Power system synchronous phasor measurement unit configuration scheme generation method and related equipment

Abstract

The invention provides a power system synchronous phasor measurement unit configuration scheme generation method and related equipment. The method comprises the steps of determining a voltage distribution coefficient of each node and a current phase of each branch of a new energy grid-connected system; identifying a main influence area of the dominant oscillation mode of the new energy grid-connected system by using the voltage distribution coefficient of each node; identifying a propagation path of a dominant oscillation mode of the new energy grid-connected system by using the current phase of each branch; and generating a synchronous phasor measurement unit configuration scheme of the new energy grid-connected system by combining the main influence area and the propagation path based on the obtained measurement demand. Therefore, the key monitoring position related to the oscillation mechanism can be accurately determined, and the generated PMU configuration scheme can highlight the real-time monitoring capability on the weak oscillation node while ensuring the measurement coverage efficiency.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method for generating a configuration scheme for a power system synchronous phasor measurement unit and related equipment. Background Technology

[0002] Currently, the scale of power electronic devices, including wind power, photovoltaics, flexible DC transmission, energy storage, and STATCOM, being connected to the power grid in new energy grid-connected systems is continuously expanding. Compared to traditional power systems dominated by synchronous generators, the large-scale connection of these devices reduces the system's equivalent inertia and grid strength, easily triggering broadband oscillations caused by interactions between the devices and the grid, as well as among the devices themselves, threatening the safe and stable operation of the power grid. As the proportion of power electronic devices further increases and the capacity of synchronous generators continues to decline in the future, the broadband oscillation problem will become increasingly prominent, necessitating the real-time monitoring of electrical quantity changes in the system through the appropriate configuration of measurement devices to promptly identify oscillation phenomena.

[0003] In existing technologies, synchronous phasor measurement units (PMUs) are widely used for power system oscillation monitoring due to their ability to acquire dynamic parameters such as oscillation frequency, amplitude, phase, and attenuation factor with high precision. Research on the optimal configuration of PMUs mainly focuses on two aspects: first, determining the minimum number and optimal installation location based on network-wide observability; and second, designing configuration schemes that meet specific application requirements and analytical tasks, such as voltage stability analysis, fault location, and oscillation identification. However, existing PMU configuration methods for oscillation identification still have shortcomings in monitoring oscillation mechanisms, making it difficult to clearly identify weak points in oscillations, thus affecting the accurate location and mitigation effectiveness of oscillation sources. Summary of the Invention

[0004] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the shortcomings of existing PMU configuration methods for oscillation identification in monitoring oscillation mechanisms, making it difficult to clearly identify weak points in oscillations, thus affecting the accurate location and mitigation of oscillation sources. This application proposes a method for generating a configuration scheme for a power system synchronous phasor measurement unit and related equipment.

[0005] In a first aspect, this application provides a method for generating a configuration scheme for a power system synchronization phasor measurement unit, the method comprising:

[0006] Determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system;

[0007] By utilizing the voltage distribution coefficients at each node, the main influence areas of the dominant oscillation mode in the new energy grid-connected system can be identified;

[0008] By utilizing the current phase of each branch, the propagation path of the dominant oscillation mode in the new energy grid-connected system can be identified;

[0009] Based on the obtained measurement requirements, combined with the main affected areas and propagation paths, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

[0010] In one embodiment, the steps of determining the voltage distribution coefficient of each node and the current phase of each branch in a new energy grid-connected system include:

[0011] Obtain the discrete state-space model of the new energy grid-connected system;

[0012] Based on the discrete state-space model, the node voltage observability matrix and branch current observability matrix of the dominant oscillation mode of the new energy grid-connected system are calculated.

[0013] The voltage distribution coefficients of each node are calculated using the node voltage observability matrix, and the phase of each branch current is calculated using the branch current observability matrix.

[0014] In one embodiment, the steps of calculating the voltage distribution coefficient of each node using the node voltage observability matrix and calculating the phase of each branch current using the branch current observability matrix include:

[0015] Calculate the voltage oscillation components of the oscillation modes corresponding to each node in the node voltage observability matrix, and obtain the node voltage distribution coefficient based on each voltage oscillation component;

[0016] Calculate the complex current response of each branch corresponding to the oscillation mode in the branch current observability matrix, and extract the branch current phase corresponding to each complex current response.

[0017] In one embodiment, the step of identifying the main influence area of ​​the dominant oscillation mode of a renewable energy grid-connected system using the voltage distribution coefficients of each node includes:

[0018] The maximum value among the voltage distribution coefficients of each node is used as the threshold of the node voltage distribution coefficient. Nodes with voltage distribution coefficients greater than the threshold are selected to obtain the target nodes. The main influence area of ​​the dominant oscillation mode of the new energy grid-connected system is formed based on the area corresponding to the target nodes.

[0019] In one embodiment, the step of identifying the propagation path of the dominant oscillation mode of a renewable energy grid-connected system using the current phase of each branch includes:

[0020] Calculate the current phase difference between adjacent branches based on the current phase of each branch.

[0021] The propagation path of the dominant oscillation mode of the new energy grid-connected system is constructed based on adjacent branches where the current phase difference is less than a preset current phase difference threshold.

[0022] In one embodiment, the step of generating a synchronous phasor measurement unit configuration scheme for a new energy grid-connected system based on the acquired measurement requirements, combined with the main affected areas and propagation paths, includes:

[0023] Based on the obtained measurement requirements, high-risk nodes in the main affected areas are located, as well as the main paths in the location propagation path.

[0024] Based on high-risk nodes and main paths, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

[0025] In one embodiment, the method further includes:

[0026] Identify the oscillation sources in the new energy grid-connected system, and optimize the configuration scheme of the synchronous phasor measurement unit based on the oscillation sources.

[0027] Secondly, this application provides a power system synchronization phasor measurement unit configuration scheme generation device, the device comprising:

[0028] The oscillation characteristic parameter determination module is used to determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system;

[0029] The main impact area identification module is used to identify the main impact areas of the dominant oscillation mode of the new energy grid-connected system by utilizing the voltage distribution coefficient of each node.

[0030] The propagation path identification module is used to identify the propagation path of the dominant oscillation mode of the new energy grid-connected system by utilizing the current phase of each branch.

[0031] The configuration scheme generation module is used to generate a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system based on the obtained measurement requirements, combined with the main affected areas and propagation paths.

[0032] Thirdly, this application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of any of the power system synchronization phasor measurement unit configuration scheme generation methods described in the above embodiments.

[0033] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0034] The memory stores computer-readable instructions, which, when executed by one or more processors, perform the steps of any of the power system synchronization phasor measurement unit configuration scheme generation methods described in the above embodiments.

[0035] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0036] The method and related equipment for generating power system synchronous phasor measurement unit (PMU) configuration schemes provided in this application, by introducing joint analysis of node voltage distribution coefficients and branch current phases, can not only reflect the spatial distribution characteristics of the dominant oscillation modes in new energy grid-connected systems, but also reveal the propagation path of oscillations within the system, thus overcoming the limitations of existing PMU configurations based on observability or single measurement indicators. By identifying the main affected areas and locking the oscillation propagation path, key monitoring locations related to the oscillation mechanism can be accurately determined. The generated PMU configuration scheme can ensure measurement coverage efficiency while highlighting the real-time monitoring capability of oscillation-prone nodes. This improves the accuracy of oscillation source location and provides reliable data support for subsequent targeted management, thereby effectively reducing the threat of broadband oscillations to the safe and stable operation of the power grid. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating the method for generating a power system synchronization phasor measurement unit configuration scheme provided in this application embodiment;

[0039] Figure 2 Example diagram of a new energy grid-connected system provided in the embodiments of this application;

[0040] Figure 3 Example diagram of the discrete-time equivalent circuit of the power element provided in the embodiments of this application;

[0041] Figure 4 Example diagram of a two-region multi-wind farm grid-connected system provided in the embodiments of this application;

[0042] Figure 5 λ provided for the embodiments of this application 235,236 Example diagram of node voltage distribution coefficient;

[0043] Figure 6 λ provided for the embodiments of this application 237,238Example diagram of node voltage distribution coefficient;

[0044] Figure 7 λ provided for the embodiments of this application 235,236 Example diagram of branch current distribution coefficient;

[0045] Figure 8 λ provided for the embodiments of this application 237,238 Example diagram of branch current distribution coefficient;

[0046] Figure 9 λ provided for the embodiments of this application 235,236 Example diagram of oscillation propagation path;

[0047] Figure 10 λ provided for the embodiments of this application 237,238 Example diagram of oscillation propagation path;

[0048] Figure 11 Example diagrams of the active power output waveforms of various wind farms provided in the embodiments of this application;

[0049] Figure 12 Example diagram of FFT analysis of RMS voltage at high-risk nodes provided in this application embodiment;

[0050] Figure 13 A schematic diagram of the power system synchronization phasor measurement unit configuration scheme generation device provided in the embodiments of this application.

[0051] Figure 14 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] This application provides a method for generating a configuration scheme for a power system synchronous phasor measurement unit. The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, server cluster, personal laptop, desktop computer, etc. Figure 1 As shown, the method includes:

[0054] S101: Determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system.

[0055] In this context, a new energy grid-connected system refers to an integrated operating system that integrates power generation and regulation equipment, primarily power electronic devices such as wind power, photovoltaics, flexible DC transmission, energy storage, and STATCOM, into the power system via a transmission or distribution network. A node refers to a bus or measurement point in a power system network model, including collection buses, substation-side buses, and connection points. The node voltage distribution coefficient is a dimensionless coefficient used to describe the relative strength or visibility of the voltage response at various network nodes under a given oscillation frequency or dominant oscillation mode. A branch refers to a transmission line or transformer branch connecting two nodes. The branch current phase refers to the phase angle (based on a synchronous timescale) of the current measured along the branch at a given frequency, used to characterize the phase characteristics of oscillation propagation in the network.

[0056] Specifically, firstly, the computer equipment can establish a communication connection with the measurement terminal of the new energy grid-connected system through the data acquisition module, obtain real-time measurement data of the voltage of each node and the current of each branch from the RTU or SCADA interface, and use the synchronization signal for time alignment to ensure the consistency and continuity of the data.

[0057] Secondly, computer equipment can preprocess the measurement data, including removing outliers, filling in missing data, and filtering out noise, to ensure the stability of the input data in amplitude and phase, providing a reliable basis for subsequent analysis.

[0058] Then, the computer equipment can identify the current dominant oscillation frequency of the system based on spectrum analysis, extract the voltage phasors of each node at that frequency, calculate the node voltage distribution coefficient, extract the current phasors of each branch and perform phase standardization processing to obtain the branch current phase.

[0059] Finally, the computer equipment can store and visualize the obtained node voltage distribution coefficients and branch current phases, which can then be used as input data for subsequent identification of the main affected areas and propagation paths, thus forming a complete analysis chain.

[0060] It is understandable that determining the voltage distribution coefficients of each node and the current phase of each branch in a renewable energy grid-connected system is necessary. The node voltage distribution coefficients quantitatively reflect the response intensity of oscillations at various locations within the system, thus identifying areas where oscillation energy is concentrated and most significantly affected. Meanwhile, the branch current phases reveal the direction and path of oscillation propagation within the network, aiding in the analysis of the correlation between the oscillation source and the affected area. By simultaneously acquiring these two types of parameters, the distribution characteristics and propagation patterns of the dominant oscillation mode can be comprehensively characterized spatially. This provides a reliable basis for accurately identifying weak nodes and key propagation channels, improving the accuracy and speed of oscillation source location, and providing strong data support for targeted optimization of measurement arrangements and the development of vibration suppression measures. Ultimately, this effectively enhances the real-time monitoring and rapid response capabilities of renewable energy grid-connected systems to broadband oscillations.

[0061] S102: Using the voltage distribution coefficients of each node, identify the main influence areas of the dominant oscillation mode of the new energy grid-connected system.

[0062] The dominant oscillation mode refers to the frequency components with significant energy and / or low damping characteristics, and their corresponding spatial distribution features, as determined by spectral analysis or parametric modal identification methods (such as Prony, matrix pencil, and AR modeling) under the operating conditions of a renewable energy grid-connected system. The main influence area refers to a set of continuous or closely related nodes identified based on node voltage distribution coefficients and system topology under the dominant oscillation mode, meeting predetermined strength, spatial connectivity, and temporal consistency conditions. This area reflects the geographical / grid subnet or bus group where oscillation energy is concentrated and has a significant impact on system stability.

[0063] Specifically, the computer equipment needs to obtain the voltage distribution coefficients of each node under the target dominant oscillation mode from the online monitoring system or historical measurement database. To ensure data reliability, the computer equipment will extract relevant timestamps, node topology information, and data confidence indicators during the acquisition process, and use preprocessing operations such as interpolation, denoising, and normalization to eliminate the interference of random errors and measurement noise on subsequent analysis.

[0064] Next, the computer equipment will perform a screening analysis on the preprocessed node voltage distribution coefficients. At the same time, the computer equipment will combine the time dimension to determine stability, that is, only nodes that maintain a high response over multiple consecutive time windows are retained, thereby ensuring that the screening results reflect stable oscillation characteristics rather than instantaneous fluctuations.

[0065] Subsequently, the computer equipment spatially clusters these high-response nodes based on the system topology. To do this, the computer equipment models the system as a graph structure, uses connectivity analysis or graph clustering algorithms to identify interconnected node clusters, and optimizes the region boundaries by combining geographical location or impedance distance information when necessary. This transforms scattered high-response nodes into physically coherent major influence areas, facilitating subsequent visualization and decision support.

[0066] After clustering, the computer system performs consistency verification on each candidate major influence region. This includes comparing the region's response characteristics at different frequency points, checking its stability over multiple time periods, and matching it with the theoretical modal distribution calculated by the system model. When the verification results meet the preset consistency and confidence requirements, the region is officially confirmed as the major influence region of the dominant oscillation mode.

[0067] The reason for using the voltage distribution coefficients of each node to identify the main affected areas of the dominant oscillation mode in a renewable energy grid-connected system is understandable. This is because the voltage distribution coefficients can quantitatively reflect the response intensity and energy distribution of each node under a specific oscillation mode. By comparing the relative magnitudes of the coefficients at each node, the area where oscillation energy is concentrated can be accurately located, thus revealing the most significant part of the oscillation impact. This method not only avoids the bias caused by relying on a single measurement point, but also comprehensively characterizes the oscillation impact range in a spatial dimension. This provides a reliable basis for subsequent PMU optimization, oscillation source location, and targeted vibration suppression measures, thereby improving the monitoring accuracy and handling efficiency of renewable energy grid-connected systems under broadband oscillation conditions and ensuring the safe and stable operation of the power grid.

[0068] S103: Using the phase of the current in each branch, identify the propagation path of the dominant oscillation mode in the new energy grid-connected system.

[0069] The propagation path refers to the energy transfer route of the dominant oscillation mode from the source to the affected area in the system, reflecting the temporal relationship and directionality of the oscillation between different branches.

[0070] Specifically, the computer equipment acquires the current phase data of each branch in the new energy grid-connected system. After receiving the measurement data of these branch currents, the computer equipment needs to perform data alignment and caching according to a unified time base to ensure that the phase information of different branches is comparable, while retaining a sufficiently long time series to provide continuous and stable input data for subsequent analysis.

[0071] Secondly, the computer equipment can preprocess the acquired branch current data to extract key information related to the dominant oscillation mode. Specifically, the computer equipment can use methods such as bandpass filtering and data smoothing to filter out components in the data that are unrelated to the target oscillation frequency, retaining only the current signal related to the oscillation mode. Simultaneously, the computer equipment can also perform quality checks on the raw phase data, removing branch data with low coherence, significant noise, or missing data to prevent erroneous information from interfering with the determination of the propagation path.

[0072] Then, the computer equipment can expand and unify the current phase of each branch. That is, the computer equipment can select a network-wide reference node as the phase zero point and convert the phase values ​​of other branches to a unified reference, so that the phase differences between different branches can be directly compared. In this process, the equipment can also determine the propagation direction of the oscillation signal between adjacent branches based on the magnitude and sign of the phase difference.

[0073] Next, the computer equipment can transform the local phase difference relationship into the identification of propagation links across the entire network. Specifically, the computer equipment can abstract the power grid into a directed graph structure composed of nodes and branches, and mark the propagation direction and phase difference of the oscillation signal on each branch. By traversing these directed links and performing path searches, the computer equipment can identify the main path of the dominant oscillation mode spreading outward from the source node, as well as multiple possible parallel propagation channels.

[0074] Furthermore, the computer device can perform statistical analysis and credibility assessment on the identified propagation paths. For example, the computer device can repeatedly perform path identification within different time windows and record the frequency of occurrence, directional stability, and degree of conformity with the system topology of each path. When a path appears continuously in multiple time windows and its direction is consistent, the device can mark it as a highly credible dominant propagation path.

[0075] The reason for using the current phase of each branch to identify the propagation path of the dominant oscillation mode in a renewable energy grid-connected system is understandable. This is because the branch current phase reflects the temporal sequence and directional characteristics of the oscillation signal within the power grid. By comparing the phase differences between different branches, the direction and path of oscillation energy transmission can be clearly identified, thus revealing the propagation link of the dominant oscillation mode in the system. This approach not only avoids the inaccurate propagation path judgment caused by relying solely on amplitude information but also enables end-to-end tracing from the oscillation source to the affected area by combining grid topology. Accurate identification of the propagation path provides a reliable basis for oscillation source location, critical channel monitoring, and targeted vibration suppression control, thereby improving the rapid response and stable operation capability of renewable energy grid-connected systems under broadband oscillation conditions.

[0076] S104: Based on the obtained measurement requirements, combined with the main affected areas and propagation paths, generate a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system.

[0077] Measurement requirements refer to a comprehensive description of the necessary electrical quantities to be collected, including measurement accuracy, sampling frequency, and data synchronization requirements, based on the objectives of monitoring and analyzing oscillations in renewable energy grid-connected systems. Synchronous phasor measurement unit (PMU) configuration schemes, while meeting measurement requirements, determine the installation location, quantity, and deployment strategy of PMUs based on the main affected areas and propagation paths. This scheme aims to achieve efficient and comprehensive monitoring of the oscillation characteristics of renewable energy grid-connected systems.

[0078] Specifically, firstly, computer equipment can map the main affected areas and propagation paths onto the topology of the renewable energy grid-connected system based on the acquired measurement requirements. This process identifies the locations of nodes requiring focused monitoring and critical transmission links, thus providing clear candidate installation points for the deployment of the PMU (Power Management Unit).

[0079] Subsequently, the computer equipment can conduct coverage and redundancy assessments of candidate installation points, provided that measurement requirements are met. The assessment includes the monitoring coverage of each installation point over the main affected area, its ability to continuously observe the propagation path, and the feasibility of equipment installation, while also taking into account factors such as communication bandwidth, engineering costs, and data synchronization accuracy.

[0080] Then, based on the above evaluation results, the computer equipment can generate a PMU configuration scheme through optimization algorithms. This scheme ensures effective coverage of the main affected areas and key propagation paths while taking into account the number of devices, deployment costs, and monitoring accuracy, ensuring that the generated configuration scheme is feasible and economical in engineering implementation.

[0081] The reason for generating a synchronous phasor measurement unit (PMU) configuration scheme for a renewable energy grid-connected system based on the acquired measurement requirements, combined with the main affected areas and propagation paths, is understandable. This approach directly links the monitoring targets to the locations in the power grid most significantly affected by oscillations and key energy transfer links, ensuring that the PMU deployment is both targeted and comprehensive. By combining measurement requirements with the main affected areas, accurate monitoring of high-risk nodes can be prioritized; further, by combining them with propagation paths, continuous observation of the entire oscillation energy transmission process can be achieved, avoiding information gaps in the monitoring links. This method not only achieves comprehensive coverage of key areas and channels with a limited number of devices but also reduces deployment costs and improves the spatiotemporal consistency of data, thereby significantly improving the accuracy of oscillation source location and the effectiveness of oscillation suppression measures, ultimately enhancing the stability and security of the renewable energy grid-connected system under broadband oscillation conditions.

[0082] In the above embodiments, by introducing joint analysis of node voltage distribution coefficient and branch current phase, not only can the spatial distribution characteristics of the dominant oscillation mode in the new energy grid-connected system be reflected, but the propagation path of the oscillation within the system can also be revealed, thus overcoming the limitations of existing PMU configurations based on observability or single measurement indicators. By identifying the main affected areas and locking the oscillation propagation path, key monitoring locations related to the oscillation mechanism can be accurately determined. The generated PMU configuration scheme can ensure measurement coverage efficiency while highlighting the real-time monitoring capability of oscillation-vulnerable nodes. This improves the accuracy of oscillation source location and provides reliable data support for subsequent targeted management, thereby effectively reducing the threat of broadband oscillations to the safe and stable operation of the power grid.

[0083] In one embodiment, the steps of determining the voltage distribution coefficient of each node and the current phase of each branch in a new energy grid-connected system include:

[0084] Obtain the discrete state-space model of the new energy grid-connected system;

[0085] Based on the discrete state-space model, the node voltage observability matrix and branch current observability matrix of the dominant oscillation mode of the new energy grid-connected system are calculated.

[0086] The voltage distribution coefficients of each node are calculated using the node voltage observability matrix, and the phase of each branch current is calculated using the branch current observability matrix.

[0087] The discrete state-space model refers to a mathematical model that describes the dynamic characteristics of a renewable energy grid-connected system in the discrete time domain using state variables and state equations, used to characterize the state changes of the system at different time steps. The node voltage observability matrix, constructed based on the discrete state-space model, is used to quantify the observability of each measured quantity with respect to the system's node voltage state variables. The branch current observability matrix, also constructed based on the discrete state-space model, is used to quantify the observability of each measured quantity with respect to the system's branch current state variables.

[0088] Specifically, when acquiring the discrete state-space model of a new energy grid-connected system, computer equipment can convert the system's dynamic characteristics into a discrete-time state equation model based on the grid's topology, equipment parameters, and field measurement data. When the original data is in continuous form, a discrete model adapted for digital computation can be obtained through discretization methods, and system identification technology can be used to complete or correct parameters when necessary.

[0089] Secondly, when calculating the node voltage observability matrix and branch current observability matrix based on the discrete state-space model, the computer equipment can construct the observation information representation according to the mapping relationship between the output variables and the state variables, and analyze the degree of influence of the system state on each observation within a preset time window, thereby generating two types of observability matrices to reflect the sensitivity of different measurement channels to the system state.

[0090] Then, using the node voltage observability matrix, the computer equipment can extract the relative contribution of each node in the dominant oscillation mode and normalize it to obtain the node voltage distribution coefficient; using the branch current observability matrix, the computer equipment can extract the response phase of each branch in the corresponding mode and standardize and suppress the noise of the phase data to obtain a stable estimate of the branch current phase.

[0091] Finally, the computer equipment can store the calculated node voltage distribution coefficients and branch current phases for subsequent analysis, providing reliable input data for identifying major affected areas and propagation paths, thus forming a stable analysis link.

[0092] It is understandable that it is necessary to obtain a discrete state-space model of the new energy grid-connected system and calculate the node voltage observability matrix and branch current observability matrix of the dominant oscillation mode based on the model. This is because the method can accurately characterize the response characteristics of each node and branch of the system to a specific oscillation mode within a unified mathematical framework. Furthermore, by calculating the node voltage distribution coefficient through the node voltage observability matrix and the branch current phase through the branch current observability matrix, the distribution of the voltage amplitude of each node under the oscillation mode and the difference relationship of the current phase of each branch can be intuitively quantified. This provides accurate data for subsequent identification of the main influence area and propagation path of oscillation, enabling in-depth analysis of the oscillation mechanism of the new energy grid-connected system and improving the pertinence and effectiveness of monitoring and control.

[0093] In one embodiment, the steps of calculating the voltage distribution coefficient of each node using the node voltage observability matrix and calculating the phase of each branch current using the branch current observability matrix include:

[0094] Calculate the voltage oscillation components of the oscillation modes corresponding to each node in the node voltage observability matrix, and obtain the node voltage distribution coefficient based on each voltage oscillation component;

[0095] Calculate the complex current response of each branch corresponding to the oscillation mode in the branch current observability matrix, and extract the branch current phase corresponding to each complex current response.

[0096] Among them, the voltage oscillation component refers to the complex quantity composed of the amplitude and phase of the node voltage signal at a specific oscillation mode frequency, reflecting the oscillation characteristics of the node in that mode. The current complex response refers to the complex quantity composed of the amplitude and phase of the branch current at the oscillation mode frequency.

[0097] Specifically, firstly, the computer equipment can select the mode vector corresponding to the target oscillation mode based on the acquired node voltage observability matrix, and extract the voltage oscillation component under that mode in the corresponding row of each node in the matrix. Then, the amplitude of the voltage oscillation component of each node is normalized according to the maximum or root mean square value to obtain the node voltage distribution coefficient, thereby quantifying the relative response of different nodes under that oscillation mode. Next, the computer equipment can extract the complex response of each branch current corresponding to the target oscillation mode based on the branch current observability matrix, and separate the phase information from it. To ensure the accuracy of the phase analysis, all phase values ​​can be phase-aligned relative to a unified reference node, and the phase data can be smoothed to reduce the interference of instantaneous fluctuations.

[0098] It is understandable that it is necessary to calculate the voltage oscillation components of each node's corresponding oscillation mode in the node voltage observability matrix and obtain the node voltage distribution coefficient, and to calculate the complex current response of each branch's corresponding oscillation mode in the branch current observability matrix and extract the branch current phase. This is because this method can quantify the response characteristics of each node and branch to the oscillation mode under a unified oscillation mode reference, thereby accurately characterizing the spatial distribution characteristics and phase relationships of the new energy grid-connected system under specific modes. This not only helps to accurately identify the areas with the most significant oscillation impact and key propagation paths, but also provides reliable data support for subsequent oscillation source localization and stability control strategies, improves the pertinence and effectiveness of system analysis results, and enhances the real-time performance and accuracy of oscillation problem handling.

[0099] In one example, the discrete state space of a novel power system with a high proportion of new energy sources is first constructed.

[0100] In new power systems with a high proportion of renewable energy, most common power components are included. These include power supply components and reactive power compensation components such as direct-drive wind turbines, doubly-fed wind turbines, photovoltaic power plants, supporting SVG (Static Var Generator), and synchronous generators. Transmission-side equipment includes DC transmission systems, transformers, and AC lines. After collecting the electrical energy, it is sent to the main power grid or load centers. Figure 2 As shown.

[0101] If we consider the grid-connected bus voltage and the current flowing into the grid as input and output variables, then any power element in the above power system can be represented by the following continuous-space state model:

[0102] (1)

[0103] In the formula, Represents a continuous state vector. and These represent the input and output vectors, respectively. , , and It is a coefficient matrix, which can be obtained by linearizing the differential equations or transfer functions of the dynamic characteristics of electrical components.

[0104] By discretizing the continuous state-space model in the above formula using a specific integration rule, we can obtain a discrete state-space model. The detailed derivation process will not be repeated here.

[0105] (2)

[0106] In the formula, This represents the state vector in the current discrete state space, which is of great significance in subsequent model modal analysis. , , and It is calculated from the coefficient matrix. This represents the discrete time step. Furthermore, the discrete-time equivalent circuit of the component can be obtained from the second term in (2), such as... Figure 3 As shown, this is equivalent to the Norton branch, where For equivalent conductance, This is the equivalent current source. The representation of the discrete-time equivalent circuit is key to constructing the system state matrix.

[0107] Next, based on the node voltage observability matrix, the oscillation risk region is identified.

[0108] Based on matrix theory, the discrete state matrix can be diagonalized as follows:

[0109] (3)

[0110] In the formula, It is composed of discrete state matrix A diagonal matrix composed of all eigenvalues. and Let be the left and right eigenvector matrices corresponding to the discrete eigenvalues, respectively, and they are orthogonal to each other, satisfying the condition... ,in It is the identity matrix with the same dimension as the state matrix.

[0111] By performing the linear transformation shown in equation (4), a new set of system state variables is obtained. This is called a modal quantity.

[0112] (4)

[0113] Substituting equations (3) and (4) together into the discrete state-space equation of the system, we can obtain the discrete state-space equation of the system expressed in terms of modal quantities, as follows:

[0114] (5)

[0115] Since the AC system can be represented in the discrete domain as a discrete equivalent circuit network composed of the equivalent conductance of the components and the historical current source, the network can be described by the node voltage equation (Equation (6)); according to the interconnection structure between the components in the system, the node injection current in the node voltage equation can be expressed as the product of the branch-node correlation matrix and the historical current term in the discrete model of each component (Equation (7)).

[0116] (6)

[0117] In the formula, and These represent column vectors for system node voltages and node injected currents, respectively.

[0118] (7)

[0119] In the formula, the superscript 'T' denotes the transpose of the matrix. It is a diagonal block matrix composed of the coefficient matrices of the historical current sources of each element. It is the state vector of the entire system, which includes the discrete state variables of all components.

[0120] By combining equations (6) and (7), the system node voltages can be obtained. With discrete state variables The mathematical relationship between them is as follows:

[0121] (8)

[0122] Substituting equation (4) into the above equation, we can also obtain the relationship between system node voltages and modal quantities, as follows:

[0123] (9)

[0124] In the formula, It is defined as the node voltage observability matrix of the system.

[0125] because The system represents a system where the modal quantities are decoupled from each other, therefore the matrix... The i-th column characterizes the response of the i-th mode in the voltage of each node. Since the node voltage and branch current are converted to the unified xy rotating coordinate system of the whole network when the state space modeling is performed, for an n-node system, the node voltage vector in equation (6) should be of order 2n×1 (including the x / y axis components of the voltage). Therefore, in order to obtain the total response of the oscillation components in the voltage of each node, it is necessary to superimpose the x-axis components and the y-axis components. Specifically, the voltage oscillation component of the i-th mode in node p The calculation is as follows:

[0126] (10)

[0127] In the formula, the letters in the parentheses represent the index numbers of the matrix elements.

[0128] To compare the relative amplitudes of the oscillating components in the voltage at each node, the node voltage distribution coefficient is defined as follows:

[0129] (11)

[0130] Based on the calculation results of the voltage distribution coefficient of each node, the power grid section composed of nodes with relatively large amplitudes can be defined as the main oscillation risk area of ​​the system.

[0131] Furthermore, based on the branch observability matrix, the oscillation propagation path is identified.

[0132] From the perspective of circuit theory, the current in the j-th branch can be expressed as:

[0133] (12)

[0134] in and It is the equivalent conductance and current source in the discrete-time equivalent circuit of the j-th branch. It is the branch voltage.

[0135] Then, by further representing the current equations of all branches in the system as matrices, we obtain:

[0136] (13)

[0137] in and These are branch current and voltage vectors. It is a diagonal matrix consisting of the equivalent conductances of all electrical components. It is the block diagonal matrix mentioned earlier.

[0138] Considering the relationship between branch voltages and node voltages, as well as the node voltage equations, we have:

[0139] (14)

[0140] (15)

[0141] in It is a node voltage variable. It is the node injection current, which consists of the current sources in the discrete-time equivalent circuit model of all components in the zero-input state of the target system.

[0142] Substituting (14) and (15) into (13), and considering the linear transformation, we can obtain:

[0143] (16)

[0144] in It is an identity matrix, where m is the total number of branches. This is the branch current observability matrix. Specifically, the elements in the i-th column of Q represent the mode response on the x / y axis for each branch current. Therefore, the total current response of branch k under mode i can be expressed as:

[0145] (17)

[0146] in This represents the element in the 2kth row and i-th column of matrix Q. The amplitude and phase in the formula reflect the oscillation amplitude and direction of the modal current on the corresponding branch, respectively. Theoretically, based on this, operators can intuitively understand the distribution and propagation of the dominant oscillation mode in complex power systems during offline analysis, and, combined with the oscillation characteristics exhibited by actual PMU measurement data, locate the source of actual broadband oscillations.

[0147] If only the oscillation amplitude is considered, the branch current distribution coefficient can be further defined as:

[0148] (18)

[0149] Equation (18) describes the ratio of the amplitude of the branch oscillating current to the total oscillating current in the system. The branch with the larger modal current can be determined based on the relationship between the branch current distribution coefficients. At the same time, it should be noted that it is not appropriate to assume that the higher the modal current flowing through the component, the higher the component's participation in the oscillation mode. These are two different concepts, reflecting different aspects of the target system.

[0150] In one embodiment, the step of identifying the main influence area of ​​the dominant oscillation mode of a renewable energy grid-connected system using the voltage distribution coefficients of each node includes:

[0151] The maximum value among the voltage distribution coefficients of each node is used as the threshold of the node voltage distribution coefficient. Nodes with voltage distribution coefficients greater than the threshold are selected to obtain the target nodes. The main influence area of ​​the dominant oscillation mode of the new energy grid-connected system is formed based on the area corresponding to the target nodes.

[0152] Specifically, firstly, the computer equipment should determine the baseline value and initial scaling parameters used for threshold determination. To this end, the computer equipment can obtain the voltage distribution coefficient sequence of each node from the current time window (or historical aggregation window) and read the maximum value as the baseline amplitude. Subsequently, a preset scaling factor, such as a configurable range in engineering, is loaded by the operation and maintenance strategy or algorithm configuration. This scaling factor is used to map the baseline amplitude to the node voltage distribution coefficient threshold. The preset scaling factor can be manually set by the system administrator or automatically initialized by the system based on historical event statistics to meet the engineering requirements of different power grid scales and risk preferences.

[0153] Secondly, the computer equipment performs necessary smoothing and robustness processing on the voltage distribution coefficient sequence before calculating the threshold. To this end, the computer equipment can first apply moving average or median filtering to the distribution coefficients in the time dimension to suppress instantaneous noise and measurement spikes, and use confidence weights to reduce or remove data from low-quality measurement points. Only data that meets the data integrity and confidence threshold requirements will participate in the threshold calculation, thereby avoiding threshold shifts caused by single-point anomalies.

[0154] Then, the computer equipment filters out high-response nodes based on a defined threshold. To do this, the computer equipment can compare the distribution coefficients of all nodes with the threshold within each analysis time window, marking nodes with coefficients greater than the threshold as candidate affected nodes; and further apply a persistence condition in the time dimension, requiring candidate nodes to continuously meet the threshold condition within several consecutive sliding windows (or appear more frequently than a preset proportion in multiple samplings) before they can be finally included in the main influence area of ​​the dominant oscillation mode, so as to eliminate misjudgments of short-term pulses or transient events.

[0155] Furthermore, the computer equipment should organize the selected nodes into continuous influence regions based on the power grid topology and physical proximity. To this end, the computer equipment can use network topology information or impedance / distance information to aggregate the connectivity of the marked nodes, merge adjacent and electrically related node groups to form several candidate influence regions, and calculate the clustering degree, energy share, and spatial expansion of each region to quantify its importance. In the case of isolated single points, the computer equipment can merge them with neighboring high-response nodes according to a strategy or expand the set of neighboring nodes according to a secondary threshold to ensure the physical continuity of the influence region.

[0156] Next, the computer device should handle extreme cases caused by the threshold to ensure robustness. To this end, when the device finds too few selected nodes (possibly due to an excessively high threshold or weak signals), it automatically activates backup strategies, such as selecting the top N high-value nodes by ranking or appropriately lowering the threshold. Conversely, when there are too many selected nodes (possibly due to an excessively low threshold or noise), the computer device can raise the threshold or use further density clustering / hierarchical screening in the candidate set to identify core influence subdomains, thereby avoiding the generation of overly broad influence area suggestions.

[0157] Then, the computer equipment should support the comprehensive determination of multiple modes and multiple frequencies. To this end, the equipment performs threshold screening on different dominant oscillation modes or similar frequencies to obtain their respective influence regions. Subsequently, according to engineering needs, these regions are subjected to intersection and union operations, such as taking the union to ensure coverage of all potential disturbed areas, or taking the intersection to identify high-risk resonance areas. When necessary, the results of different modes are fused according to weights to form the final comprehensive influence region determination.

[0158] It is understandable that by multiplying the maximum value of the voltage distribution coefficient of each node by a preset ratio to set a threshold, and selecting nodes with distribution coefficients exceeding this threshold as the main affected areas, key nodes with significant oscillation energy distribution can be automatically screened based on the relative response intensity of each node in the dominant oscillation mode. This avoids over- or under-screening caused by improper setting of absolute values. This method can not only adaptively adjust the screening criteria in different operating conditions and in new energy grid-connected systems of different scales to ensure the accuracy and robustness of the identification of the main affected areas, but also effectively focus on the nodes that contribute the most to oscillation propagation, providing a reliable basis for subsequent oscillation source location, suppression strategy formulation, and monitoring resource optimization.

[0159] In one embodiment, the step of identifying the propagation path of the dominant oscillation mode of a renewable energy grid-connected system using the current phase of each branch includes:

[0160] Calculate the current phase difference between adjacent branches based on the current phase of each branch.

[0161] The propagation path of the dominant oscillation mode of the new energy grid-connected system is constructed based on adjacent branches where the current phase difference is less than a preset current phase difference threshold.

[0162] Specifically, firstly, the computer equipment should establish a candidate set of adjacent branch pairs based on the extracted branch current phase data. To this end, the computer equipment uses system topology information to determine the "adjacent branch" relationship, such as line pairs sharing a common bus or directly connected in the topology, and reads the preprocessed and phase-normalized phase values ​​for each pair of adjacent branches within each analysis time window to prepare input data for subsequent phase difference calculation.

[0163] Secondly, the computer equipment performs phase unrolling and consistency processing on the read phase data to eliminate wrapping effects and artifacts caused by instantaneous jumps. Specifically, this includes aligning phase values ​​to a unified reference standard, performing phase unrolling to avoid misjudgments caused by ±180° foldbacks, and smoothing or weighting the phase sequence over multiple adjacent time windows to improve the robustness of phase difference calculation and suppress the influence of instantaneous noise.

[0164] Then, the computer equipment calculates the phase difference for each pair of adjacent branches and simultaneously calculates the corresponding confidence index. The confidence index includes the coherence, amplitude, or modal participation of the two branch signals at that frequency. The equipment uses these indicators to downweight or eliminate phase difference results to avoid misleading propagation decisions due to low-energy or low-coherence phase differences. The phase difference threshold here is configurable; a commonly used reference value in engineering is 90°, but it can be adjusted according to system characteristics.

[0165] Next, the computer device makes a preliminary judgment based on the phase difference and confidence level. When the phase difference between two adjacent branches is less than a preset threshold and the confidence level meets the threshold, the computer device marks the branch pair as a "candidate of in-phase propagation," indicating that there is a tendency for oscillating components to propagate between the branches. When the phase difference is greater than the preset threshold (e.g., close to or exceeding 90°) and the confidence level is sufficient, it is marked as a "candidate of out-of-phase / interaction," indicating that there may be out-of-phase coupling or energy exchange between the two branches, but it should not be directly used as the basis for judging the main propagation direction. The above discrimination rules enable the construction of propagation paths to identify both intuitive in-phase propagation chains and potential interaction regions.

[0166] Subsequently, the computer equipment constructs a propagation candidate graph using a graph structure. Nodes represent converter stations or buses, and edges represent adjacent branch pairs. For branch pairs identified as in-phase candidates, directed or undirected propagation edges are established, and the edge weights are set as comprehensive weights, such as the degree to which the phase difference approaches zero, coherence, amplitude contribution, and time duration. For out-of-phase candidates, interaction edges can be recorded in the graph with different labels or weights for differentiated processing during path selection.

[0167] Then, the computer device performs path search and scoring on the propagation candidate graph to identify the dominant propagation path. For this purpose, the computer device can employ path scoring algorithms, such as maximum consistency link search based on cumulative edge weights or multi-segment priority selection sorted by edge weights, to traverse possible links and calculate a comprehensive score for each path. The path score takes into account phase consistency, energy transfer intensity, coherence stability, and temporal persistence. The path with the highest score that meets the minimum length and stability requirements is identified as the dominant propagation path. If branches exist, the device records the branch structure and sorts them by importance.

[0168] In addition, computer equipment should consider temporal consistency and robustness verification. To this end, the computer equipment repeatedly executes the above identification process on multiple sliding time windows, statistically analyzes the frequency of occurrence and directional stability of each path, and only considers paths that appear continuously in most time windows and have a consistent direction as high-confidence propagation paths; for paths that appear occasionally or for short periods of time, they are marked as temporary phenomena and their priority is reduced to prevent the mistaken identification of transient coupling as the dominant propagation channel.

[0169] Next, for out-of-phase situations and complex coupling scenarios, the computer equipment should provide specialized processing strategies. Specifically, when certain adjacent branches exhibit significant out-of-phase relationships (phase difference close to or exceeding 90°) and alternate with in-phase edges, they should be identified as possible coupling or backpropagation regions. These regions can be further distinguished using auxiliary time-domain cross-correlation or causal analysis methods to differentiate between true backpropagation, reflection, and phase differences caused by a common excitation source.

[0170] It is understandable that by calculating the current phase difference between adjacent branches based on the current phase of each branch, and using a preset phase difference threshold as a criterion to select branch pairs with smaller phase differences to construct propagation paths, the actual transmission direction and correlation of oscillation energy in the system can be effectively reflected. Since a smaller phase difference implies that the current oscillation components of these branches have high synchronicity, they can be approximated as having a tendency to propagate in the same direction, thus accurately depicting the propagation link of the dominant oscillation mode in the renewable energy grid-connected system. This method not only avoids misjudgments that may arise from relying solely on amplitude or frequency information, but also clearly locates the main flow path of oscillation energy in complex power grid topologies, providing a high-confidence basis for oscillation source location, key channel monitoring, and suppression strategy formulation.

[0171] In one embodiment, the step of generating a synchronous phasor measurement unit configuration scheme for a new energy grid-connected system based on the acquired measurement requirements, combined with the main affected areas and propagation paths, includes:

[0172] Based on the obtained measurement requirements, high-risk nodes in the main affected areas are located, as well as the main paths in the location propagation path.

[0173] Based on high-risk nodes and main paths, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

[0174] Specifically, firstly, the computer equipment transforms the acquired measurement requirements into quantifiable constraints and objectives, including the dominant modes that must be covered, the desired sampling and reporting rates, phase and amplitude accuracy, synchronization accuracy, and budget and engineering limitations. Simultaneously, the identified major influencing areas and candidate propagation paths are mapped onto the system topology, forming a preliminary set of candidate monitoring points, providing a foundation for subsequent screening.

[0175] Secondly, within the main affected area, computer equipment calculates a risk score for each candidate node to locate high-risk nodes. The risk score comprehensively considers the node's voltage distribution coefficient, modal participation, the node's importance in the system, its historical disturbance sensitivity, and the reliability of measurement data, combined with a time-based stability test to eliminate short-term anomalies, thus obtaining a list of high-risk nodes ranked from highest to lowest risk.

[0176] Then, computer equipment assesses the propagation path to locate the main path. The assessment includes the path's phase consistency, coherence between branches, energy percentage along the path, frequency of occurrence of the path under different operating conditions, and degree of overlap with the main affected area. Simultaneously, key nodes in the path are identified, and their importance to path continuity and oscillatory energy transfer is assessed to determine the dominant path segments requiring focused monitoring.

[0177] Next, the computer equipment combines high-risk nodes with the dominant path to generate synchronous phasor measurement unit deployment schemes, and performs coverage and redundancy analysis on each scheme. During the generation process, the improvement effect of each measurement point on the observation of high-risk nodes, continuous path monitoring, and observability of different oscillation modes is evaluated. At the same time, considering factors such as communication conditions, power supply conditions, GPS synchronization feasibility, and installation costs, a feasibility score is calculated for each scheme, resulting in multiple deployment schemes to choose from.

[0178] It is understandable that by combining measurement needs with the oscillation characteristics of new energy grid-connected systems, and prioritizing the identification of high-risk nodes and main propagation paths in key affected areas, focused coverage of the most sensitive locations to system stability can be achieved with limited monitoring resources, thereby improving the configuration efficiency and observation effectiveness of synchronous phasor measurement units. Since high-risk nodes often play a crucial role in triggering and amplifying oscillations, and the main path is the core channel for the transmission of oscillation energy within the system, deploying measurement points based on these two factors can significantly enhance the ability to quickly locate oscillation sources and accurately track the propagation process, while reducing the cost waste caused by redundant deployment points, ultimately improving the dynamic safety monitoring level and operational reliability of new energy grid-connected systems.

[0179] In one embodiment, the method further includes:

[0180] Identify the oscillation sources in the new energy grid-connected system, and optimize the configuration scheme of the synchronous phasor measurement unit based on the oscillation sources.

[0181] Specifically, the first step is to determine potential oscillation trigger locations based on system operating data and oscillation mode analysis results. Computer equipment can utilize real-time acquired node voltage and branch current measurements, through methods such as spectrum analysis and modal participation factor calculation, to identify nodes and equipment locations that contribute significantly to the dominant oscillation mode. These high-participation locations typically correspond to potential oscillation sources, such as grid connection points in wind farms or photovoltaic farms, converter interface locations, and synchronous machine terminals directly connected to the grid.

[0182] After identifying potential oscillation sources, further cross-validation is performed by combining system topology and equipment operating characteristics. For example, for oscillations caused by wind farm converters, causality can be confirmed by analyzing the phase relationship between their output power fluctuations and the overall system oscillations; for oscillations caused by synchronous machines, the consistency between the unit rotor speed deviation and the grid oscillation phase can be used for judgment. Through this multi-dimensional verification, false positives can be effectively reduced and the accuracy of oscillation source location can be improved.

[0183] Once the oscillation source is identified, it needs to be prioritized for monitoring, and the configuration of synchronous phasor measurement units (PMUs) should be optimized. In renewable energy grid-connected systems, the oscillation source is not only the starting point for oscillation triggering but also a key location for the outward propagation of oscillation energy. Therefore, prioritizing the deployment of PMUs at these locations can enable source monitoring and rapid early warning of oscillations. Furthermore, PMUs can be deployed along the main transmission channels connected to the oscillation source to simultaneously acquire the dynamic characteristics of the source point and its initial propagation path, thereby enhancing the end-to-end perception capability of the oscillation development process.

[0184] It is understandable that by identifying the oscillation sources of the renewable energy grid-connected system and using this as the basis for optimizing the configuration of the synchronous phasor measurement unit, monitoring equipment can prioritize covering the oscillation trigger location and its critical propagation path, thereby achieving real-time capture and precise location of the oscillation at its source. This deployment method can rapidly obtain high-value measurement data in the early stages of oscillation, improve the sensitivity and response speed of oscillation event detection, and provide accurate decision-making basis for subsequent oscillation suppression and control, ultimately effectively improving the stability and security of the renewable energy grid-connected system.

[0185] To facilitate understanding of the scheme in this application, specific examples are provided below.

[0186] Given a two-region multi-wind farm grid-connected system, such as Figure 4 As shown, the direct-drive wind farms are connected to the grid-controlled system at multiple nodes, with a total installed capacity of 2000MW. Each offshore wind farm is connected to the onshore control station via an offshore substation and a double-circuit 220kV AC submarine cable, and then connected to the system via a certain length of overhead transmission line. The onshore control station is equipped with high-voltage reactive power compensation. In addition, WF1 is also equipped with an 80MVar SVG for dynamic reactive power compensation.

[0187] Basic operating conditions: The basic operating conditions of the given system are shown in the table.

[0188]

[0189] Under the aforementioned basic operating conditions, a discrete state-space model of the entire system was constructed and the full-dimensional eigenvalues ​​of the system were calculated. The calculation results of the dominant oscillation mode are shown in the table.

[0190]

[0191] Where, λ 235,236 , λ 237,238 The nodes are close to the imaginary axis, have weak damping, and pose a high risk of instability. This is the dominant oscillation mode of the system. Therefore, the node voltage observability matrix and branch current observability matrix are calculated for these two oscillation modes. Based on the calculation results, the node voltage distribution coefficient and branch current distribution coefficient are obtained as follows: Figures 5-8 As shown. Based on the node voltage distribution coefficient, it can be seen that λ 235,236 The main affected area is the nodes near Region II, which manifests as the interaction between various wind farms and the power grid; λ 237,238 The main affected areas include nodes near the wind farms in both regions and nodes related to some transmission lines connecting the two regions. In summary, the above-mentioned areas can be designated as the main areas affected by the oscillations, and the nodes included therein are high-risk nodes.

[0192] Furthermore, the propagation path of the aforementioned dominant oscillation mode can be calculated based on the branch current observability matrix, as follows: Figure 9 , Figure 10 As shown.

[0193] Based on the above analysis results, PMU measurement devices can be configured at the outlets of each wind farm and at nodes 21, 22, 23, 24, 16, 17, 26, 27, 28, and 29 to monitor and trace the dominant oscillation mode of the system.

[0194] Simulation Analysis: With the line between nodes 16 and 21 disconnected, the system changes from a fully connected mode to an N-1 operating mode. The active power waveforms output by each wind farm are as follows: Figure 11 As shown, an FFT (Fast Fourier Transform) analysis is performed on the bus voltage configured with the PMU, as follows: Figure 12 As shown. By Figure 11 and Figure 12The time-domain simulation results show that the active power outputs of WF1 / 2 / 3 and WF4 / 5 are out of phase. Furthermore, based on the FFT analysis of the high-risk node voltage, this is a subsynchronous oscillation instability dominated by WF1 / 2 / 3. According to the oscillation characteristics obtained from the PMU, combined with the aforementioned offline analysis results, it can be determined that this is an oscillation mode λ. 237,238 Oscillation instability characterizes the interaction between WF1 / 2 / 3 and WF4 / 5. Therefore, the PMU configuration strategy based on offline identification of oscillation risk areas and oscillation propagation paths can achieve broadband oscillation source location by configuring as few PMU monitoring systems as possible on high-risk buses and clarifying the interaction relationship by combining the phase relationship of output power.

[0195] The following describes the power system synchronization phasor measurement unit configuration scheme generation apparatus provided in the embodiments of this application. The power system synchronization phasor measurement unit configuration scheme generation apparatus described below and the power system synchronization phasor measurement unit configuration scheme generation method described above can be referred to in correspondence. Figure 13 As shown, this application provides a power system synchronization phasor measurement unit configuration scheme generation device, the device comprising:

[0196] The oscillation characteristic parameter determination module 201 is used to determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system.

[0197] The main influence area identification module 202 is used to identify the main influence area of ​​the dominant oscillation mode of the new energy grid-connected system by utilizing the voltage distribution coefficient of each node.

[0198] The propagation path identification module 203 is used to identify the propagation path of the dominant oscillation mode of the new energy grid-connected system by utilizing the current phase of each branch.

[0199] The configuration scheme generation module 204 is used to generate a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system based on the obtained measurement requirements and in combination with the main affected areas and propagation paths.

[0200] In one embodiment, the oscillation characteristic parameter determination module 201 includes:

[0201] Discrete state-space model acquisition unit, used to acquire the discrete state-space model of the new energy grid-connected system;

[0202] The observability matrix calculation unit is used to calculate the node voltage observability matrix and branch current observability matrix of the dominant oscillation mode of the new energy grid-connected system based on the discrete state-space model.

[0203] The oscillation characteristic parameter determination unit is used to calculate the voltage distribution coefficient of each node through the node voltage observability matrix, and to calculate the phase of each branch current through the branch current observability matrix.

[0204] In one embodiment, the oscillation characteristic parameter determination unit includes:

[0205] The node voltage distribution coefficient determination sub-unit is used to calculate the voltage oscillation component of the corresponding oscillation mode of each node in the node voltage observability matrix, and obtain the node voltage distribution coefficient based on each voltage oscillation component.

[0206] The branch current phase determination sub-unit is used to calculate the complex current response of each branch corresponding to the oscillation mode in the branch current observability matrix, and extract the branch current phase corresponding to each complex current response.

[0207] In one embodiment, the main affected area identification module 202 includes:

[0208] The main influence area identification unit is used to select nodes whose voltage distribution coefficients are greater than the threshold value of each node as a preset proportion of the maximum value of each node voltage distribution coefficient, and to obtain the target node. The main influence area of ​​the dominant oscillation mode of the new energy grid-connected system is formed based on the area corresponding to the target node.

[0209] In one embodiment, the propagation path identification module 203 includes:

[0210] The current phase difference calculation unit is used to calculate the current phase difference between adjacent branches based on the current phase of each branch.

[0211] The propagation path construction unit constructs the propagation path of the dominant oscillation mode of the new energy grid-connected system, based on adjacent branches whose current phase difference is less than a preset current phase difference threshold.

[0212] In one embodiment, the configuration scheme generation module 204 includes:

[0213] The configuration scheme generation unit is used to locate high-risk nodes in the main affected areas and the main paths in the propagation path based on the acquired measurement requirements.

[0214] Based on high-risk nodes and main paths, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

[0215] In one embodiment, the apparatus further includes:

[0216] The configuration scheme optimization module is used to identify the oscillation source of the new energy grid-connected system and optimize the configuration scheme of the synchronous phasor measurement unit according to the oscillation source.

[0217] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the power system synchronization phasor measurement unit configuration scheme generation method as described in any of the above embodiments.

[0218] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the power system synchronization phasor measurement unit configuration scheme generation method as described in any of the above embodiments.

[0219] Indicatively, such as Figure 14 As shown, Figure 14 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 14 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions executable by the processing component 302, such as application programs. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the power system synchronization phasor measurement unit configuration scheme generation method of any of the above embodiments.

[0220] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0221] Those skilled in the art will understand that Figure 14 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0222] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0223] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0224] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating a configuration scheme for a power system synchronous phasor measurement unit, characterized in that, The method includes: Determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system; By utilizing the voltage distribution coefficients of each node, the main influence areas of the dominant oscillation mode of the new energy grid-connected system are identified; By utilizing the phase of the current in each of the aforementioned branches, the propagation path of the dominant oscillation mode of the new energy grid-connected system is identified; Based on the obtained measurement requirements, and combined with the main affected areas and the propagation path, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

2. The method for generating a power system synchronization phasor measurement unit configuration scheme according to claim 1, characterized in that, The steps for determining the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system include: Obtain the discrete state-space model of the new energy grid-connected system; Based on the discrete state-space model, the node voltage observability matrix and branch current observability matrix of the dominant oscillation mode of the new energy grid-connected system are calculated. The voltage distribution coefficients of each node are calculated using the node voltage observability matrix, and the phase of each branch current is calculated using the branch current observability matrix.

3. The method for generating a configuration scheme for a power system synchronization phasor measurement unit according to claim 2, characterized in that, The steps of calculating the voltage distribution coefficient of each node using the node voltage observability matrix and calculating the phase of each branch current using the branch current observability matrix include: Calculate the voltage oscillation component of the oscillation mode corresponding to each node in the node voltage observability matrix, and obtain the node voltage distribution coefficient based on each voltage oscillation component; Calculate the complex current response of each branch corresponding to the oscillation mode in the branch current observability matrix, and extract the branch current phase corresponding to each complex current response.

4. The method for generating a power system synchronization phasor measurement unit configuration scheme according to claim 1, characterized in that, The step of identifying the main influence area of ​​the dominant oscillation mode of the new energy grid-connected system using the voltage distribution coefficients of each node includes: A preset proportion of the maximum value among the voltage distribution coefficients of each node is used as the threshold of the node voltage distribution coefficient. Nodes with voltage distribution coefficients greater than the threshold are selected to obtain target nodes. The main influence area of ​​the dominant oscillation mode of the new energy grid-connected system is formed based on the area corresponding to the target node.

5. The method for generating a power system synchronization phasor measurement unit configuration scheme according to claim 1, characterized in that, The step of identifying the propagation path of the dominant oscillation mode of the new energy grid-connected system by utilizing the phase of each branch current includes: Calculate the current phase difference between each adjacent branch based on the current phase of each branch. The propagation path of the dominant oscillation mode of the new energy grid-connected system is constructed according to the adjacent branches whose current phase difference is less than the preset current phase difference threshold.

6. The method for generating a power system synchronization phasor measurement unit configuration scheme according to claim 1, characterized in that, The step of generating a synchronous phasor measurement unit configuration scheme for the new energy grid-connected system based on the acquired measurement requirements, combined with the main affected area and the propagation path, includes: Based on the obtained measurement requirements, high-risk nodes in the main affected area are located, as well as the main paths in the propagation path are located. Based on the high-risk nodes and the main paths, a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system is generated.

7. The method for generating a power system synchronization phasor measurement unit configuration scheme according to any one of claims 1 to 6, characterized in that, The method further includes: Identify the oscillation source of the new energy grid-connected system, and optimize the configuration scheme of the synchronous phasor measurement unit based on the oscillation source.

8. A device for generating a configuration scheme for a power system synchronous phasor measurement unit, characterized in that, The device includes: The oscillation characteristic parameter determination module is used to determine the voltage distribution coefficient of each node and the current phase of each branch in the new energy grid-connected system; The main influence area identification module is used to identify the main influence area of ​​the dominant oscillation mode of the new energy grid-connected system by utilizing the voltage distribution coefficient of each node. The propagation path identification module is used to identify the propagation path of the dominant oscillation mode of the new energy grid-connected system by utilizing the current phase of each branch. The configuration scheme generation module is used to generate a configuration scheme for the synchronous phasor measurement unit of the new energy grid-connected system based on the obtained measurement requirements, combined with the main affected area and the propagation path.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the power system synchronization phasor measurement unit configuration scheme generation method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the power system synchronization phasor measurement unit configuration scheme generation method as described in any one of claims 1 to 7.

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