Dynamic reactive stationing optimization method of power electronic converter system and related equipment

Abstract

The embodiment of the invention provides a dynamic reactive stationing optimization method of a power electronic converter system and related equipment, and belongs to the technical field of power system stability and control. The method comprises the following steps: constructing an empirical controllability covariance matrix of each point; therefore, two quantitative evaluation indexes, namely a key weak node transient voltage support effect index and a response similarity index, are provided. Constructing a double-layer optimization model: screening out a key compensation point set by taking maximization of global dynamic controllability of the system as a target at the upper layer; and the lower layer aims at maximizing support for key weak nodes, introduces response similarity constraints to avoid point distribution redundancy, and refines the result of the upper layer. And finally solving the model to obtain an optimal point distribution scheme. According to the method, the defects that a traditional linearization method is difficult to describe nonlinear transient behaviors and redundant point distribution cannot be effectively avoided are overcome, and a dynamic reactive power compensation configuration scheme with high dynamic supporting capacity, high economical efficiency and good complementarity can be provided for a power electronic system.

Description

Technical Field

[0001] This application relates to the field of power system stability and control technology, and in particular to a dynamic reactive power distribution optimization method and related equipment for a power electronic converter system. Background Technology

[0002] New energy sources, such as wind and solar power, are being developed on a large scale in resource-rich areas far from load centers and connected to the power grid via power electronic converters (such as inverters and converters). This transformation leads to a decrease in the inertia and a weakening of the damping characteristics of the power system, resulting in dynamic characteristics characterized by strong nonlinearity and multi-time-scale coupling. Voltage stability issues, especially the voltage support and recovery capabilities during transient processes, have become a key bottleneck restricting the safe and stable operation of power systems with a high proportion of new energy sources.

[0003] Dynamic reactive power compensation devices (such as STATCOM and SVC) are an effective means to improve system voltage stability. However, traditional reactive power compensation deployment planning methods mainly rely on steady-state power flow calculations or sensitivity analysis based on linearized models (such as the Jacobian matrix). These methods have significant shortcomings: First, they are difficult to accurately characterize the complex nonlinear transient behavior of the system under large disturbances such as faults and drastic power fluctuations (such as voltage overshoot, oscillation, and slow recovery); second, in high-proportion power electronic systems, the dynamic interaction between different converter control strategies makes the system response more complex, and traditional linearization methods cannot effectively characterize this time-domain interaction characteristic; finally, the randomness and volatility of renewable energy output lead to frequent changes in the system operating point, which may result in highly similar voltage support effects of different candidate compensation points under various operating scenarios. Improper planning can easily lead to redundant deployment of compensation devices and low investment efficiency.

[0004] Therefore, there is an urgent need for a new method for optimizing reactive power compensation placement that can accurately quantify transient voltage support capability, effectively identify and avoid redundant placement, and is applicable to the dynamic characteristics of high-proportion power electronic systems. Summary of the Invention

[0005] The main objective of this application is to propose a dynamic reactive power distribution optimization method, electronic equipment, storage medium, and program product for a power electronic converter system. Based on system transient response data, it can quantitatively evaluate the dynamic support capability and complementarity of compensation points, and achieve synergy between global dynamic performance improvement and precise support for local weak nodes through a two-layer optimization framework, ultimately obtaining an economical, efficient, and highly complementary optimized configuration scheme.

[0006] To achieve the above objectives, one aspect of this application proposes a dynamic reactive power distribution optimization method for a power electronic converter system, the method comprising: Based on the principle of empirical controllability covariance, a first index is constructed to evaluate the ability of candidate compensation points to support transient voltage at key weak nodes, and a second index is constructed to evaluate the similarity of dynamic response patterns among different candidate compensation points. A two-layer optimization model is constructed, comprising an upper-layer optimization model and a lower-layer optimization model; wherein... The upper-level optimization model aims to maximize the global controllability of the system by selecting a set of key compensation points from all candidate compensation points. The lower-level optimization model aims to maximize the transient voltage support capability for the key weak nodes, and uses the second index as a constraint to determine the final reactive power compensation point layout scheme from the set of key compensation points. Solve the two-layer optimization model to output the optimal dynamic reactive power compensation point layout scheme.

[0007] In some embodiments, constructing the first metric and the second metric includes: Multiple sets of reactive step disturbance signals with different amplitudes are applied to each candidate compensation point; The system acquires voltage time-domain response data under each group of disturbances, and constructs an empirical controllability covariance matrix corresponding to each candidate compensation point based on the voltage time-domain response data. Based on the aforementioned empirical controllability covariance matrix, a first index is calculated to quantify the dynamic support capability of candidate compensation points for a set of key weak nodes, and a second index is calculated to quantify the similarity of dynamic response patterns between any two candidate compensation points.

[0008] In some embodiments, the first index is a transient voltage support index for critical weak nodes, which is obtained by projecting the empirical controllability covariance matrix corresponding to candidate compensation point i onto a subspace composed of critical weak nodes and calculating the trace of the projection matrix.

[0009] In some embodiments, the second metric is a response similarity metric, which is obtained by calculating the Frobenius inner product of the empirical controllability covariance matrices corresponding to two candidate compensation points i and j in the critical weak node subspace.

[0010] In some embodiments, the objective function of the upper-level optimization model is to maximize the determinant of the combined empirical controllability covariance matrix corresponding to the selected set of key compensation points, or equivalently minimize its negative logarithmic determinant, so as to screen out the candidate point combination that contributes the most to improving the overall dynamic controllability of the system.

[0011] In some embodiments, the objective function of the lower-level optimization model is to maximize the sum of the transient voltage support indicators of all selected compensation points for the set of key weak nodes; its constraints include: 1) Global controllability constraint: The global controllability of the lower-level optimization scheme is required to be no less than the threshold determined by the upper-level optimization scheme; 2) Response similarity constraint: The average value of the response similarity index between any two selected compensation points in the scheme shall not exceed the preset threshold in order to avoid redundant point placement.

[0012] In some embodiments, the empirical controllability covariance matrix is ​​constructed as follows: For the h-th reactive step disturbance applied at candidate compensation point i, an empirical energy matrix is ​​constructed based on the system voltage state increment vector; By performing weighted averaging and normalization on the empirical energy matrices corresponding to multiple disturbances of different amplitudes, the empirical controllability covariance matrix of the candidate compensation point i is obtained.

[0013] In some embodiments, the method further includes an initial step: determining the set of candidate dynamic reactive power compensation buses for the system and the set of key weak nodes that require key support.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0017] Compared with the prior art, the beneficial effects of this application are as follows: 1) Data-driven and physically interpretable: It abandons the limitations of traditional linearized models and directly constructs an empirical controllability covariance matrix based on electromagnetic transient simulation or measured time-domain response data. This matrix can more realistically reflect the nonlinear and coupled transient characteristics of high-proportion power electronic systems. Moreover, the matrix has a clear control theory background (estimation of the system controllability Gram matrix) and clear physical meaning.

[0018] 2) Comprehensive quantitative assessment capabilities: It is the first to propose both the "support role index" to quantify the dynamic support capability of a single point and the "response similarity index" to quantify the complementarity / redundancy between points, providing a more comprehensive quantitative basis for site selection decisions.

[0019] 3) The optimization framework is scientific and effective: The proposed "global screening-local refinement" two-layer optimization framework effectively coordinates the two objectives of improving the overall dynamic controllability of the system and providing precise support for key weak areas. By introducing similarity constraints, it fundamentally avoids redundant configuration of compensation resources, so that the final solution has both global performance and economic benefits.

[0020] 4) Good engineering applicability: The method does not depend on specific fault types. It stimulates system dynamics by applying reactive step disturbances, making it suitable for multi-scenario analysis. The core calculation is based on offline simulation data, avoiding the direct embedding of large and time-consuming electromagnetic transient simulations into optimization loops, resulting in high solution efficiency and applicability to the planning and analysis of actual power grids. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the overall architecture of the dynamic reactive power distribution optimization method for high-proportion power electronic converter systems in this application embodiment.

[0022] Figure 2 This is a schematic diagram of the test case system structure used in the embodiments of this application.

[0023] Figure 3 This is a schematic diagram of the typical time-domain response curves of the voltage of each bus in the system when a reactive step disturbance is applied to a candidate bus.

[0024] Figure 4 It is a schematic diagram of the heatmap of the empirical controllability covariance matrix corresponding to a candidate bus calculated based on the response data.

[0025] Figure 5 This is a comparison chart of the voltage recovery curves under typical faults between the optimized point layout scheme obtained by the method of this embodiment and the traditional point layout scheme.

[0026] Figure 6 This is a flowchart illustrating the steps of the dynamic reactive power distribution optimization method for a power electronic converter system in this application embodiment.

[0027] Figure 7 This is a schematic diagram of the hardware structure of the electronic device in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0030] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0031] 1) Power electronic converters are core devices based on power electronic technology that convert electrical energy into its form through semiconductor switching devices (such as IGBTs and MOSFETs). They can change electrical parameters such as voltage, frequency, and number of phases and are widely used in the field of power control.

[0032] In regions rich in renewable energy resources far from load centers, large-scale renewable energy sources (such as wind and solar power) are connected to the grid via power electronic converters. This structural change significantly weakens the system's inertial support and damping characteristics, posing new challenges to voltage regulation, stability margin, and dynamic response. Specifically, the large-scale integration of renewable energy sources leads to frequent fluctuations in active power output, making system voltage stability highly dependent on continuous and rapid reactive power regulation. Under transient disturbances such as faults or switching, active-reactive power coupling and dynamic mismatch in converter control switching will exacerbate overvoltage and recovery oscillations. These characteristics highlight the crucial role of dynamic reactive power compensation in improving voltage stability and ensuring reliable operation in such systems.

[0033] In this context, traditional dynamic reactive power configuration methods relying on steady-state sensitivity analysis or linearized models are no longer sufficient to accurately characterize the real voltage dynamic behavior under varying operating conditions, nor can they effectively depict the time-domain interaction characteristics between multiple converters. To achieve effective voltage support configuration and reactive power regulation capabilities in high-proportion renewable energy scenarios, a novel dynamic reactive power planning and evaluation method capable of characterizing the transient full-process response of high-proportion power electronic converter systems is needed.

[0034] The existing technical solutions mainly have the following problems: 1) Under complex transient response, it is difficult to quantify the voltage support effect of the system.

[0035] In high-proportion power electronic systems, voltage dynamics are jointly determined by converter control, line parameters, and multi-source coupling, exhibiting strong nonlinearity, strong coupling, and multi-timescale characteristics. Traditional voltage support assessment methods based on linearized power flow Jacobian matrices can only reflect local sensitivity under small disturbances, making it difficult to characterize complex transient behaviors such as voltage overshoot, slow recovery, and oscillations that occur during faults, large power fluctuations, or control switching. Although fine-grained responses can be obtained through electromagnetic transient simulation, its computational cost is enormous. Directly embedding electromagnetic transient simulation into optimization models can lead to dimensionality explosion, convergence difficulties, and uncontrolled solution time, making system-level dynamic reactive power planning in hundreds of scenarios or all operating modes virtually infeasible. Therefore, constructing a quantitative index of voltage support capability that can accurately extract the core features of transient processes and be used for optimization solutions while ensuring physical interpretability is a key problem that urgently needs to be addressed.

[0036] 2) The problem of homogenization of support functions in reactive power compensation point layout In the context of high-proportion renewable energy, the operating point varies significantly with power output, and the voltage and power flow responses of different compensation candidate points may exhibit high similarity or partial overlap in various scenarios. Indiscriminately including all candidate points in the optimization would lead to redundant deployment, wasted compensation resources, and decreased global regulation efficiency. Therefore, a method is needed to measure the similarity of responses of different buses under multiple operating scenarios to identify candidate points with similar compensation effects, avoid redundant deployment of compensation equipment, and improve the flexibility of global planning.

[0037] In view of this, this application proposes a dynamic reactive power distribution optimization method, electronic equipment, storage medium, and program product for a power electronic converter system. The key technical points of this solution include: 1) proposing a voltage support effect difference evaluation index based on empirical controllability covariance, constructing a support effect index and response similarity index for key weak nodes through disturbance response data, thereby realizing a quantitative characterization of the dynamic adjustment potential and effect difference of compensation points; 2) constructing a two-layer dynamic reactive power distribution optimization model that takes into account both global controllability and local weak node support requirements, selecting key buses with significant global adjustment effects at the upper layer, and avoiding redundant distribution at the lower layer through support rewards and response similarity constraints, thereby obtaining an optimal compensation configuration scheme with high dynamic adjustment efficiency and complementarity.

[0038] This application provides a method for dynamic reactive power optimization of a power electronic converter system, relating to the field of power system stability and control technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the dynamic reactive power optimization method for a power electronic converter system, but is not limited to the above forms.

[0039] like Figure 6 As shown, this embodiment provides a method for dynamic reactive power distribution optimization of a power electronic converter system, including the following steps: S1: Based on the principle of empirically controllable covariance, construct evaluation indicators.

[0040] In one embodiment, step S1 specifically includes: applying multiple sets of reactive step disturbances of different amplitudes to each dynamic reactive power compensation candidate bus, and collecting time-domain response data of the voltage of each bus in the system; constructing an empirical controllability covariance matrix for each candidate bus based on the data; and then calculating a "critical weak node transient voltage support effect index" (first index) to quantify the dynamic support capability of the candidate point to the preset critical weak node set, and a "response similarity index" (second index) to quantify the similarity of the dynamic response modes between any two candidate points.

[0041] Furthermore, the response similarity index is obtained by calculating the Frobenius inner product of the empirical controllability covariance matrices of two candidate points i and j within the critical weak node subspace. A larger index value indicates a more similar dynamic disturbance influence pattern between the two points on the system, while simultaneously indicating a lower marginal benefit from point placement.

[0042] S2: Construct a two-layer optimization model.

[0043] Specifically, the model includes an upper-level optimization model and a lower-level optimization model: 1) The upper-level optimization model aims to maximize the global dynamic controllability of the system. Its objective function is usually to maximize the determinant of the covariance matrix of the combined empirical controllability corresponding to the selected candidate point combination (or minimize its negative logarithmic determinant). From all candidate points, an initial set of key compensation points that contributes the most to improving the overall dynamic adjustment potential of the system is selected.

[0044] 2) The lower-level optimization model is based on the upper-level optimization results, with the core objective of maximizing the transient voltage support capability for the set of key weak nodes. Its objective function is to maximize the sum of the first indicators of all selected compensation points. Simultaneously, two key constraints are introduced: first, a "global controllability constraint," ensuring that the global controllability of the lower-level scheme is not lower than a relaxation threshold determined by the upper-level scheme, preventing local optimization from damaging global performance; second, a "response similarity constraint," requiring that the average of the second indicators between any two selected compensation points within the scheme does not exceed a preset threshold, thereby proactively avoiding the selection of redundant compensation points with similar dynamic response modes and improving the spatial complementarity and adjustment diversity of the point placement.

[0045] Furthermore, the transient voltage support effect index of the key weak node is obtained by projecting the empirical controllability covariance matrix of candidate point i onto a subspace composed of the states of the key weak node, and calculating the trace of the projection matrix. The larger the index value, the stronger the dynamic voltage support effect of installing compensation equipment at that point on the weak area.

[0046] S3: Model solution and solution output.

[0047] In one embodiment, an applicable optimization algorithm (such as a mixed integer programming solver, a heuristic global optimization algorithm, etc.) is used to solve the two-layer optimization model, and finally the optimal dynamic reactive power compensation equipment layout scheme is output.

[0048] The solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific application examples.

[0049] See Figure 1 , Figure 1 The framework of the dynamic reactive power distribution optimization method for high-proportion power electronic converter systems proposed in this embodiment is illustrated. The main components of this method are as follows: In the first stage, two transient voltage support evaluation indicators—the support effect of key weak nodes and the response similarity—are constructed based on the principle of empirically controllable covariance. The approach is as follows: First, equivalent reactive power pulse signals are applied to each candidate compensation point to obtain the system's transient voltage response and construct an empirically controllable covariance matrix. Based on this, the support effect index of key weak nodes and the response similarity index of compensation points are defined. The support effect index of key weak nodes quantitatively characterizes the dynamic reactive power support effect of candidate buses on key weak areas by projecting the empirically controllable covariance matrix onto the weak node subspace. The response similarity index is constructed based on the inner product of the response modes of the covariance matrix and is used to measure the degree of difference in the dynamic response modes of different candidate buses, thereby identifying candidate points with similar support capabilities and high redundancy. These indicators together constitute an evaluation system for judging the merits of compensation node decision combinations, providing a data-driven quantitative basis for subsequent node optimization.

[0050] Furthermore, this embodiment proposes a two-layer reactive power distribution optimization method that simultaneously improves overall system controllability, provides precise support for key weak nodes, and avoids redundant compensation distribution. The method consists of an upper-layer optimization and a lower-layer optimization, respectively enhancing global regulation capability and refining local support capability. In the upper-layer optimization, this embodiment aims to maximize the global controllability energy of the system. Utilizing the spectral characteristics of the empirical controllability covariance matrix, it quantitatively screens the voltage regulation potential of each candidate compensation bus, selecting a set of key distribution points that can significantly improve the overall dynamic controllability of the system from the candidate bus set. The lower-layer optimization, based on the upper-layer screening results, aims to maximize the transient voltage support effect in weak areas. It constructs a constraint mechanism based on response mode similarity to suppress the high overlap of regulation paths and effects between different distribution points, avoiding redundant deployment of compensation equipment. Through the joint solution of the above-mentioned global controllability objective, weak node support index, and response similarity constraint, an optimal reactive power distribution configuration scheme that combines complementarity, economy, and dynamic performance can be obtained.

[0051] (1) Analytical evaluation index of dynamic reactive power compensation's ability to support transient voltage at key weak nodes Let the set of candidate buses for dynamic reactive power compensation in the system be . , m This represents the total number of candidate busbars. The analytical evaluation index for transient voltage support capability proposed in this embodiment is an empirically controllable covariance matrix constructed from simulation data. Constructed for parameters. The generation process is as follows: For each candidate compensation bus Without specifying a particular fault type, multiple sets of reactive step signals with different amplitudes are applied. Simulate the reactive power response process of dynamic reactive power compensation equipment.H The order of the disturbance amplitude.

[0052] For each set of step signals Starting from the moment of disturbance, the system voltage state vector According to a fixed step size The sampling records its time-domain evolution and uses the pre-perturbation steady-state value as the basis. Based on this, calculate the state increment. The system voltage state vector is represented as a stack of vector sequences of voltages from each bus: (1) In the formula, N The total number of system buses. K This is the total duration of the disturbance measurement. For each step signal... Its empirical energy matrix is ​​constructed as follows: (2) The system is on the bus. i Empirical controllability covariance matrix under compensated disturbance It can be constructed according to the following formula: (3) matrix Reflected in the busbar i The degree to which the dynamic state of the system can be excited over the entire time domain when reactive disturbances are injected.

[0053] Based on the spectral characteristics of the covariance matrix mentioned above, this embodiment proposes the following two analytical evaluation indicators to describe the ability of dynamic reactive power compensation schemes to support transient voltage at key weak nodes: 1.1) Indicators of transient voltage support at key weak nodes Assume the set of critical weak points in the system is The corresponding key node subspace selection matrix is ​​represented as follows: This is used to extract the state components of key weak nodes from the system's full state vector. It is also used to measure the dynamic reactive power compensation point. i Set of key weak nodes The transient voltage support effect will affect the empirical controllability covariance matrix. Projected onto the weak node subspace and using the trace operator As an indicator of the supporting role of key weak nodes, it is represented by symbols. express: (4) index The larger the value, the higher the busbar size. i The stronger the reactive power injection stimulates the dynamic response of key weak nodes, the higher the voltage support capability for local weak areas.

[0054] 1.2) Response Similarity Index To further evaluate the differences in dynamic response characteristics of different busbars or different types of compensation devices, this paper proposes different busbar compensation methods based on the Frobenius inner product. i and j Similarity index of dynamic response patterns between This is used to measure the consistency of the system's state response patterns under two different disturbance inputs. It is defined as follows: (5) In the formula, Characterizing the busbar i and j Similarity of dynamic response patterns within the weak node subspace. The larger the value, the higher the compensation point. i and j The more similar the voltage support effects on the same weak nodes, the lower the marginal benefit of repeated voltage point installations; conversely, The smaller the value, the more complementary the dynamic response modes of the two compensation points are.

[0055] (2) Data-driven two-layer reactive power distribution optimization method 2.1) Model Design Set binary decision variables Characterizes the location scheme of dynamic reactive power compensation equipment. Indicates the first i Whether to install equipment on each candidate bus. A first-level optimization aims to enhance global controllability by selecting equipment combinations that significantly improve system controllability. Specifically, this first-level optimization aims to maximize the determinant of the covariance matrix of the selected equipment combinations, i.e., minimize the negative logarithmic determinant. (6) In the formula, g The preset number of devices to be installed. The logarithmic form of the determinant of the ECC matrix is ​​equivalent to the geometric mean of the eigenvalues, characterizing the overall controllability of the system across all state directions and reflecting the balance of the system's global controllability. Therefore, the first-level optimization aims to select the configuration scheme with the optimal potential for global controllability from all possible device combinations.

[0056] After obtaining the optimal solution for the first layer of optimization, a relaxation threshold is set. The optimal solution of the first layer This is transformed into explicit constraints at the second layer. Furthermore, supporting rewards for key weak nodes and similarity constraints in response patterns are introduced to constrain the target... Modeling: (7) In the above formula, the target The controllable energy contributed by the reactive power compensation scheme to the weak node subspace was calculated by applying the transient voltage support index of the key weak node as defined in Section (1). As a constraint on global controllability, the second-level optimization scheme is required not to significantly deteriorate in terms of global controllability, ensuring that local reinforcement does not disrupt the global equilibrium controllability structure. The response similarity index defined in Section (1) is applied to form a response similarity constraint on the placement of points in the reactive power compensation scheme, wherein, For global aggregation of dynamic response pattern similarity, This refers to the number of busbar pairs. This is the acceptable maximum average similarity threshold. This constraint is used to limit the distribution of compensation devices in locations with low similarity in response characteristics, thereby improving the spatial complementarity of the layout and the diversity of system responses.

[0057] 2.2) Optimize the process The specific optimization process is as follows: Step 1: Without applying a fault, apply the compensation candidate bus. Apply multiple sets of reactive step signals of different amplitudes Construct the state response vector.

[0058] Step 2: State response vectors based on reactive disturbances of different amplitudes Compared with the benchmark Calculate the empirical controllability covariance matrix .

[0059] Step 3: Building and solving the two-layer optimization model. By incorporating a two-layer optimization framework and employing the derivative-free mixed-integer global optimization algorithm NOMAD, a dynamic reactive power compensation location scheme that balances enhanced global controllability with local synergistic benefits is obtained.

[0060] (3) Calculation example The overall architecture of the test system is as follows: Figure 2 As shown, an electromagnetic transient simulation model architecture was built using PSCAD / EMTDC and simulations were performed. The rated capacity of the source-side renewable energy power stations is 3000 MW~4000 MW, with a total installed capacity of 16 million kilowatts. Each renewable energy power station consists of multiple renewable energy units, whose output power is boosted and collected to the sending-end flexible DC converter station. The total number of system buses... N =12, and are respectively recorded as #1-#12.

[0061] 3.1) Calculation of Voltage Support Evaluation Indicators The substation busbars (#1-#5) and the main transmission network busbars (#6-#11) were selected as candidate buses for dynamic reactive power equipment deployment, forming a candidate busbar set. .

[0062] Record the steady-state voltage vector of the system before the disturbance begins. For any candidate compensation bus Multiple sets of step reactive power pulse signals with different amplitudes and lasting 1 second are applied to the candidate bus. The reactive power disturbance amplitude of the pulse signals is set as follows:

[0063] Starting from the moment the disturbance begins, with a fixed sampling step size Record the voltage response of all buses in the system. Corresponding reactive power disturbance amplitude. At the sampling time The system voltage state vector can be obtained as follows:

[0064] Taking a reactive power disturbance with an amplitude of 400 Mvar injected into bus #6 as an example, the corresponding compensation point number is: The disturbance amplitude is After performing time-domain simulation of the disturbance scenario, the transient voltage response curves of all buses in the system can be obtained, such as... Figure 3 As shown in the figure, each curve represents the time-domain voltage response of each bus in the system under reactive power disturbance, with different colors corresponding to the voltage response of different buses. The starting point of all voltage curves corresponds to the start time of the disturbance. t =8s, and continue recording until the disturbance ends. t =9s followed by a 1s recovery phase.

[0065] For each disturbance amplitude All according to formula (1) Figure 3 Medium voltage curve Stacking constitutes voltage state vector Further calculations are performed according to equation (2). As shown in Table 1.

[0066] Table 1 Empirical Energy Matrix (unit: )

[0067]

[0068] right H indivual Normalization is performed according to equation (3) to obtain the candidate compensation busbars. The corresponding empirical controllability covariance matrix ,like Figure 4 As shown.

[0069] Finally, take The PCC point for new energy grid connection is designated as a critical weak node. Further calculations of the support role indicators for each critical weak node can be performed. and dynamic response pattern similarity index As shown in Tables 2 and 3.

[0070] Table 2 Indicators of Transient Voltage Support at Key Weak Nodes result

[0071] Table 3 Response Pattern Similarity Index result

[0072] 3.2) Analysis of the Optimization Results of Double-Layer Reactive Power Layout 3.2.1) Optimal Point Layout Results Pick For different numbers of equipment installed g The configuration combinations obtained after performing two-level addressing optimization are shown in Table 4. Table 4 Optimal Location Results

[0073] When configuring only a single compensation unit, node 5 is selected for both levels, indicating that it has the most significant regulating effect on global voltage recovery in the considered fault scenarios. As the number of installed devices increases... g With the increase of [a certain number of nodes], the first-level optimization solution tends to select nodes that enhance the overall controllability of the system; in the second-level optimization... Within a controllable range of performance loss, the similarity of responses is constrained, and support for key nodes is maximized within this feasible region. This process tends to replace redundant nodes with nodes that have slightly lower individual contributions but provide strong support for key nodes and are highly complementary to other devices in the combination, ultimately obtaining the optimal solution that balances global performance and local support efficiency.

[0074] 3.2.2) Comparative Experiment Simulation verification was performed using the dynamic reactive power compensation device STATCOM. The results were compared with those of the uncompensated (Uncomp) reactive power compensation optimization method using Static-State Voltage Sensitivity Index (SVSI) and Dynamic Voltage Sensitivity Index (DVSI) in traditional power grids. Figure 5 As shown, from Figure 5 The simulation results show that the method proposed in this embodiment has significant advantages in both performance and collaborative compensation benefits.

[0075] (4) Summary and advantages In summary, the key technical points of the technical method proposed in this embodiment include: 1) Propose an analytical evaluation index for the transient voltage support effect and similarity of dynamic reactive power compensation schemes.

[0076] This embodiment addresses the challenge of traditional linearization methods in accurately characterizing the dynamic voltage characteristics after a fault and the transient voltage support capability of different compensation points for the entire network in high-proportion renewable energy transmission systems. It constructs a data-driven analytical evaluation of transient voltage support. First, an empirical controllability covariance matrix is ​​built based on a series of time-domain response samples of reactive power injection and node voltage. Building upon this, this embodiment proposes two analytically calculable indices: the support role of key weak nodes and response similarity. These indices quantify the dynamic support capability of compensation points in weak regions and the degree of overlap in the adjustment effects between compensation points, thus comprehensively characterizing the system support effect and dynamic controllability of a given distribution scheme.

[0077] 2) A two-layer reactive power distribution optimization method is proposed, which simultaneously considers global controllability, the support capacity of local weak nodes, and avoids redundant distribution.

[0078] Based on the voltage support capability analytical evaluation index proposed above, this embodiment constructs a two-layer dynamic reactive power distribution optimization framework with "global-local" collaboration. This framework can simultaneously improve the overall controllability of the system and provide precise voltage support for key weak areas. In the upper-layer optimization, the goal is to maximize the global controllability of the system. Using the spectral characteristics of the empirical controllability covariance matrix, a set of key compensation buses that significantly impact the overall voltage regulation capability of the system is selected. The lower-layer optimization aims to maximize the dynamic support capability of key weak nodes. Response similarity serves as a constraint condition to suppress redundancy in the regulation effects between candidate compensation points, thereby improving the complementarity and efficiency of the distribution. Through this two-layer optimization, this embodiment obtains an optimal dynamic reactive power compensation configuration scheme that combines economy, improved dynamic performance, and complementary regulation functions.

[0079] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0080] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0081] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 using the methods described in the embodiments of this application. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0082] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0083] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0084] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0085] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0086] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0087] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0088] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0089] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0090] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0091] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0092] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0094] The units described above 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or 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 multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for dynamic reactive power distribution optimization of a power electronic converter system, characterized in that, The method includes the following steps: Based on the principle of empirical controllability covariance, a first index is constructed to evaluate the ability of candidate compensation points to support transient voltage at key weak nodes, and a second index is constructed to evaluate the similarity of dynamic response patterns among different candidate compensation points. A two-layer optimization model is constructed, comprising an upper-layer optimization model and a lower-layer optimization model; wherein... The upper-level optimization model aims to maximize the global controllability of the system by selecting a set of key compensation points from all candidate compensation points. The lower-level optimization model aims to maximize the transient voltage support capability for the key weak nodes, and uses the second index as a constraint to determine the final reactive power compensation point layout scheme from the set of key compensation points. Solve the two-layer optimization model to output the optimal dynamic reactive power compensation point layout scheme.

2. The method according to claim 1, characterized in that, Constructing the first metric and the second metric includes: Multiple sets of reactive step disturbance signals with different amplitudes are applied to each candidate compensation point; The system acquires voltage time-domain response data under each group of disturbances, and constructs an empirical controllability covariance matrix corresponding to each candidate compensation point based on the voltage time-domain response data. Based on the aforementioned empirical controllability covariance matrix, a first index is calculated to quantify the dynamic support capability of candidate compensation points for a set of key weak nodes, and a second index is calculated to quantify the similarity of dynamic response patterns between any two candidate compensation points.

3. The method according to claim 2, characterized in that, The first indicator is the transient voltage support effect indicator of key weak nodes, which is obtained by projecting the empirical controllability covariance matrix corresponding to candidate compensation point i onto the subspace composed of key weak nodes and calculating the trace of the projection matrix.

4. The method according to claim 2, characterized in that, The second indicator is the response similarity indicator, which is obtained by calculating the Frobenius inner product of the empirical controllability covariance matrices of two candidate compensation points i and j in the critical weak node subspace.

5. The method according to claim 1, characterized in that, The objective function of the upper-level optimization model is to maximize the determinant of the combined empirical controllability covariance matrix corresponding to the selected set of key compensation points, or equivalently minimize its negative logarithmic determinant, so as to select the candidate point combination that contributes the most to improving the overall dynamic controllability of the system.

6. The method according to claim 1, characterized in that, The objective function of the lower-level optimization model is to maximize the sum of the transient voltage support indicators of all selected compensation points for the set of key weak nodes. Its constraints include: 1) Global controllability constraint: The global controllability of the lower-level optimization scheme is required to be no less than the threshold determined by the upper-level optimization scheme; 2) Response similarity constraint: The average value of the response similarity index between any two selected compensation points in the scheme shall not exceed the preset threshold in order to avoid redundant point placement.

7. The method according to claim 2, characterized in that, The empirical controllability covariance matrix is ​​constructed as follows: For the h-th reactive step disturbance applied at candidate compensation point i, an empirical energy matrix is ​​constructed based on the system voltage state increment vector; By performing weighted averaging and normalization on the empirical energy matrices corresponding to multiple disturbances of different amplitudes, the empirical controllability covariance matrix of the candidate compensation point i is obtained.

8. The method according to any one of claims 1 to 7, characterized in that, The method also includes an initial step: determining the set of candidate dynamic reactive power compensation buses for the system and the set of key weak nodes that need to be supported.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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