Oscillation stability control method and device applied to black start process of power system, equipment and storage medium
By constructing a state matrix and optimizing control parameters during the black start process of the power system, the problem of insufficient oscillation stability control efficiency in power systems with a high proportion of new energy access was solved, thereby improving system stability and increasing the success rate of black start.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WENZHOU ELECTRIC POWER BUREAU
- Filing Date
- 2026-04-08
- Publication Date
- 2026-05-12
AI Technical Summary
During the black start process of a power system with a high proportion of renewable energy integration, the existing oscillation stabilization control technology is inefficient and cannot fully utilize the flexible adjustment potential of power electronic equipment, resulting in limited improvement in system stability margin and a high risk of black start failure.
By acquiring control parameter data of grid-connected equipment, power flow coupling processing is performed to construct a state matrix. Heuristic and analytical optimization techniques are used to optimize control parameters, forming a system operation control scheme that adapts to the time-varying characteristics of the system in real time and fully releases the regulation potential of power electronic equipment.
It significantly improves the system's oscillation stability margin and black start success rate, accurately suppresses various oscillations, and enhances the system's stability and reliability.
Smart Images
Figure CN122026348A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system operation control technology, and in particular to an oscillation stabilization control method, device, equipment and storage medium applied to the black start process of a power system. Background Technology
[0002] For power systems in coastal areas with a high proportion of renewable energy integration, their operating conditions exhibit greater time-varying characteristics compared to conventional power systems, particularly during the black start process following a major blackout caused by extreme weather conditions. During black start, the grid strength is weak, voltage / frequency support is insufficient, and the dynamic interactions between grid-connected units such as renewable energy generation, energy storage systems, and conventional generating units can trigger various types of oscillation and stability problems, affecting system operational safety, leading to black start failure, and resulting in corresponding economic and social losses.
[0003] In existing technologies, conventional oscillation stabilization control technology is achieved by unilaterally adjusting the system operating state or individually optimizing the control parameters of certain grid-connected equipment. This technical approach originates from traditional power systems dominated by synchronous generators, where the adjustment of the system operating state and the dynamic process of the equipment are separated on the time scale and can be carried out independently.
[0004] However, the fluctuations in new energy power generation and the rapid control dynamics of power electronic equipment are highly coupled on the time scale. The system operating state and equipment control characteristics will affect each other and jointly dominate the system stability. Therefore, conventional oscillation stabilization control technology is not efficient and effective in strong time-varying processes such as black start. The optimization adjustment space is limited, and the flexible adjustment potential of power electronic equipment cannot be fully utilized. Ultimately, the improvement of system stability margin is limited, and the risk of black start failure increases. Summary of the Invention
[0005] This invention provides an oscillation stability control method, device, equipment, and storage medium for use in the black start process of power systems. It addresses the technical problems of insufficient efficiency and effectiveness of oscillation stability control in power systems during strong time-varying processes such as black start, limited optimization adjustment space, and inability to fully utilize the flexible adjustment potential of power electronic devices. This invention aims to improve the oscillation stability level of the system during strong time-varying operation processes such as black start.
[0006] To address the aforementioned technical problems, this invention provides an oscillation stability control method applied to the black start process of a power system, the method comprising: Acquire control parameter data of the target power system, wherein the control parameter data is used to control several grid-connected devices; The power flow coupling processing is performed on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; the eigenvalue analysis processing is performed on the state matrix to obtain the target damping ratio data of the target power system. Using minimizing the target damping ratio data as the first objective function, the control parameter data is processed using heuristic optimization techniques to obtain the first control parameter vector; The oscillation stability index obtained by mapping the first control parameter vector is analyzed with the preset damping ratio limit, and the oscillation stability index sequence of the target power system is determined based on the analysis results. Using minimizing the operating parameter data as the second objective function, analytical optimization techniques are applied to process the oscillation stability index sequence to obtain the system operation control scheme for the target power system. Execute the system operation control scheme.
[0007] As one preferred embodiment, the step of performing power flow coupling processing on the obtained output power of the several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system includes: Based on node power balance, the output power of several grid-connected devices and the operating parameter data of the target power system are processed to obtain a set of nonlinear power flow equations; The nonlinear power flow equations are linearized analytically to obtain the state matrix of the target power system.
[0008] As one preferred embodiment, the process of using heuristic optimization techniques to process the control parameter data to obtain a first control parameter vector includes: The control parameter data is encoded to obtain the data to be optimized for the target power system; The first control parameter vector is obtained by iteratively evolving the data to be optimized using heuristic optimization techniques.
[0009] As one preferred embodiment, the step of analyzing the oscillation stability index obtained by mapping the first control parameter vector with a preset damping ratio limit, and determining the oscillation stability index sequence of the target power system based on the analysis results, includes: Based on simulation technology, the first control parameter vector is mapped using state-space modeling to obtain the oscillation stability index; The oscillation stability index and the preset damping ratio limit are compared and analyzed using a threshold method to obtain the analysis results. The analysis results are serialized and discretized to obtain the oscillation stability index sequence.
[0010] As one preferred embodiment, the step of processing the oscillation stability index sequence using analytical optimization techniques to obtain the system operation control scheme of the target power system includes: The oscillation stability index sequence is processed using analytical optimization techniques to obtain a set of running points; The convergence verification process is performed on the set of operating points to obtain the system operation control scheme of the target power system.
[0011] As a preferred embodiment, the convergence verification process performed on the set of operating points to obtain the system operation control scheme for the target power system includes: The set of running points is subjected to convergence verification to obtain candidate running points; The candidate operating points are selected using a decision-making technique based on the optimal state to obtain the system operation control scheme for the target power system.
[0012] As one preferred embodiment, the oscillation stabilization control method applied to the black start process of a power system further includes the following when implementing the system operation control scheme: The obtained execution results are subjected to stability reassessment to obtain the current stable state identifier of the target power system; The current stable state identifier is analyzed using logic analysis techniques to obtain the logic control result.
[0013] The present invention also provides an oscillation stabilization control device applied to the black start process of a power system, comprising: An acquisition module is used to acquire control parameter data of a target power system, wherein the control parameter data is used to control several grid-connected devices; The coupling module is used to perform power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; and to perform eigenvalue analysis processing on the state matrix to obtain the target damping ratio data of the target power system. The processing module is used to process the control parameter data using heuristic optimization techniques, with minimizing the target damping ratio data as the first objective function, to obtain a first control parameter vector; The analysis module is used to analyze the oscillation stability index obtained by mapping the first control parameter vector with the preset damping ratio limit, and determine the oscillation stability index sequence of the target power system based on the analysis results. An optimization module is used to process the oscillation stability index sequence using analytical optimization techniques with the goal of minimizing the operating parameter data as the second objective function, thereby obtaining the system operation control scheme of the target power system. The execution module is used to execute the system operation control scheme.
[0014] The present invention also provides an oscillation stabilization control device for use in the black start process of a power system, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the oscillation stabilization control method for use in the black start process of a power system as described above.
[0015] The present invention further provides a computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the oscillation stabilization control method applied to the black start process of a power system as described above.
[0016] Compared with the prior art, the beneficial effects of the present invention are at least one of the following: This invention acquires control parameter data of a target power system, wherein the control parameter data is used to control several grid-connected devices; performs power flow coupling processing on the acquired output power of the several grid-connected devices and the operating parameter data of the target power system to construct a state matrix of the target power system; performs eigenvalue analysis on the state matrix to obtain target damping ratio data of the target power system; uses minimizing the target damping ratio data as the first objective function, and uses heuristic optimization techniques to process the control parameter data to obtain a first control parameter vector; analyzes the oscillation stability index obtained by mapping the first control parameter vector with a preset damping ratio limit, and determines the oscillation stability index sequence of the target power system based on the analysis results; uses minimizing the operating parameter data as the second objective function, and uses analytical optimization techniques to process the oscillation stability index sequence to obtain a system operation control scheme for the target power system; and executes the system operation control scheme.
[0017] Compared with existing technologies, this method acquires control parameters of grid-connected equipment and system operation data, constructs a state matrix through power flow coupling, obtains damping ratio data through eigenvalue analysis, and then uses heuristic optimization to obtain the first control parameter vector with the goal of maximizing the damping ratio. Combined with preset damping ratio limits, it determines the oscillation stability index sequence. Finally, with the goal of minimizing operating parameter deviation, it forms a system operation control scheme through analytical optimization and executes it. Its core is to accurately suppress various oscillations and significantly improve the system stability margin and black start success rate by coordinating the optimization of control parameters of multiple devices, adapting to the time-varying characteristics of the system in real time, and fully releasing the flexible adjustment potential of power electronic equipment. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating an oscillation stabilization control method applied to the black start process of a power system in one embodiment of the present invention. Figure 2 This is a flowchart illustrating a two-parameter spatial collaborative optimization method for enhancing the oscillation stability of the black start process in a power-electronic power system, according to one embodiment of the present invention. Figure 3 This is a schematic diagram of the oscillation stabilization control device applied to the black start process of a power system in one embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an oscillation stabilization control device applied to the black start process of a power system in one embodiment of the present invention; Figure label: Among them, 11. Acquisition module; 12. Coupling module; 13. Processing module; 14. Analysis module; 15. Optimization module; 16. Execution module; 21. Processor; 22. Memory. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0020] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.
[0021] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0022] One embodiment of the present invention provides an oscillation stabilization control method applied to the black start process of a power system. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart illustrating an oscillation stabilization control method applied to the black start process of a power system according to one embodiment of the present invention. The method includes: S1: Obtain control parameter data of the target power system, wherein the control parameter data is used to control several grid-connected devices; S2: Perform power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; perform eigenvalue analysis processing on the state matrix to obtain the target damping ratio data of the target power system; S3: Using minimizing the target damping ratio data as the first objective function, the control parameter data is processed using heuristic optimization techniques to obtain the first control parameter vector; S4: Analyze the oscillation stability index obtained by mapping the first control parameter vector with the preset damping ratio limit, and determine the oscillation stability index sequence of the target power system based on the analysis results; S5: Using the minimization of the operating parameter data as the second objective function, the oscillation stability index sequence is processed using analytical optimization techniques to obtain the system operation control scheme of the target power system; S6: Execute the system operation control scheme.
[0023] The control parameter data includes parameters of multiple time-scale control links in wind power generation, photovoltaic power generation, and energy storage systems, such as the time coefficient, proportional coefficient, and integral coefficient of the voltage and current control loop in the power converter; and the virtual inertia coefficient, damping coefficient, and droop coefficient of the primary frequency regulation in the control link of the grid-connected energy storage system. These parameters are used to control several grid-connected devices.
[0024] The power flow coupling processing is performed on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system. Specifically, this includes: processing the output power of several grid-connected devices and the operating parameter data of the target power system based on node power balance to obtain a nonlinear power flow equation set; and performing linearization analytical processing on the nonlinear power flow equation set to obtain the state matrix of the target power system.
[0025] Specifically, all buses of the target power system are first classified, distinguishing between PQ nodes, PV nodes, and slack nodes. Then, the output active and reactive power of grid-connected equipment, the initial or measured values of voltage amplitude and phase angle of each node in the system, load data, and network parameters such as the impedance admittance of transmission line transformers are collected. Subsequently, based on Kirchhoff's current law, the correlation equations between injected power and node voltage phase angle and inter-node admittance are established for each PQ node and PV node. These equations are nonlinear due to the involvement of voltage squares and trigonometric functions, ultimately forming a nonlinear power flow equation set containing multiple independent equations.
[0026] Next, the node voltage amplitude and phase angle that satisfy power balance under the current operating state are obtained to determine the stable operating point of the system. Then, at this operating point, the partial derivatives of each equation in the nonlinear equation system with respect to each state variable are calculated to construct the Jacobian matrix. The nonlinear equations are then approximately transformed into linear incremental equations. Finally, the system dynamic component model is combined with mathematical transformations to form the state matrix in the standard linear state-space model.
[0027] The target damping ratio data of the target power system is obtained by performing eigenvalue analysis on the state matrix.
[0028] In optimizing the target damping ratio for oscillating stability, only the voltage component is considered as the operating parameter. The voltage component is coupled with the power parameters through the power flow equations, and a set of voltage components corresponds one-to-one with a set of power parameters. Therefore, the system operating point can be described using either power parameters or voltage components.
[0029] Specifically, linear algebra numerical calculation methods are used to solve for the eigenvalues of the state matrix. Through matrix decomposition or iterative operations, all eigenvalues are obtained. These eigenvalues are mostly presented in complex number form. Each complex eigenvalue contains two parts: a real part and an imaginary part. The real part of the eigenvalue represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part of the eigenvalue represents the oscillation frequency of the corresponding oscillation mode.
[0030] The damping ratio is calculated based on the real and imaginary parts of each complex eigenvalue. The absolute value of the real part of the eigenvalue is divided by the eigenvalue magnitude using a formula to obtain the damping ratio corresponding to each oscillation mode. The damping ratio of the key oscillation mode is then selected as the target damping ratio data. The key oscillation mode is usually the mode with the smallest damping ratio and the most likely to cause instability.
[0031] Using minimizing the target damping ratio data as the first objective function, the control parameter data is processed using heuristic optimization techniques to obtain a first control parameter vector. Specifically, this includes: encoding the control parameter data to obtain the data to be optimized for the target power system; and iteratively evolving the data to be optimized using heuristic optimization techniques to obtain the first control parameter vector.
[0032] It is important to first clarify the physical constraints of each control parameter, define the value boundaries based on the equipment operating limits and system safety requirements, and then select the encoding method according to the adaptability of heuristic optimization techniques. Each control parameter is converted into an algorithm-recognizable encoding form, with each control parameter corresponding to a segment of encoding. The encodings of all control parameters are combined in sequence to form a complete set of data to be optimized, and each set of data to be optimized corresponds to a set of control parameter combinations.
[0033] In the iterative evolution process using heuristic optimization techniques, a reasonable population size is first set according to the complexity of the optimization problem, and an initial population containing multiple sets of data to be optimized is generated. Then, the fitness value corresponding to each set of data to be optimized in the population is calculated using the minimization of the target damping ratio as the fitness evaluation criterion. Subsequently, the data to be optimized with better fitness is retained through selection, the encoding segments of different data to be optimized are recombined through crossover, and the values of some encoding bits are randomly adjusted through mutation to generate a new generation of population. The process of fitness calculation, selection, crossover, and mutation is repeated for iterative evolution until the preset number of iterations or the fitness value meets the optimization requirements. Finally, the optimal data to be optimized obtained in the iteration process is decoded and restored to a combination of control parameters with actual physical meaning, namely the first control parameter vector.
[0034] Preferably, heuristic optimization techniques include, but are not limited to, genetic algorithms, ant colony algorithms, and brute-force search algorithms.
[0035] The oscillation stability index obtained by mapping the first control parameter vector is analyzed with a preset damping ratio limit. Based on the analysis results, the oscillation stability index sequence of the target power system is determined. Specifically, this includes: mapping the first control parameter vector using state-space modeling based on simulation technology to obtain the oscillation stability index; performing threshold comparison analysis on the oscillation stability index and the preset damping ratio limit to obtain the analysis results; and performing serialization and discretization processing on the analysis results to obtain the oscillation stability index sequence.
[0036] The first control parameter vector is substituted into the state space model of the target power system to replace the original control parameters. At the same time, the relevant matrix elements reflecting the control characteristics of the equipment in the state space model are updated. Then, a corresponding simulation platform is built, and the simulation boundary conditions and time steps are set to be consistent with the actual black start scenario. The simulation program is run to simulate the dynamic response process of the system. Key parameters related to oscillation stability in the simulation results are extracted, including the damping ratio, oscillation frequency, and amplification decay rate of each oscillation mode. These parameters are integrated to form an oscillation stability index.
[0037] The specific value of the preset damping ratio limit is determined based on the power system safety operation standards and the stability requirements of the black start scenario.
[0038] The damping ratio data of each oscillation mode in the oscillation stability index are compared with the preset limit one by one to determine whether each index has reached the safety threshold and by how much it exceeds or falls below the limit. At the same time, the oscillation mode type and occurrence time corresponding to each index are recorded to form a comprehensive analysis result that includes the index compliance status, deviation degree, and related oscillation modes.
[0039] According to the preset sorting rules, the comprehensive analysis results can be arranged in an orderly manner according to the priority of the oscillation mode, the order of occurrence, or the degree of deviation. Then, the continuous deviation data and the compliance status are discretized according to fixed intervals or classification standards, and each analysis result is converted into a discrete value or status identifier, ultimately forming a set of oscillation stability index sequences with a regular structure and clear order.
[0040] Furthermore, taking minimizing the operating parameter data as the second objective function, the system operation control scheme of the target power system is obtained by processing the oscillation stability index sequence using analytical optimization techniques, including: processing the oscillation stability index sequence using analytical optimization techniques to obtain a set of operating points; and performing convergence verification processing on the set of operating points to obtain the system operation control scheme of the target power system.
[0041] Specifically, the expression of the second objective function focuses on minimizing key operating parameters such as voltage deviation, frequency fluctuation, and power loss. It uses the oscillation stability index sequence as a constraint condition to limit the stability threshold that each operating parameter must meet, and then selects an appropriate analytical optimization technique based on the characteristics of the problem.
[0042] Preferably, analytical optimization techniques include, but are not limited to, continuously linearized interior point optimization algorithms.
[0043] A mathematical optimization model containing the objective function and constraints is established. Analytical optimization techniques are used to solve the model to obtain multiple feasible operating points that satisfy the constraints and minimize the operating parameter data. These operating points are then organized into a set of operating points according to a preset dimension.
[0044] In the following process, for each operating point in the set of operating points, the simulation platform built above is constructed to simulate the system response process under strongly time-varying operating scenarios such as black start, monitor the changing trend of operating parameters over time, determine whether the parameters can quickly converge to a stable value, and whether there are problems such as overshoot or continuous oscillation during the convergence process. Qualified operating points with fast convergence speed, small steady-state deviation, and no risk of dynamic instability are selected. Then, the optimal solution with the smallest operating parameter data is selected from the qualified operating points and transformed into specific control instructions for each grid-connected device, system topology adjustment strategy, and operating parameter setpoints, which are then integrated to form a complete system operation control scheme.
[0045] Furthermore, the set of operating points is subjected to convergence verification processing to obtain the system operation control scheme of the target power system. This includes performing convergence verification processing on the set of operating points to obtain candidate operating points; and using decision-making technology based on optimization state to perform solution selection processing on the candidate operating points to obtain the system operation control scheme of the target power system.
[0046] The simulation program simulates the dynamic response process of the system at the operating point, monitors the changes in core operating parameters such as voltage, frequency, and power in real time, and determines whether the parameters can reach a stable value within a specified time, whether the deviation is within the allowable range after stabilization, and whether there are abnormalities such as excessive overshoot or continuous oscillation during the response process. All operating points that meet the convergence criteria are retained and integrated to form candidate operating points.
[0047] Then, based on the existing situation of the power system, a decision evaluation index system is determined, covering key dimensions such as the degree of optimization of operating parameters, system stability margin, equipment operating losses, and control execution difficulty. Weights are then assigned according to the importance of each index, and each candidate operating point is comprehensively evaluated through weighted scoring or multi-objective decision-making algorithms. The comprehensive performance score of each candidate point is quantified, and the candidates are sorted from high to low scores. The candidate operating point with the best comprehensive performance is selected, and the control parameters, topology adjustment methods, equipment operating thresholds, and other information corresponding to the operating point are transformed into operating instructions and system operating rules that can be directly executed by each grid-connected device. These are then integrated to form a complete system operation control scheme.
[0048] Execute the system operation control scheme.
[0049] Furthermore, after executing the system operation control scheme, the oscillation stability control method applied to the black start process of the power system further includes: performing stability reassessment processing on the obtained execution results to obtain the current stable state identifier of the target power system; and using logic analysis technology to analyze the current stable state identifier to obtain the logic control result.
[0050] The specific steps are as follows: First, the key operating data of the target power system after the execution of the control scheme is collected through the real-time power system monitoring device, including the voltage amplitude and phase of each node, system frequency, output power of grid-connected equipment, charging and discharging status of energy storage system, etc., and the time, frequency and attenuation characteristics of oscillation are recorded.
[0051] Then, using the previously constructed state matrix and eigenvalue analysis model, the real-time collected data is substituted into the system to recalculate the current damping ratio, oscillation mode, and stability margin, among other core indicators. Finally, the stability indicators obtained from the reassessment are compared with the preset safety thresholds, and a current stability status identifier is generated based on the comparison results. The identifier includes stable, critically stable, unstable, and the corresponding degree of indicator deviation.
[0052] A logical rule base based on stable state identifiers is then established. The rule base contains response strategies for different stable states. For example, when the identifier is stable, the rule is set to maintain the current control parameters and continuously monitor state changes; when the identifier is critically stable, the rule is set to fine-tune the virtual inertia parameters of the renewable energy inverter to increase system damping; when the identifier is unstable, the rule is set to urgently disconnect some non-critical loads and increase the active power support of the energy storage system. The rule base also needs to take into account equipment operation constraints and system recovery objectives. The current stable state identifier is then input into the logic analysis module. Through pattern matching and inference algorithms, the corresponding optimal response strategy is selected from the rule base to generate a logical control result containing specific control instructions, execution priorities, and time windows. For example, priority is given to adjusting the reactive power output of the photovoltaic inverter, and the Q value is increased to 0.8 pu within 100ms.
[0053] In another embodiment, Figure 2 This is a flowchart illustrating a two-parameter spatial collaborative optimization method for enhancing oscillation stability during the black start process of a power-electronic power system, according to one embodiment of the present invention. The method is as follows: S1: For a given system operating point, by constructing a system state-space model and calculating the key eigenvalues of the model's state matrix, the minimum damping ratio of the system's oscillation mode at the current operating point is obtained. ,by As a quantitative indicator of system oscillation stability.
[0054] Preferably, this operating point is derived from the recovery path plan for black start of the power system.
[0055] S2: Set the limit N for the number of rounds of two-parameter spatial interactive iterative optimization and the limit of the damping ratio to meet the system's operational safety requirements. If the system's safety requirements are not met, i.e., the minimum damping ratio is less than the damping ratio limit, then the interactive iterative optimization process S3, S4, and S5 in the two-parameter space is entered.
[0056] S3: Collect adjustable parameters of the power electronic control links in the grid-connected equipment of the system to form control parameter data, which constitutes the first parameter optimization space; collect system operating parameters, mainly including the active and reactive power of dispatchable generation units and adjustable load demand, to form operating parameter data, which constitutes the second parameter optimization space.
[0057] S4: For the first parameter optimization space, with the goal of maximizing the minimum damping ratio of the system oscillation mode, optimization constraints are formed based on the adjustable range of the control parameters. Heuristic optimization methods (including but not limited to genetic algorithms, ant colony algorithms, brute-force search algorithms, etc.) are used to optimize and adjust the setpoint of the control parameters. After the control parameters are optimized, the system is recalculated under the updated control parameters. .like Not less than If the oscillation stability level at the system operating point meets the safety requirements, the two-parameter space optimization process terminates, and the parameters of the corresponding control loop are tuned online using the optimized control parameters. Less than The oscillation stability level of the system operating point still does not meet the safety requirements, so the second parameter optimization space is optimized and adjusted (S5).
[0058] S5: Regarding the optimization space of the second parameter, the optimization objective is to minimize the deviation of the operating parameters from the original operating point, with the system oscillation stability index not less than [value missing]. To stabilize the constraints, additional constraints such as system voltage safety constraints, line transmission power constraints, and power network flow constraints are added. Analytical optimization algorithms (including but not limited to continuously linearized interior-point optimization algorithms) are used to optimize the system operating parameters, i.e., adjusting the original operating point. If the optimization is successful, the system operating point is updated with the optimized operating parameters, and the two-parameter space optimization process terminates. If the optimization fails, the system operating point is updated with the system operating parameters corresponding to the optimization termination point. In the case of a failed optimization: if the number of rounds of two-parameter space optimization has reached the limit N, the process terminates, and the current operating parameter vector is output; otherwise, the next round of two-parameter space optimization begins, i.e., steps S4 and S5 are repeated.
[0059] Taking the application of genetic algorithms as an example, the optimization variables are represented by the control parameter vector based on direct floating-point encoding. During the optimization iteration process, based on the control parameter values corresponding to each individual in the population, and following the further scheme corresponding to step S1, the state matrix of the power system is reconstructed, and its key characteristic values are calculated to obtain the system oscillation stability quantification index. After the heuristic algorithm reaches the iteration termination condition, the oscillation stability index corresponding to the optimal control parameter vector is compared with the minimum damping ratio limit. Based on the comparison result, the subsequent two-parameter space interactive iterative optimization process is implemented.
[0060] After steps S4 and S5, one round of dual-parameter space interactive iterative optimization is completed. If the cumulative number of rounds exceeds the set limit N, the current control parameter vector and the operating parameter vector are output, which are used to tune the parameters of the power electronic equipment control loop and adjust the system operating state online, respectively. If the cumulative number of rounds does not exceed the limit N, the previous operating point replaces the original system operating point, and the next round of dual-parameter space optimization is performed according to the method described in steps S4 and S5.
[0061] Another embodiment of the present invention provides an oscillation stabilization control device applied to the black start process of a power system. For details, please refer to [link to relevant documentation]. Figure 3 , Figure 3The diagram shown is a structural schematic of an oscillation stabilization control device applied to the black start process of a power system according to one embodiment of the present invention. The device includes: The acquisition module 11 is used to acquire control parameter data of the target power system, wherein the control parameter data is used to control several grid-connected devices; The coupling module 12 is used to perform power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; and to perform eigenvalue analysis processing on the state matrix to obtain the target damping ratio data of the target power system. Processing module 13 is used to process the control parameter data using heuristic optimization techniques with minimizing the target damping ratio data as the first objective function, to obtain a first control parameter vector; Analysis module 14 is used to analyze the oscillation stability index obtained by mapping the first control parameter vector with the preset damping ratio limit, and determine the oscillation stability index sequence of the target power system based on the analysis results. Optimization module 15 is used to process the oscillation stability index sequence using analytical optimization techniques with minimizing the operating parameter data as the second objective function, so as to obtain the system operation control scheme of the target power system; The execution module 16 is used to execute the system operation control scheme.
[0062] See Figure 4 This is a schematic diagram of the structure of an oscillation stabilization control device applied to the black start process of a power system, provided in an embodiment of the present invention. The oscillation stabilization control device for the black start process of a power system, provided in an embodiment of the present invention, includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps described in the above embodiment of the oscillation stabilization control method for the black start process of a power system. For example... Figure 1 The steps S1 to S6 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the acquisition module 11.
[0063] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the oscillation stabilization control device applied during the black start process of a power system. For example, the computer program can be divided into an acquisition module 11, a coupling module 12, a processing module 13, etc., with the specific functions of each module as follows: The acquisition module 11 is used to acquire control parameter data of the target power system, wherein the control parameter data is used to control several grid-connected devices; The coupling module 12 is used to perform power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; and to perform eigenvalue analysis processing on the state matrix to obtain the target damping ratio data of the target power system. Processing module 13 is used to process the control parameter data using heuristic optimization techniques with minimizing the target damping ratio data as the first objective function, to obtain a first control parameter vector; Analysis module 14 is used to analyze the oscillation stability index obtained by mapping the first control parameter vector with the preset damping ratio limit, and determine the oscillation stability index sequence of the target power system based on the analysis results. Optimization module 15 is used to process the oscillation stability index sequence using analytical optimization techniques with minimizing the operating parameter data as the second objective function, so as to obtain the system operation control scheme of the target power system; The execution module 16 is used to execute the system operation control scheme.
[0064] The oscillation stabilization control device applied during the black start process of a power system may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of an oscillation stabilization control device applied during the black start process of a power system and does not constitute a limitation on such a device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the oscillation stabilization control device applied during the black start process of a power system may also include input / output devices, network access devices, buses, etc.
[0065] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the oscillation stabilization control device applied during the black start process of the power system, connecting all parts of the oscillation stabilization control device through various interfaces and lines.
[0066] The memory 22 can be used to store the computer program and / or modules. The processor 21 implements various functions of the oscillation stabilization control device applied to the black start process of the power system by running or executing the computer program and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0067] The module integrated into the oscillation stabilization control device applied during the black start process of a power system, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0069] Accordingly, embodiments of the present invention provide a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform steps as described in the above-described embodiment of the oscillation stabilization control method applied to the black start process of a power system, for example... Figure 1 Steps S1 to S6 described herein are applied to oscillation stability control during the black start process of a power system.
[0070] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.
Claims
1. An oscillation stability control method applied to the black start process of a power system, characterized in that, include: Acquire control parameter data of the target power system, wherein the control parameter data is used to control several grid-connected devices; The power flow coupling processing is performed on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; the eigenvalue analysis processing is performed on the state matrix to obtain the target damping ratio data of the target power system. Using minimizing the target damping ratio data as the first objective function, the control parameter data is processed using heuristic optimization techniques to obtain the first control parameter vector; The oscillation stability index obtained by mapping the first control parameter vector is analyzed with the preset damping ratio limit, and the oscillation stability index sequence of the target power system is determined based on the analysis results. Using minimizing the operating parameter data as the second objective function, analytical optimization techniques are applied to process the oscillation stability index sequence to obtain the system operation control scheme for the target power system. Execute the system operation control scheme.
2. The oscillation stabilization control method applied to the black start process of a power system as described in claim 1, characterized in that, The process of performing power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system includes: Based on node power balance, the output power of several grid-connected devices and the operating parameter data of the target power system are processed to obtain a set of nonlinear power flow equations; The nonlinear power flow equations are linearized analytically to obtain the state matrix of the target power system.
3. The oscillation stabilization control method applied to the black start process of a power system as described in claim 1, characterized in that, The process of processing the control parameter data using heuristic optimization techniques to obtain a first control parameter vector includes: The control parameter data is encoded to obtain the data to be optimized for the target power system; The first control parameter vector is obtained by iteratively evolving the data to be optimized using heuristic optimization techniques.
4. The oscillation stabilization control method applied to the black start process of a power system as described in claim 1, characterized in that, The step of analyzing the oscillation stability index obtained by mapping the first control parameter vector with a preset damping ratio limit, and determining the oscillation stability index sequence of the target power system based on the analysis results, includes: Based on simulation technology, the first control parameter vector is mapped using state-space modeling to obtain the oscillation stability index; The oscillation stability index and the preset damping ratio limit are compared and analyzed using a threshold method to obtain the analysis results. The analysis results are serialized and discretized to obtain the oscillation stability index sequence.
5. The oscillation stabilization control method applied to the black start process of a power system as described in claim 1, characterized in that, The process of using analytical optimization techniques to process the oscillation stability index sequence to obtain the system operation control scheme of the target power system includes: The oscillation stability index sequence is processed using analytical optimization techniques to obtain a set of running points; The convergence verification process is performed on the set of operating points to obtain the system operation control scheme of the target power system.
6. The oscillation stabilization control method applied to the black start process of a power system as described in claim 5, characterized in that, The convergence verification process performed on the set of operating points yields the system operation control scheme for the target power system, including... The set of running points is subjected to convergence verification to obtain candidate running points; The candidate operating points are selected using a decision-making technique based on the optimal state to obtain the system operation control scheme for the target power system.
7. The oscillation stabilization control method applied to the black start process of a power system as described in claim 1, characterized in that, In implementing the system operation control scheme, the oscillation stabilization control method applied to the black start process of the power system further includes: The obtained execution results are subjected to stability reassessment to obtain the current stable state identifier of the target power system; The current stable state identifier is analyzed using logic analysis techniques to obtain the logic control result.
8. An oscillation stabilization control device applied to the black start process of a power system, characterized in that, include: An acquisition module is used to acquire control parameter data of a target power system, wherein the control parameter data is used to control several grid-connected devices; The coupling module is used to perform power flow coupling processing on the obtained output power of several grid-connected devices and the operating parameter data of the target power system to construct the state matrix of the target power system; and to perform eigenvalue analysis processing on the state matrix to obtain the target damping ratio data of the target power system. The processing module is used to process the control parameter data using heuristic optimization techniques, with minimizing the target damping ratio data as the first objective function, to obtain a first control parameter vector; The analysis module is used to analyze the oscillation stability index obtained by mapping the first control parameter vector with the preset damping ratio limit, and determine the oscillation stability index sequence of the target power system based on the analysis results. An optimization module is used to process the oscillation stability index sequence using analytical optimization techniques with the goal of minimizing the operating parameter data as the second objective function, thereby obtaining the system operation control scheme of the target power system. The execution module is used to execute the system operation control scheme.
9. An oscillation stabilization control device applied to the black start process of a power system, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the oscillation stabilization control method for the black start process of a power system as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the oscillation stabilization control method for the black start process of a power system as described in any one of claims 1 to 7.