameters corresponding to each set of parameter samples are set into the controller of the actual equipment, and then the relevant electrical quantities of the common connection node are collected through measuring equipment (such as power sensors, voltage sensors, etc.). After data processing and analysis, the value of the objective function is obtained.

[0068] As an example only, regarding the associated parameters of the quantitative indicator selected in the example of step 104: , ,..., A reasonable range of values ​​can be set for each associated parameter, for example, fluctuating by a certain percentage (e.g., ±20%) above or below its design value. Then, a Monte Carlo method (e.g., using a Sobol sequence generator) is used to generate N sets (e.g., N=5000) of parameter samples in the joint value space of these parameters. For each set of parameter samples ( , ,..., ), where j=1,...,N, are substituted into the power system simulation model, the simulation is run, and the oscillation amplitude Y(j) of the active power at the dominant oscillation frequency is calculated after the simulation (or after the oscillation stabilizes). After collecting all N sets of parameter samples and their corresponding objective function values ​​Y(j), the calculation formula of Sobol sensitivity analysis is used to calculate each associated parameter. First-order sensitivity index of objective function Y and total sensitivity index .

[0069] Step 203: Based on each set of parameter samples and the value of its corresponding objective function, use a preset sensitivity analysis algorithm to determine the sensitivity index of various correlation parameters of the quantitative index to the objective function.

[0070] In some embodiments, the first-order sensitivity index and the total sensitivity index of various correlation parameters of the quantification index with respect to the objective function can be determined using the Sobol sensitivity analysis method based on each set of parameter samples and the value of their corresponding objective function.

[0071] First-order sensitivity index Indicates the first i The contribution of individual parameter variations to the variance of the objective function.

[0072] Total Sensitivity Index Indicates the first i The individual variations of each parameter sample and their interactions with other parameters contribute to the variance of the objective function.

[0073] The following describes the power system negative damping oscillation source identification device provided by the present invention. The power system negative damping oscillation source identification device described below can be referred to in correspondence with the power system negative damping oscillation source identification method described above.

[0074] Figure 4 This is a schematic diagram of the structure of the power system negative damping oscillation source identification device provided by the present invention.

[0075] like Figure 4 As shown, the power system negative damping oscillation source identification device includes the following modules.

[0076] The acquisition module 410 is used to acquire monitoring data when the target power system experiences negative damping oscillations.

[0077] The first determining module 420 is used to determine the quantitative index of the negative damping oscillation of the target power system based on the monitoring data.

[0078] The construction module 430 is used to construct an objective function based on the quantitative index, so as to characterize the intensity of the negative damping oscillation of the target power system based on the objective function.

[0079] The second determining module 440 is used to determine the sensitivity index of various correlation parameters of the quantification index with respect to the objective function using a preset sensitivity analysis algorithm.

[0080] The third determining module 450 is used to determine the negative damping oscillation source of the target power system based on the sensitivity index of the various correlation parameters to the objective function.

[0081] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, communications interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a method for identifying negative damped oscillation sources in a power system. This method includes: acquiring monitoring data when a target power system experiences negative damped oscillations; determining a quantitative index of the negative damped oscillations of the target power system based on the monitoring data; constructing a target function based on the quantitative index to characterize the intensity of the negative damped oscillations of the target power system; determining the sensitivity index of various correlation parameters of the quantitative index with respect to the target function using a preset sensitivity analysis algorithm; and identifying the negative damped oscillation source of the target power system based on the sensitivity index of the various correlation parameters with respect to the target function.

[0082] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the power system negative damping oscillation source identification method provided by the above methods. The method includes: acquiring monitoring data when a target power system experiences negative damping oscillation; determining a quantitative index of the negative damping oscillation of the target power system based on the monitoring data; constructing a target function based on the quantitative index to characterize the intensity of the negative damping oscillation of the target power system based on the target function; determining the sensitivity index of various correlation parameters of the quantitative index with respect to the target function using a preset sensitivity analysis algorithm; and determining the negative damping oscillation source of the target power system based on the sensitivity index of the various correlation parameters with respect to the target function.

[0084] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the power system negative damping oscillation source identification method provided by the above methods. This method includes: acquiring monitoring data when a target power system experiences negative damping oscillation; determining a quantitative index of the negative damping oscillation of the target power system based on the monitoring data; constructing a target function based on the quantitative index to characterize the intensity of the negative damping oscillation of the target power system based on the target function; determining the sensitivity index of various correlation parameters of the quantitative index with respect to the target function using a preset sensitivity analysis algorithm; and determining the negative damping oscillation source of the target power system based on the sensitivity index of the various correlation parameters with respect to the target function.

[0085] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying negatively damped oscillation sources in a power system, characterized in that, include: Acquire monitoring data when the target power system experiences negatively damped oscillations; Based on the monitoring data, a quantitative index of the negative damping oscillation of the target power system is determined; Based on the quantitative indicators, an objective function is constructed to characterize the intensity of the negative damping oscillation of the target power system. Using a preset sensitivity analysis algorithm, the sensitivity index of various correlation parameters of the quantitative index with respect to the objective function is determined; Based on the sensitivity index of the various correlation parameters to the objective function, the negative damping oscillation source of the target power system is determined.

2. The method for identifying negative damped oscillation sources in a power system according to claim 1, characterized in that, The quantitative indicators of the negative damping oscillation of the target power system include at least one of the active power oscillation amplitude and voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system. The step of constructing the objective function based on the quantitative index includes: The objective function is established based on at least one of the active power oscillation amplitude and voltage oscillation amplitude at a preset frequency at the point of common coupling of the target power system.

3. The method for identifying negative damped oscillation sources in a power system according to any one of claims 1 to 2, characterized in that, The step of using a preset sensitivity analysis algorithm to determine the sensitivity index of various correlation parameters of the quantification index with respect to the objective function includes: Within the normal range of the various correlation parameters, samples are taken from each of the various correlation parameters to obtain multiple sets of parameter samples; Based on each set of parameter samples, determine the value of the objective function for each parameter sample; Based on each set of parameter samples and the value of its corresponding objective function, the sensitivity index of various related parameters of the quantitative index to the objective function is determined using the preset sensitivity analysis algorithm.

4. The method for identifying negative damped oscillation sources in a power system according to claim 3, characterized in that, The preset sensitivity analysis algorithm is the Sobol sensitivity analysis method; Within the normal range of the various correlation parameters, samples are taken from each correlation parameter to obtain multiple sets of parameter samples, including: Using the Monte Carlo sampling algorithm, the various correlation parameters are sampled within their normal range to obtain the multiple sets of parameter samples; The step of determining the sensitivity index of various correlation parameters of the quantification index with respect to the objective function based on each set of parameter samples and their corresponding objective function values ​​using the preset sensitivity analysis algorithm includes: Based on each set of parameter samples and the value of its corresponding objective function, the first-order sensitivity index and the total sensitivity index of the various correlation parameters of the quantification index with respect to the objective function are determined using the Sobol sensitivity analysis method.

5. The method for identifying negative damped oscillation sources in a power system according to claim 4, characterized in that, The associated parameters include the proportional-integral control parameters of the static var generator and the direct-drive wind turbine controller in the target power system. The Monte Carlo sampling algorithm is used to sample the various correlation parameters within their normal value ranges to obtain the multiple sets of parameter samples, including: Using the Monte Carlo sampling algorithm, within the normal range of the proportional-integral control parameters of the static var generator and the direct-drive wind turbine controller in the target power system, the proportional-integral control parameters of the static var generator and the direct-drive wind turbine controller in the target power system are sampled to obtain the multiple sets of parameter samples.

6. The method for identifying negative damped oscillation sources in a power system according to claim 5, characterized in that, The step of determining the negative damped oscillation source of the target power system based on the sensitivity index of the various correlation parameters to the objective function includes: The component to which the correlation parameter with the largest total sensitivity index belongs is taken as the negative damping oscillation source of the target power system.

7. A device for identifying negative damped oscillation sources in a power system, characterized in that, include: The acquisition module is used to acquire monitoring data when the target power system experiences negative damped oscillations. The first determining module is used to determine the quantitative index of the negative damping oscillation of the target power system based on the monitoring data; A construction module is used to construct an objective function based on the quantitative indicators, so as to characterize the intensity of the negative damping oscillation of the target power system based on the objective function; The second determining module is used to determine the sensitivity index of various correlation parameters of the quantitative index with respect to the objective function using a preset sensitivity analysis algorithm; The third determining module is used to determine the negative damping oscillation source of the target power system based on the sensitivity index of the various correlation parameters to the objective function.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the power system negative damping oscillation source identification method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power system negative damping oscillation source identification method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power system negative damping oscillation source identification method as described in any one of claims 1 to 6.

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