Damping cooperative control method, device and equipment based on multi-mode oscillation suppression

Abstract

This application discloses a damping-based coordinated control method, device, and equipment for multi-mode oscillation suppression, relating to the field of power system control technology. The method includes: determining target additional damping control parameters based on the power level, real-time short-circuit ratio, and operating state index of the target power station under current operating conditions; determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode; solving a task allocation optimization model based on the state information, mode demand information, and grid parameters of the generation units in the target power station to determine the resource allocation ratio of the generation units under the oscillation mode; generating coordinated control commands for the generation units based on the resource allocation ratio, target additional damping control parameters, and dynamic suppression weight of the oscillation mode; and performing additional damping control on the generation units based on the coordinated control commands. Through this method, the suppression capability of multi-mode oscillations can be improved under all operating conditions, ensuring the safe and stable operation of the power grid.

Description

Technical Field

[0001] This application relates to the field of power system control technology, and in particular to a damping coordinated control method, device and equipment based on multi-mode oscillation suppression. Background Technology

[0002] With the large-scale centralized grid connection of new energy sources such as wind power and photovoltaics, the power system's electronic characteristics are becoming increasingly prominent. New energy power plants experience high grid connection impedance, weak damping, and frequent fluctuations in operating conditions, making them highly susceptible to stability issues such as wideband oscillations, which seriously threaten the safe and stable operation of the power grid. Additional damping is currently an important control method for suppressing grid connection oscillations of new energy sources and improving system damping, and it is widely used in new energy power plants and flexible DC transmission grid connection scenarios.

[0003] Traditional additional damping control strategies currently used in engineering applications all employ fixed-gain control parameters tuned for a single rated operating condition. These strategies are simple in logic, easy to implement, and can adapt to the basic oscillation suppression requirements of the grid's rated steady-state operation, showing some applicability in traditional synchronous machine-dominated power grids. However, with the integration of a high proportion of renewable energy sources, the operating characteristics of the power grid and the output characteristics of power plants have fundamentally changed. The adaptability of the fixed parameters used in traditional additional damping control strategies has significantly decreased, failing to meet the stability control requirements of new power systems.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a damping coordinated control method, device, and equipment based on multi-mode oscillation suppression, aiming to solve the technical problem that the traditional additional damping control strategy in the prior art uses fixed control parameters, which cannot adapt to the full operating conditions of the station and has low control stability.

[0006] To achieve the above objectives, this application provides a damping cooperative control method based on multi-mode oscillation suppression, the method comprising: Based on the target power level, real-time short-circuit ratio, and operating status index of the target power station under the current operating conditions, the target additional damping control parameters under the current operating conditions are determined. The oscillation pattern of the target station is detected, and the dynamic suppression weight of the oscillation pattern is determined based on the comprehensive risk index of the oscillation pattern. A task allocation optimization model is constructed, and the task allocation optimization model is solved based on the state information, mode requirement information and grid parameters of the power generation units in the target power station to determine the resource allocation ratio of the power generation units in the oscillation mode. Based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode, a coordinated control command is generated. The power generation unit is subjected to additional damping control based on the aforementioned coordinated control command.

[0007] In one embodiment, the step of determining the target additional damping control parameters under the current operating conditions based on the target power level, real-time short-circuit ratio, and operating state index of the target power station includes: Based on the target power station's power level, real-time short-circuit ratio, and operating status index under the current operating conditions, a feature vector under the current operating conditions is generated; Based on the mapping database, the target additional damping control parameters corresponding to the feature vector of the current working condition are determined. The mapping database stores sample feature vectors under different working conditions and the additional damping control parameters corresponding to the sample feature vectors.

[0008] In one embodiment, the step of determining the target additional damping control parameters corresponding to the feature vector of the current operating condition based on a mapping database includes: Based on the weighted Euclidean distance between the feature vector of the current working condition and the feature vector of the sample in the mapping database, the feature vector of the neighboring sample is determined. Obtain additional damping control parameters from the neighboring sample feature vectors in the mapping database; Based on the additional damping control parameters of the feature vectors of the neighboring samples, the target additional damping control parameters corresponding to the feature vectors of the current operating condition are determined.

[0009] In one embodiment, the step of determining the target additional damping control parameter corresponding to the feature vector of the current operating condition based on the additional damping control parameter of the feature vector of the neighboring samples includes: The additional damping control parameters of the neighboring sample feature vectors are weighted and interpolated to obtain the initial additional damping control parameters; Based on a preset smoothing coefficient, the initial additional damping control parameters are smoothed to obtain the target additional damping control parameters corresponding to the feature vector of the current operating condition.

[0010] In one embodiment, the step of determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode further includes: Calculate the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode; Obtain the first correspondence between amplitude indicators, damping indicators, growth rate indicators, frequency risk indicators, and comprehensive risk index; Based on the first correspondence and the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode, the comprehensive risk index of the oscillation mode is determined.

[0011] In one embodiment, the step of determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode includes: Obtain the weight distribution adjustment coefficient and the comprehensive risk index, as well as the second correspondence between the weight distribution adjustment coefficient and the dynamic suppression weight; Based on the comprehensive risk index of the oscillation mode, the weight distribution adjustment coefficient, and the second correspondence, the dynamic suppression weight of the oscillation mode is determined.

[0012] In one embodiment, the steps of constructing a task allocation optimization model include: Obtain the third correspondence between resource allocation ratio, suppression effectiveness and suppression effect index, and the fourth correspondence between resource allocation ratio, damping power, average load rate and load balance degree. Also obtain the fifth correspondence between resource allocation ratio and robustness index, and the sixth correspondence between suppression effect index, load balance, robustness index and comprehensive utility. Based on the third, fourth, fifth, and sixth correspondences, a target optimization function is constructed. Based on the objective optimization function and objective constraints, a task allocation optimization model is obtained. The objective constraints include power generation unit resource constraints, oscillation mode coverage constraints, and capacity constraints.

[0013] In one embodiment, after the step of performing additional damping control on the power generation unit based on the cooperative control command, the method further includes: The suppression effect of the power generation unit under the cooperative control command is evaluated to obtain the suppression evaluation value of the power generation unit; When the suppression evaluation value of the power generation unit is less than the preset evaluation threshold, the resource allocation ratio of the power generation unit in the oscillation mode is re-determined; Based on the redefined resource allocation ratio of the power generation unit in the oscillation mode, the process returns to the step of generating a coordinated control command based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode.

[0014] Furthermore, to achieve the above objectives, this application also proposes a damping cooperative control device based on multi-mode oscillation suppression, which includes: The operating condition adaptive module is used to determine the target additional damping control parameters under the current operating condition based on the power level, real-time short-circuit ratio and operating state index of the target power station under the current operating condition. The collaborative suppression module is used to detect the oscillation mode of the target station and determine the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode. The global optimization module is used to construct a task allocation optimization model. Based on the state information, mode requirement information and grid parameters of the power generation units in the target power station, the task allocation optimization model is solved to determine the resource allocation ratio of the power generation units in the oscillation mode. The damping control module is used to generate coordinated control commands based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode. The damping control module is also used to perform additional damping control on the power generation unit based on the cooperative control command.

[0015] Furthermore, to achieve the above objectives, this application also proposes a damping cooperative control device based on multi-mode oscillation suppression. The damping cooperative control device based on multi-mode oscillation suppression includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the damping cooperative control method based on multi-mode oscillation suppression as described above.

[0016] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the damping cooperative control method based on multi-mode oscillation suppression as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the damped cooperative control method based on multi-mode oscillation suppression as described above.

[0018] This application provides a damping-coordinated control method based on multi-mode oscillation suppression. Based on the power level, real-time short-circuit ratio, and operating state index of the target power station under current operating conditions, the method determines the target additional damping control parameters for the current operating conditions. It detects the oscillation modes of the target power station and determines the dynamic suppression weights of the oscillation modes based on the comprehensive risk index of the oscillation modes. A task allocation optimization model is constructed, and the model is solved based on the state information, mode demand information, and grid parameters of the power generation units in the target power station to determine the resource allocation ratio of the power generation units under oscillation modes. Based on the resource allocation ratio of the power generation units under oscillation modes, the target additional damping control parameters, and the dynamic suppression weights of the oscillation modes, a coordinated control command is generated. The method then applies additional damping control to the power generation units based on the coordinated control command. This application adaptively calculates the target additional damping control parameters suitable for the current operating condition, ensuring excellent damping performance under different operating conditions. It adapts to the full operating conditions of the power station, guaranteeing the safe and stable operation of the power grid. Furthermore, it calculates dynamic suppression weights based on the comprehensive risk index of different oscillation modes, taking all oscillation modes into account. It utilizes the dynamic suppression weights of different oscillation modes for coordinated suppression, avoiding suppression conflicts between different modes. It can effectively suppress oscillations even in multi-mode oscillation scenarios, improving the suppression capability of multi-mode oscillations and maintaining the stability of the power grid. In addition, it uses a task allocation optimization model to find the optimal resource allocation ratio for different oscillation modes. During the oscillation coordinated suppression process, resource scheduling is performed according to the optimal resource allocation ratio, achieving optimal matching between suppression tasks and execution resources for different oscillation modes, maximizing the overall suppression effect, and further ensuring the safe and stable operation of the power grid. This solves the technical problem of traditional additional damping control strategies using fixed control parameters, which cannot adapt to the full operating conditions of the power station and have low control stability. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the damping cooperative control method based on multi-mode oscillation suppression in this application. Figure 2 A schematic diagram of the overall architecture of the damping cooperative control method based on multi-mode oscillation suppression provided in Embodiment 1 of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the damping cooperative control method based on multi-mode oscillation suppression of this application; Figure 4 This is a schematic diagram of the module structure of the damping cooperative control device based on multi-mode oscillation suppression according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the damping cooperative control method based on multi-mode oscillation suppression in the embodiments of this application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] Currently, fixed control parameters cannot adapt to all operating conditions of power plants, resulting in poor adaptability and low control stability. Daily operation of renewable energy power plants involves power output fluctuations such as full-load, limited-load, and light-load conditions. The grid-connected power grid also experiences structural switching between strong and weak grids, and frequently faces transient scenarios such as grid fault ride-through and disturbance recovery. The system damping, oscillation modes, and grid impedance characteristics vary greatly under different operating conditions. In traditional additional damping control, fixed gain parameters have significant adaptation blind spots: insufficient damping gain under light-load conditions in weak grids leads to lag in oscillation suppression and an inability to effectively quell oscillations; excessive gain under full-load conditions in strong grids easily leads to control overshoot, power oscillations, and the risk of secondary oscillations; and insufficient parameter response speed under fault transient conditions fails to quickly suppress severe transient oscillations, easily causing power plant disconnection accidents.

[0026] In view of this, this application provides a damping cooperative control method based on multi-mode oscillation suppression. Adaptive control parameters are calculated according to different operating conditions, enabling the additional damping controller to automatically adapt to the complete operating range from zero power to full power generation, from strong grids to extremely weak grids, and from steady state to various transient states. Furthermore, it can take into account all oscillation modes, achieving intelligent cooperative suppression of multiple oscillation modes, avoiding control conflicts, and improving the suppression capability of multi-mode oscillations. In addition, the damping control resources of all power generation units within the power station are integrated into a flexibly schedulable resource pool, achieving optimal matching between suppression tasks and execution resources. This enhances the suppression capability of multi-mode oscillations under all operating conditions, ensuring the safe and stable operation of the power grid.

[0027] The executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone; or an electronic device capable of performing the above functions, a damping cooperative control device based on multi-mode oscillation suppression, etc. This embodiment does not specifically limit it. The following uses a damping cooperative control device based on multi-mode oscillation suppression as an example to describe this embodiment and the following embodiments.

[0028] This application provides a damped collaborative control method based on multi-mode oscillation suppression, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the damping cooperative control method based on multi-mode oscillation suppression in this application.

[0029] In this embodiment, the damping cooperative control method based on multi-mode oscillation suppression includes steps S10~S50: Step S10: Based on the power level, real-time short-circuit ratio and operating status index of the target power station under the current operating conditions, determine the target additional damping control parameters under the current operating conditions. It should be noted that the current operating condition refers to the actual operating condition at the current moment, such as: light load operating condition of a weak grid, full power generation operating condition of a strong grid, or transient fault operating condition. This embodiment does not make specific limitations on this. The target power station refers to the renewable energy power station currently being controlled.

[0030] In practice, the operating conditions of the target site are constantly changing. Each time a change in operating conditions is detected, steps S10 to S50 will be re-executed.

[0031] In one feasible implementation, step S10 may include steps S101 to S102: Step S101: Based on the power level, real-time short-circuit ratio and operating status index of the target power station under the current operating conditions, generate the feature vector under the current operating conditions. It should be noted that in this embodiment, power level, real-time short-circuit ratio, and operating state index are selected as the dimensions that most significantly affect oscillation characteristics and additional damping control performance to construct multi-dimensional features. Among them, power level That is, the standardized power level. , This is the reference value for the rated active power of the power station. The actual active power output of the power station, and the real-time short-circuit ratio. Operating condition index can be obtained through impedance measurement or estimation. Used to quantify transients such as fault ride-through and voltage anomalies. , The input characteristic mapping function represents the additional damping controller of the new energy grid-connected inverter. Indicates the voltage deviation at the grid connection point. This indicates the frequency deviation of the power system (currently connected to the power grid). .

[0032] Understandably, this involves calculating the power level of the target power station. Real-time short-circuit ratio and operating status index Based on the power level of the target power station Real-time short-circuit ratio and operating status index The constructed multidimensional features can be considered as three-dimensional feature vectors. ,Right now .

[0033] In practice, feature vectors are usually calculated according to a pre-set period, for example, the feature vector of the current running point is calculated every 10ms.

[0034] Step S102: Based on the mapping database, determine the target additional damping control parameters corresponding to the feature vector of the current working condition. The mapping database stores the sample feature vectors under different working conditions and the additional damping control parameters corresponding to the sample feature vectors.

[0035] It should be noted that the mapping database is a pre-built prior knowledge base that stores sample feature vectors under different operating conditions and the corresponding additional damping control parameters. The sample feature vectors refer to typical feature vectors generated according to the dimensions that most affect the oscillation characteristics and the performance of the additional damping control. It can be based on the power level of the most representative / most frequently occurring target power station under historical operating conditions. Real-time short-circuit ratio and operating status index Determined, that is Additional damping control parameters At least include the proportional gain parameter Integral gain parameter Phase compensation parameters ,at this time .

[0036] Additionally, it should be noted that the additional damping control parameters stored in the mapping database are the optimal additional damping control parameters corresponding to the feature vectors of each sample found from electromagnetic transient simulation data or historical operating data. In practical implementation, optimization can be achieved using a genetic algorithm.

[0037] Understandably, a mapping database is constructed based on the correspondence between sample feature vectors and additional damping control parameters. This mapping database can be configured according to... The sample feature vector and the corresponding additional damping control parameters are stored in the form of a .

[0038] In one feasible implementation, step S102 may include steps S1021 to S1023: Step S1021: Determine the feature vectors of neighboring samples based on the weighted Euclidean distance between the feature vector of the current working condition and the feature vector of the samples in the mapping database; It is understandable that the weighted Euclidean distance is the Euclidean distance obtained after weighted calculation. After obtaining the feature vector of the current working condition, the weighted Euclidean distance between the feature vector and the feature vectors of all samples in the mapping database is calculated, as shown below:

[0039] In the formula, Indicates the weighted Euclidean distance. , , These represent the target power station's power level, real-time short-circuit ratio, and operating status index, respectively, in the feature vector. , , These represent the power level, real-time short-circuit ratio, and operating status index of the target power station under historical operating conditions, respectively, in the sample feature vector. , , The weights represent the sensitivity of different dimensions to the impact of additional damping control performance, and can be determined through sensitivity analysis.

[0040] It should be understood that these sample feature vectors are sorted in ascending order according to the weighted Euclidean distance, and then selected sequentially from front to back according to the sorting order. The feature vectors of samples with smaller weighted Euclidean distances are used as the feature vectors of neighboring samples. The specific value can be set according to actual needs, and this embodiment does not impose specific limitations on it.

[0041] Step S1022: Obtain additional damping control parameters for the feature vectors of neighboring samples from the mapping database; It should be noted that the corresponding additional damping control parameters can be found in the mapping database for each neighboring sample feature vector.

[0042] Step S1023: Based on the additional damping control parameters of the feature vectors of neighboring samples, determine the target additional damping control parameters corresponding to the feature vectors of the current working condition.

[0043] It is understandable that the target additional damping control parameter is the optimal additional damping control parameter under the current operating condition. In this embodiment, the optimal additional damping control parameter for the current operating condition is calculated based on the additional damping control parameters of the feature vectors of neighboring samples.

[0044] In one feasible implementation, step S1023 may include: performing weighted interpolation on the additional damping control parameters of the feature vectors of neighboring samples to obtain initial additional damping control parameters; and smoothing the initial additional damping control parameters based on a preset smoothing coefficient to obtain the target additional damping control parameters corresponding to the feature vector of the current working condition.

[0045] In practical implementation, additional damping control parameters are applied to the feature vectors of neighboring samples. Weighted interpolation is performed to obtain adaptive parameters applicable to the current operating condition. These adaptive parameters are the initially obtained additional damping control parameters, i.e., the initial additional damping control parameters. To prevent disturbances caused by parameter jumps during operating condition switching, a first-order inertial element is used to smooth the initial additional damping control parameters.

[0046] It is understandable that the historical additional damping control parameters are the target additional damping control parameters corresponding to the eigenvector of the previous time step. The calculation formula used for smoothing is shown below:

[0047] In the formula, This represents the target additional damping control parameters at the current moment. This represents the initial additional damping control parameters at the current moment. This represents the target additional damping control parameters at the previous moment. This represents the smoothing coefficient. It can be a smaller value (e.g., 0.05) in steady state, and can be increased (e.g., 0.3) in transient states such as faults to enable a faster response.

[0048] In this embodiment, a highly sensitive three-dimensional operating condition feature vector composed of power level, real-time short-circuit ratio, and operating state index is defined. The feature space is traversed offline to find the optimal additional damping control parameters for each typical operating condition, forming a priori knowledge base. During online operation, multiple nearest sample points are matched in the priori knowledge base using weighted Euclidean distance, and their corresponding additional damping control parameters are weighted and interpolated to generate the optimal additional damping control parameters adapted to the current operating condition in real time. At the same time, a first-order inertial element is introduced to avoid control command jumps caused by operating condition switching, thus balancing the smoothness of the steady-state process and the rapid response of the transient process.

[0049] It should be understood that through parameter adaptive mapping, the additional damping control parameters can automatically adapt to the complete operating range from zero power to full power generation, from strong grid to extremely weak grid, and from steady state to various transient states, and always maintain excellent damping performance.

[0050] Step S20: Detect the oscillation mode of the target station, and determine the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode. It should be noted that oscillating component bandpass and power frequency bandstop filters are used for parallel extraction. The oscillation components of each preset frequency channel / adaptive frequency channel are determined. There are several oscillation modes. For each oscillation mode, its comprehensive risk index (the degree of harm obtained from comprehensive assessment) is calculated. The comprehensive risk index is used to determine the dynamic suppression weight of each oscillation mode. The dynamic suppression weight is used to characterize the suppression priority of each oscillation mode.

[0051] In one feasible implementation, prior to determining the dynamic suppression weight of an oscillation mode based on its comprehensive risk index, the process may include: calculating the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode; obtaining a first correspondence between the amplitude index, damping index, growth rate index, frequency risk index, and comprehensive risk index; and determining the comprehensive risk index of the oscillation mode based on the first correspondence and the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode.

[0052] It should be noted that the amplitude indicator Used to reflect the magnitude of oscillation energy, damping index Damping ratios estimated using the Prony algorithm or Hilbert transform show that negative damping poses a significant risk, and the growth rate index... Frequency risk indicator is used to reflect the increasing trend of the amplitude of oscillations over a short period of time. Used to assess whether the current frequency is close to the natural frequency of the shaft system of nearby thermal power units. The risks are as follows:

[0053] In the formula, Indicates frequency risk indicators, Indicates the first The weight of the Taiwanese unit For frequency risk bandwidth, Indicates the current frequency. This indicates the natural frequency of the shaft system of a nearby thermal power unit.

[0054] Additionally, it should be noted that the primary correspondence between the amplitude indicator, damping indicator, growth rate indicator, frequency risk indicator, and the comprehensive risk index refers to the calculation formula of the comprehensive risk index, as shown below:

[0055] In the formula, Indicates the first A comprehensive risk index for each oscillation pattern. , , , They represent the first Amplitude, damping, growth rate, and frequency risk indicators for each oscillation pattern. , , , Indicates the weight.

[0056] Understandably, for each oscillation pattern, the amplitude index, damping index, growth rate index, and frequency risk index are calculated in real time. The amplitude index, damping index, growth rate index, and frequency risk index are then substituted into the first correspondence mentioned above to calculate the comprehensive risk index.

[0057] In one feasible implementation, the step of determining the dynamic suppression weight of an oscillation mode based on the comprehensive risk index of the oscillation mode may include: obtaining the weight distribution adjustment coefficient and a second correspondence between the comprehensive risk index, the weight distribution adjustment coefficient and the dynamic suppression weight; and determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode, the weight distribution adjustment coefficient and the second correspondence.

[0058] It should be noted that the weight distribution adjustment coefficient is used to adjust the sharpness of the weight distribution. Generally speaking, the greater the harm (risk) of the oscillation mode, the higher the dynamic suppression weight is assigned, and the channel gain in the corresponding additional damping control parameter is also increased proportionally.

[0059] Understandably, for The weight vector obtained by Softmax normalizing the comprehensive risk index of each oscillation mode is the dynamic suppression weight of each oscillation mode. The second correspondence between the comprehensive risk index, the weight distribution adjustment coefficient, and the dynamic suppression weight, i.e., the calculation formula for the dynamic suppression weight, is shown below:

[0060] In the formula, Indicates the first Dynamic suppression weights for each oscillation mode. Indicates the first A comprehensive risk index for each oscillation pattern. This represents the weight distribution adjustment coefficient. This indicates the number of oscillation modes. Substituting the comprehensive risk index and weight distribution adjustment coefficient of each oscillation mode into the second correspondence mentioned above, the dynamic suppression weight of each oscillation mode is calculated.

[0061] In this embodiment, a four-dimensional evaluation system is constructed that integrates oscillation amplitude, damping ratio, growth rate and shaft resonance risk. The comprehensive risk index is transformed into a dynamic suppression weight, which can automatically tilt resources toward the most risky oscillation mode based on the dynamic suppression weight.

[0062] It should be understood that through multi-dimensional risk assessment and dynamic weight allocation, the system can automatically identify and focus on the oscillation mode with the greatest current risk, while taking other modes into account. This achieves intelligent and coordinated suppression of multiple oscillation modes, avoids control conflicts, and improves the governance capabilities of complex oscillation scenarios.

[0063] Step S30: Construct a task allocation optimization model. Based on the state information, mode requirement information and grid parameters of the power generation units in the target power station, solve the task allocation optimization model to determine the resource allocation ratio of the power generation units in the oscillation mode. It should be noted that renewable energy power plants typically have multiple power generation units, the specific number of which is determined based on actual conditions. The task allocation optimization model refers to the optimization model used to assign tasks to each power generation unit. By solving the model, the optimal match between suppressing tasks and execution resources can be found.

[0064] Additionally, it should be noted that the state information refers to the state of the power generation unit, the mode requirement information refers to the requirement of the power generation unit for the oscillation mode, and the grid parameters are the relevant parameters of the power generation units within the grid, such as: the total active power of all power generation units within the grid, the total predicted active power of all power generation units within the grid, the grid average voltage, the grid total installed capacity, the grid total reactive power, and the grid average current. Usually, the parameters involved in the solution process can be selected, and this embodiment does not make specific limitations on this.

[0065] In one feasible implementation, the steps of constructing a task allocation optimization model include: obtaining a third correspondence between resource allocation ratio, suppression effectiveness, and suppression effect index, and a fourth correspondence between resource allocation ratio, damping power, average load rate, and load balance; obtaining a fifth correspondence between resource allocation ratio and robustness index, and a sixth correspondence between suppression effect index, load balance index, robustness index, and comprehensive utility; constructing an objective optimization function based on the third, fourth, fifth, and sixth correspondences; and obtaining a task allocation optimization model based on the objective optimization function and objective constraints, wherein the objective constraints include power generation unit resource constraints, oscillation mode coverage constraints, and capacity constraints.

[0066] It should be noted that the resource allocation ratio refers to the resource allocation ratio of the power generation unit in the oscillation mode, i.e., the ratio of the first generation unit to the second generation unit. Each power generation unit allocates its available control resources to suppressing the first The proportion of each oscillation mode.

[0067] The third correspondence between resource allocation ratio, suppression effectiveness, and suppression effect index, i.e., the calculation formula for the suppression effect index, is as follows:

[0068] In the formula, Indicators of inhibitory effect Indicates the first The power generation unit in the first Resource allocation ratio under each oscillation mode Indicates the first The power generation unit for the first The suppression efficacy of each oscillation mode, Indicates the number of oscillation modes.

[0069] The fourth correspondence between resource allocation ratio, damping power, average load rate, and load balance degree, namely the calculation formula for load balance degree, is as follows:

[0070] In the formula, Indicates load balancing degree. Indicates the first The power generation unit in the first Resource allocation ratio under each oscillation mode Indicates suppression of the first Damping power required for each oscillation mode Indicates the average load factor. Indicates the first Available damping capacity of each power generation unit.

[0071] The fifth correspondence between resource allocation ratios and robustness indicators, i.e., the calculation formula for robustness indicators, is shown below:

[0072] In the formula, Indicates robustness index, Indicates the first The power generation unit in the first Resource allocation ratio under each oscillation mode.

[0073] The sixth correspondence between the suppression effect index, load balancing degree, robustness index, and overall utility, i.e., the formula for calculating overall utility, is shown below:

[0074] In the formula, Indicates overall utility. , , These represent the suppression effect index, load balancing index, and robustness index, respectively. , , This represents the weight, and the specific value is not specified.

[0075] Therefore, the objective optimization function can be constructed as follows:

[0076] In the formula, Indicates overall utility. , , These represent the suppression effect index, load balancing index, and robustness index, respectively. , , Indicates weight, Assign matrices to tasks. , The number of power generation units, This represents the number of oscillation modes.

[0077] Furthermore, corresponding numerical constraints are added to the objective function, namely objective constraints, which include power generation unit resource constraints, oscillation mode coverage constraints, and capacity constraints.

[0078] The resource constraints of the power generation unit are as follows:

[0079] The oscillation mode coverage constraints are shown below:

[0080] The capacity constraints are as follows:

[0081] Among the above objective constraints, Indicates the first The power generation unit in the first Resource allocation ratio under each oscillation mode Indicates the first Available damping capacity of each power generation unit, , , The number of power generation units, This represents the number of oscillation modes.

[0082] Understandably, by combining the objective optimization function and objective constraints, a final task allocation optimization model is formed. In each decision cycle, the station-level collaborative controller (which can be set up in a damped collaborative control system based on multi-mode oscillation suppression) collects the state information, mode requirement information, and grid parameters of each power generation unit, solves the task allocation optimization model, and obtains an optimal task allocation matrix. The elements in the task allocation matrix are the elements of the task allocation model. Each power generation unit allocates its available control resources to suppressing the first The proportion of the oscillation mode, that is, the th oscillation mode The power generation unit in the first Resource allocation ratio under each oscillation mode .

[0083] In this embodiment, the oscillation suppression problem of multiple units and multiple modes in the station is abstracted into a constrained "task allocation matrix" optimization problem. By simultaneously optimizing the three objectives of suppression effect, load balancing and system robustness, and taking into account actual constraints such as electrical coupling degree and unit capacity, the globally optimal dynamic capacity constraint scheduling of damping control tasks in distributed resources is realized.

[0084] It should be understood that by using the task allocation optimization model, the damping control resources of all power generation units in the power station are integrated into a flexibly schedulable resource pool, achieving the optimal matching between suppression tasks and execution resources. This not only maximizes the overall suppression effect but also achieves load balancing among the power generation units.

[0085] Step S40: Generate a coordinated control command based on the resource allocation ratio of the power generation unit in oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode. It should be noted that the coordinated control command refers to the command used for coordinated control of damping. It is sent to each power generation unit, usually in the form of data packets.

[0086] It is understandable that, in addition to resource allocation ratios, target additional damping control parameters, and dynamic suppression weights, the coordinated control instructions also include the current frequency and the power limits allocated by the power generation unit to each oscillation mode, as shown below:

[0087] In the formula, Indicates the first Coordinated control commands for individual power generation units Indicates the current frequency. This indicates the target additional damping control parameters. Indicates the first The power generation unit in the first Resource allocation ratio under each oscillation mode Indicates the first Dynamic suppression weights for each oscillation mode. Indicates the first The power generation unit is allocated to the first Power limits for each oscillation mode , Indicates the first Available damping capacity of each power generation unit.

[0088] Step S50: Perform additional damping control on the power generation unit based on the coordinated control command.

[0089] Understandably, the first After receiving the coordination command, each power generation unit executes each task independently, using parameters. Generate frequency The damping current reference value, the first Each power generation unit targets frequency. The damping current reference value is ; Calculate the contribution of the power generation unit to the oscillation mode, the first The power generation unit for the first The contribution of each oscillation mode is The contribution of the superimposed power generation unit to all oscillation modes, the first The total contribution of each power generation unit is and ensure ,Will The superimposed current loop reference command (the target command value input to the inverter current closed-loop control loop determines the amplitude, phase, and control components of the final grid-connected current output by the inverter).

[0090] refer to Figure 2In this embodiment, a two-tiered collaborative control architecture of centralized decision-making and distributed execution is adopted. Specifically, a two-tiered collaborative architecture is designed with centralized situational awareness, strategy optimization, and resource scheduling at the power plant level, and distributed execution of precise current injection at the generation unit level. This architecture ensures both the coordination and optimality of global decision-making while retaining the reliability and flexibility of distributed execution. Furthermore, a clear power limit management mechanism is established to ensure that damping control does not affect the normal power generation plan and equipment safety of the power plant, thus ensuring grid stability while also considering the economic efficiency of power plant operation.

[0091] This embodiment provides a damping cooperative control method based on multi-mode oscillation suppression. Based on the target power station's power level, real-time short-circuit ratio, and operating state index under the current operating conditions, the target additional damping control parameters are determined. The oscillation modes of the target power station are detected, and the dynamic suppression weights of the oscillation modes are determined based on the comprehensive risk index of the oscillation modes. A task allocation optimization model is constructed, and based on the state information, mode requirement information, and grid parameters of the power generation units in the target power station, the task allocation optimization model is solved to determine the resource allocation ratio of the power generation units under the oscillation modes. Based on the resource allocation ratio of the power generation units under the oscillation modes, the target additional damping control parameters, and the dynamic suppression weights of the oscillation modes, cooperative control commands are generated. Additional damping control is then applied to the power generation units based on the cooperative control commands. This embodiment calculates adaptive control parameters based on different operating conditions, enabling the additional damping controller to automatically adapt to the complete operating range from zero power to full power generation, from strong grid to extremely weak grid, and from steady state to various transient states, always maintaining excellent damping performance. It can also take into account all oscillation modes, achieving intelligent collaborative suppression of multiple oscillation modes, avoiding control conflicts, and improving the ability to manage complex oscillation scenarios. In addition, the damping control resources of all power generation units in the substation are integrated into a flexibly dispatchable resource pool, achieving optimal matching between suppression tasks and execution resources. This improves the ability to suppress multi-mode oscillations under all operating conditions, ensuring the safe and stable operation of the power grid.

[0092] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the above embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S50 may be followed by steps S601-S602: Step S601: Evaluate the suppression effect of the power generation unit under the cooperative control command to obtain the suppression evaluation value of the power generation unit; It should be noted that after the coordinated control command is issued to each power generation unit, it is executed by the power generation unit.

[0093] Understandably, during the operation of the power generation unit, the oscillation suppression effect and status of each power generation unit are continuously monitored. The oscillation suppression effect is characterized using a suppression evaluation value, as shown below:

[0094] In the formula, Indicates the first The suppression evaluation value of each oscillation mode, Indicates the start time of triggering control. This represents the time difference between the current time and the starting time. Indicates the first A matrix consisting of the resource allocation ratios of each oscillation mode.

[0095] Step S602: When the suppression evaluation value of the power generation unit is less than the preset evaluation threshold, the resource allocation ratio of the power generation unit in the oscillation mode is re-determined in order to regenerate the collaborative control command.

[0096] It should be noted that the preset evaluation threshold is a pre-set threshold. If the suppression evaluation value of the power generation unit is less than the preset evaluation threshold, it means that the current task allocation has not reached the optimal level. At this time, the task allocation matrix is ​​recalculated, that is, the process returns to step S30 to re-solve the resource allocation ratio of the power generation unit in the oscillation mode, thereby regenerating the collaborative control command and sending it to the power generation unit to achieve closed-loop adaptation.

[0097] In addition, when the state of the power generation unit changes abruptly, it will also trigger the recalculation of the task allocation matrix, re-solve the resource allocation ratio of the power generation unit in the oscillation mode, and regenerate the collaborative control command to be sent to the power generation unit.

[0098] This embodiment provides a damping cooperative control method based on multi-mode oscillation suppression. It evaluates the suppression effect of power generation units under cooperative control commands, obtaining a suppression evaluation value for each unit. When the suppression evaluation value of a power generation unit is less than a preset evaluation threshold, the resource allocation ratio of the power generation unit in oscillation mode is re-determined to regenerate cooperative control commands. This embodiment integrates the damping control resources of all power generation units within the power station into a flexibly schedulable resource pool, achieving optimal matching between suppression tasks and execution resources. This maximizes the overall suppression effect, achieves load balancing among power generation units, and maintains suppression functionality through task reallocation when some power generation units fail, significantly improving control efficiency and robustness.

[0099] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the damping cooperative control method based on multi-mode oscillation suppression in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0100] This application also provides a damping cooperative control device based on multi-mode oscillation suppression, please refer to... Figure 4 The damping cooperative control device based on multi-mode oscillation suppression includes: The adaptive operating condition module 10 is used to determine the target additional damping control parameters under the current operating condition based on the power level, real-time short-circuit ratio and operating state index of the target power station under the current operating condition. The collaborative suppression module 20 is used to detect the oscillation mode of the target station and determine the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode. The global optimization module 30 is used to construct a task allocation optimization model. Based on the state information, mode requirement information and grid parameters of the power generation units in the target power station, the task allocation optimization model is solved to determine the resource allocation ratio of the power generation units in the oscillation mode. The damping control module 40 is used to generate coordinated control commands based on the resource allocation ratio of the power generation unit in oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode. The damping control module 40 is also used to perform additional damping control on the power generation unit based on the cooperative control command.

[0101] In one feasible implementation, the operating condition adaptive module 10 is further configured to generate a feature vector under the current operating condition based on the power level, real-time short-circuit ratio and operating status index of the target power station under the current operating condition. Based on the mapping database, the target additional damping control parameters corresponding to the feature vector of the current working condition are determined. The mapping database stores the sample feature vectors under different working conditions and the additional damping control parameters corresponding to the sample feature vectors.

[0102] In one feasible implementation, the working condition adaptive module 10 is further used to determine the feature vector of neighboring samples based on the weighted Euclidean distance between the feature vector of the current working condition and the feature vector of the samples in the mapping database. Obtain additional damping control parameters from the feature vectors of neighboring samples from the mapping database; Based on the additional damping control parameters of the feature vectors of neighboring samples, the target additional damping control parameters corresponding to the feature vectors of the current operating condition are determined.

[0103] In one feasible implementation, the working condition adaptive module 10 is also used to perform weighted interpolation on the additional damping control parameters of the feature vectors of neighboring samples to obtain the initial additional damping control parameters. Based on a preset smoothing coefficient, the initial additional damping control parameters are smoothed to obtain the target additional damping control parameters corresponding to the feature vector of the current working condition.

[0104] In one feasible implementation, the collaborative suppression module 20 is also used to calculate the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode. Obtain the first correspondence between amplitude indicators, damping indicators, growth rate indicators, frequency risk indicators, and comprehensive risk index; Based on the first correspondence and the amplitude, damping, growth rate, and frequency risk indicators of the oscillation mode, a comprehensive risk index for the oscillation mode is determined.

[0105] In one feasible implementation, the collaborative inhibition module 20 is also used to obtain the weight distribution adjustment coefficient and the comprehensive risk index, the second correspondence between the weight distribution adjustment coefficient and the dynamic inhibition weight; Based on the comprehensive risk index of oscillation mode, the weight distribution adjustment coefficient, and the second correspondence, the dynamic suppression weight of oscillation mode is determined.

[0106] In one feasible implementation, the global optimization module 30 is also used to obtain the third correspondence between resource allocation ratio, suppression effectiveness and suppression effect index, and the fourth correspondence between resource allocation ratio, damping power, average load rate and load balance degree, and to obtain the fifth correspondence between resource allocation ratio and robustness index, and the sixth correspondence between suppression effect index, load balance, robustness index and comprehensive utility. Based on the third, fourth, fifth, and sixth correspondences, construct the objective optimization function; Based on the objective optimization function and objective constraints, a task allocation optimization model is obtained. The objective constraints include power generation unit resource constraints, oscillation mode coverage constraints, and capacity constraints.

[0107] In one feasible implementation, the damping control module 40 is also used to evaluate the suppression effect of the power generation unit under the cooperative control command, and obtain the suppression evaluation value of the power generation unit. When the suppression evaluation value of the power generation unit is less than the preset evaluation threshold, the resource allocation ratio of the power generation unit in the oscillation mode is re-determined; Based on the redefined resource allocation ratio of the power generation unit in oscillation mode, return to the step of generating coordinated control commands based on the resource allocation ratio of the power generation unit in oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode.

[0108] The damping coordinated control device based on multi-mode oscillation suppression provided in this application employs the damping coordinated control method based on multi-mode oscillation suppression described in the above embodiments. This solves the technical problem that traditional additional damping control strategies use fixed control parameters, cannot adapt to all operating conditions of the station, and suffer from low control stability. Compared with the prior art, the beneficial effects of the damping coordinated control device based on multi-mode oscillation suppression provided in this application are the same as those of the damping coordinated control method based on multi-mode oscillation suppression provided in the above embodiments. Furthermore, other technical features in the damping coordinated control device based on multi-mode oscillation suppression are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0109] This application provides a damping cooperative control device based on multi-mode oscillation suppression. The damping cooperative control device based on multi-mode oscillation suppression includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the damping cooperative control method based on multi-mode oscillation suppression in the above embodiment 1.

[0110] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a damping cooperative control device based on multi-mode oscillation suppression suitable for implementing embodiments of this application. The damping cooperative control device based on multi-mode oscillation suppression in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The damping cooperative control device based on multi-mode oscillation suppression shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0111] like Figure 5As shown, the damping cooperative control device based on multi-mode oscillation suppression may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the damping cooperative control device based on multi-mode oscillation suppression. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the damping cooperative control device based on multi-mode oscillation suppression to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a damping cooperative control device based on multi-mode oscillation suppression with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0112] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0113] The damping coordinated control device based on multi-mode oscillation suppression provided in this application adopts the damping coordinated control method based on multi-mode oscillation suppression in the above embodiments, which can solve the technical problem that traditional additional damping control strategies use fixed control parameters, cannot adapt to the full operating conditions of the station, and have low control stability. Compared with the prior art, the beneficial effects of the damping coordinated control device based on multi-mode oscillation suppression provided in this application are the same as the beneficial effects of the damping coordinated control method based on multi-mode oscillation suppression provided in the above embodiments, and other technical features in this damping coordinated control device based on multi-mode oscillation suppression are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0114] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0115] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0116] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the damping cooperative control method based on multi-mode oscillation suppression in the above embodiments.

[0117] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0118] The aforementioned computer-readable storage medium may be included in a damping cooperative control device based on multi-mode oscillation suppression; or it may exist independently and not assembled into a damping cooperative control device based on multi-mode oscillation suppression.

[0119] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a damping cooperative control device based on multi-mode oscillation suppression, the damping cooperative control device based on multi-mode oscillation suppression performs the following actions: Based on the power level, real-time short-circuit ratio, and operating state index of the target power station under the current operating condition, it determines the target additional damping control parameters for the current operating condition; detects the oscillation mode of the target power station and determines the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode; constructs a task allocation optimization model and solves the model based on the state information, mode requirement information, and grid parameters of the power generation units in the target power station to determine the resource allocation ratio of the power generation units under the oscillation mode; generates cooperative control commands based on the resource allocation ratio of the power generation units under the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode; and performs additional damping control on the power generation units based on the cooperative control commands.

[0120] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0123] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned damping cooperative control method based on multi-mode oscillation suppression. This solves the technical problem that traditional additional damping control strategies use fixed control parameters, cannot adapt to all operating conditions of the station, and suffer from low control stability. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the damping cooperative control method based on multi-mode oscillation suppression provided in the above embodiments, and will not be repeated here.

[0124] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the damped cooperative control method based on multi-mode oscillation suppression as described above.

[0125] The computer program product provided in this application can solve the technical problem that traditional additional damping control strategies use fixed control parameters, cannot adapt to the full operating conditions of the station, and have low control stability. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the damping cooperative control method based on multi-mode oscillation suppression provided in the above embodiments, and will not be repeated here.

[0126] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A damping cooperative control method based on multi-mode oscillation suppression, characterized by, The method includes: Based on the target power level, real-time short-circuit ratio, and operating status index of the target power station under the current operating conditions, the target additional damping control parameters under the current operating conditions are determined. The oscillation pattern of the target station is detected, and the dynamic suppression weight of the oscillation pattern is determined based on the comprehensive risk index of the oscillation pattern. A task allocation optimization model is constructed, and the task allocation optimization model is solved based on the state information, mode requirement information and grid parameters of the power generation units in the target power station to determine the resource allocation ratio of the power generation units in the oscillation mode. Based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode, a coordinated control command is generated. The power generation unit is subjected to additional damping control based on the aforementioned coordinated control command; Before determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode, the method further includes: calculating the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode; obtaining a first correspondence between the amplitude index, damping index, growth rate index, frequency risk index, and comprehensive risk index; and determining the comprehensive risk index of the oscillation mode based on the first correspondence and the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode, wherein the comprehensive risk index is used to characterize the degree of harm in the comprehensive assessment. The step of determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode includes: obtaining the weight distribution adjustment coefficient and the second correspondence between the comprehensive risk index, the weight distribution adjustment coefficient and the dynamic suppression weight; and determining the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode, the weight distribution adjustment coefficient and the second correspondence.

2. The method of claim 1, wherein, The step of determining the target additional damping control parameters under the current operating conditions based on the target power level, real-time short-circuit ratio, and operating state index of the target power station includes: Based on the target power station's power level, real-time short-circuit ratio, and operating status index under the current operating conditions, a feature vector under the current operating conditions is generated; Based on the mapping database, the target additional damping control parameters corresponding to the feature vector of the current working condition are determined. The mapping database stores sample feature vectors under different working conditions and the additional damping control parameters corresponding to the sample feature vectors.

3. The method of claim 2, wherein, The step of determining the target additional damping control parameters corresponding to the feature vector of the current operating condition based on the mapping database includes: Based on the weighted Euclidean distance between the feature vector of the current working condition and the feature vector of the sample in the mapping database, the feature vector of the neighboring sample is determined. Obtain additional damping control parameters from the neighboring sample feature vectors in the mapping database; Based on the additional damping control parameters of the feature vectors of the neighboring samples, the target additional damping control parameters corresponding to the feature vectors of the current operating condition are determined.

4. The method of claim 3, wherein, The step of determining the target additional damping control parameters corresponding to the feature vector of the current operating condition based on the additional damping control parameters of the neighboring sample feature vectors includes: The additional damping control parameters of the neighboring sample feature vectors are weighted and interpolated to obtain the initial additional damping control parameters; Based on a preset smoothing coefficient, the initial additional damping control parameters are smoothed to obtain the target additional damping control parameters corresponding to the feature vector of the current operating condition.

5. The method as described in claim 1, characterized in that, The steps to build a task allocation optimization model include: Obtain the third correspondence between resource allocation ratio, suppression effectiveness and suppression effect index, and the fourth correspondence between resource allocation ratio, damping power, average load rate and load balance degree. Also obtain the fifth correspondence between resource allocation ratio and robustness index, and the sixth correspondence between suppression effect index, load balance, robustness index and comprehensive utility. Based on the third, fourth, fifth, and sixth correspondences, a target optimization function is constructed. Based on the objective optimization function and objective constraints, a task allocation optimization model is obtained. The objective constraints include power generation unit resource constraints, oscillation mode coverage constraints, and capacity constraints.

6. The method according to any one of claims 1 to 5, characterized in that, Following the step of performing additional damping control on the power generation unit based on the cooperative control command, the method further includes: The suppression effect of the power generation unit under the cooperative control command is evaluated to obtain the suppression evaluation value of the power generation unit; When the suppression evaluation value of the power generation unit is less than the preset evaluation threshold, the resource allocation ratio of the power generation unit in the oscillation mode is re-determined; Based on the redefined resource allocation ratio of the power generation unit in the oscillation mode, the process returns to the step of generating a coordinated control command based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode.

7. A damped cooperative control device based on multi-mode oscillation suppression, characterized in that, The device includes: The operating condition adaptive module is used to determine the target additional damping control parameters under the current operating condition based on the power level, real-time short-circuit ratio and operating state index of the target power station under the current operating condition. The collaborative suppression module is used to detect the oscillation mode of the target station and determine the dynamic suppression weight of the oscillation mode based on the comprehensive risk index of the oscillation mode. The global optimization module is used to construct a task allocation optimization model. Based on the state information, mode requirement information and grid parameters of the power generation units in the target power station, the task allocation optimization model is solved to determine the resource allocation ratio of the power generation units in the oscillation mode. The damping control module is used to generate coordinated control commands based on the resource allocation ratio of the power generation unit in the oscillation mode, the target additional damping control parameters, and the dynamic suppression weight of the oscillation mode. The damping control module is also used to perform additional damping control on the power generation unit based on the cooperative control command; The collaborative suppression module is also used to calculate the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode. Obtain the first correspondence between amplitude indicators, damping indicators, growth rate indicators, frequency risk indicators, and comprehensive risk index; Based on the first correspondence and the amplitude index, damping index, growth rate index, and frequency risk index of the oscillation mode, a comprehensive risk index of the oscillation mode is determined. The comprehensive risk index is used to characterize the degree of harm in the comprehensive assessment. The collaborative inhibition module is also used to obtain the weight distribution adjustment coefficient and the comprehensive risk index, as well as the second correspondence between the weight distribution adjustment coefficient and the dynamic inhibition weight; Based on the comprehensive risk index of the oscillation mode, the weight distribution adjustment coefficient, and the second correspondence, the dynamic suppression weight of the oscillation mode is determined.

8. A damping cooperative control device based on multi-mode oscillation suppression, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the damped cooperative control method based on multi-mode oscillation suppression as described in any one of claims 1 to 6.

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