Method and device for optimizing phase modifier excitation system based on model predictive control

Abstract

The embodiment of the invention discloses a method and device for optimizing a phase modifier excitation system based on model predictive control, and relates to the technical field of power system automatic control, and the method comprises the steps: building a mathematical model for describing the dynamic characteristics of the phase modifier excitation system; constructing a target function based on the mathematical model, wherein the target function at least comprises an output deviation item, a control input change rate item and a commutation failure suppression item; and in each control period, solving an optimization problem of the target function based on real-time data to obtain a target control input sequence, and adjusting the excitation current of the current period. According to the method, the commutation failure suppression item is introduced into the target function, commutation failure suppression is converted into direct target optimization, commutation failure can be effectively suppressed, system reliability, dynamic response and stability are improved, and multi-target comprehensive optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of power system automatic control technology, and in particular to a method and apparatus for optimizing a synchronous condenser excitation system based on model predictive control. Background Technology

[0002] High-voltage direct current (HVDC) transmission is a key technology in modern power systems for long-distance, high-capacity power transmission. When the AC grid connected to an HVDC transmission system is weak, voltage fluctuations and faults can easily cause continuous commutation failures in converters, potentially leading to DC blocking and threatening the safe and stable operation of the grid. Synchronous condensers, as dynamic reactive power compensation devices, can provide rapid reactive power support to the grid by adjusting their excitation current, making them an important technical means to enhance the stability of weak grids and suppress voltage fluctuations.

[0003] Traditional excitation control methods often rely on fixed strategies or simple PID control. When faced with power grid faults, traditional control methods have slow response speed and poor stability. Summary of the Invention

[0004] The main objective of this invention is to provide a method and apparatus for optimizing a synchronous condenser excitation system based on model predictive control, so as to solve the problem that current model predictive control-based synchronous condenser excitation control methods are insufficient in suppressing continuous commutation failures.

[0005] To achieve the above objectives, this application provides a method for optimizing a synchronous condenser excitation system based on model predictive control, the method comprising: A mathematical model is established for model predictive control to describe the dynamic characteristics of the synchronous condenser excitation system. The mathematical model characterizes the dynamic relationship between the excitation current as the control input and the grid voltage and frequency as the control output. Based on the mathematical model, an objective function is constructed, which includes at least: an output deviation term, a control input change rate term, and a commutation failure suppression term. In each control cycle, based on the collected real-time system operation data as the initial state, the target control input sequence is obtained by solving the optimization problem of the objective function within the preset prediction time domain. Based on the target control input sequence, the excitation current of the synchronous condenser excitation system in the current cycle is adjusted.

[0006] This application also provides an apparatus for optimizing a synchronous condenser excitation system based on model predictive control, comprising: The model building module is used to build a mathematical model for model predictive control that describes the dynamic characteristics of the synchronous condenser excitation system. The mathematical model characterizes the dynamic relationship between the excitation current as the control input and the grid voltage and frequency as the control output. The objective function construction module is used to construct an objective function based on the mathematical model. The objective function includes at least: an output deviation term, a control input change rate term, and a commutation failure suppression term. The optimization and solution module is used to obtain the target control input sequence by solving the optimization problem of the objective function within a preset prediction time domain, based on the collected real-time system operation data as the initial state in each control cycle. The control application module is used to adjust the excitation current of the synchronous condenser excitation system in the current cycle based on the target control input sequence.

[0007] In another aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method described in the first aspect.

[0008] This application provides a method and apparatus for optimizing a synchronous condenser excitation system based on model predictive control. The method involves establishing a mathematical model for model predictive control that describes the dynamic characteristics of the synchronous condenser excitation system. This mathematical model characterizes the dynamic relationship between the excitation current (control input) and the grid voltage and frequency (control output). Based on the mathematical model, an objective function is constructed, which includes at least an output deviation term, a control input rate of change term, and a commutation failure suppression term. In each control cycle, based on the collected real-time system operating data as the initial state, within a preset prediction time domain, the target control input sequence is obtained by solving the optimization problem of the objective function. The first control input value in the target control input sequence is applied to the synchronous condenser excitation system to adjust the excitation current for the current cycle. This allows for precise control of the excitation current, ensuring that the synchronous condenser can quickly respond to grid changes under various operating conditions, preventing commutation failure and improving system stability.

[0009] This application has the following beneficial effects: This application explicitly introduces a commutation failure suppression term into the objective function of model predictive control, transforming commutation failure suppression from an indirect control outcome into a direct, proactive optimization objective. This allows the controller to intervene proactively when commutation failure risks are predicted, effectively suppressing commutation failure and significantly improving the operational reliability of the HVDC transmission system under fault conditions. Furthermore, this application inherits the advantages of model predictive control, enabling rapid and precise adjustment of the excitation current through prediction and rolling optimization of the system's future behavior, thereby improving the dynamic response speed and overall stability of the synchronous condenser excitation system. In addition, this application unifies output tracking accuracy, control smoothness, and operational safety within a single optimization framework, achieving a comprehensive balance and optimization of multiple system performance indicators. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] in: Figure 1 A flowchart illustrating a method for optimizing a synchronous condenser excitation system based on model predictive control, provided in an embodiment of this application; Figure 2 This is a schematic diagram of a system structure provided in an embodiment of this application; Figure 3 This is a schematic diagram of a device for optimizing a synchronous condenser excitation system based on model predictive control, provided in an embodiment of this application.

[0012] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0014] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0015] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0016] The embodiments of this application are described below with reference to the accompanying drawings.

[0017] Please see Figure 1 This is a flowchart illustrating a method for optimizing a synchronous condenser excitation system based on model predictive control, as provided in an embodiment of this application.

[0018] The method described in this application primarily provides a model predictive control method for a synchronous condenser excitation system that includes a commutation failure suppression term. This method establishes a linearized system model and constructs an optimization objective function that includes output deviation, control input change rate, and a commutation failure suppression term. This enables precise, rapid, and stable control of the synchronous condenser excitation current, thereby actively suppressing the risk of commutation failure in the high-voltage direct current transmission system while maintaining grid voltage stability.

[0019] Figure 2 This is a schematic diagram of the system control structure according to an embodiment of this application, illustrating a typical system environment in which the method is applied. The system mainly includes a model predictive controller 100, a synchronous condenser excitation system 200, and a power system 300. The power system 300 is the controlled object, containing a synchronous condenser serving as a reactive power compensation device and voltage support device, as well as a connected high-voltage direct current (HVDC) converter station and an AC power grid. The model predictive controller 100 is the core unit for executing this method. It collects feedback signals from the power system 300 in real time through sensors or measurement units, such as the grid voltage and grid frequency at the synchronous condenser's connection point. Based on this real-time data, the model predictive controller 100 executes the control algorithm described in this application to calculate the optimal control signal, i.e., the expected value of the excitation current. The control signal is sent to the synchronous condenser excitation system 200. The synchronous condenser excitation system 200 (e.g., it may contain an excitation regulator) drives the excitation power supply to apply a corresponding excitation effect to the excitation winding of the synchronous condenser according to the received control signal, thereby changing the reactive power output of the synchronous condenser to influence and stabilize the voltage of the power system 300. This process constitutes a complete closed-loop feedback control loop. It is understood that the model predictive controller 100 can be implemented by hardware such as a digital signal processor, field-programmable gate array, industrial control computer, or embedded system, and has internally embedded or running software program modules for implementing the method of this application.

[0020] The following will combine Figure 1 and Figure 2 The steps in this embodiment will be described in detail.

[0021] like Figure 1 As shown, the method includes: 101. Establish a mathematical model for model predictive control that describes the dynamic characteristics of the synchronous condenser excitation system. The mathematical model characterizes the dynamic relationship between the excitation current as the control input and the grid voltage and frequency as the control output.

[0022] In this embodiment of the application, the subject executing the method can be a device for optimizing the excitation system of a synchronous condenser based on model predictive control, and in practical applications it can be an electronic device, such as a computer.

[0023] To achieve model predictive control, a mathematical model that accurately describes the dynamic characteristics of the system is needed. The model's function is to predict the system's response over a future period based on the current system state and future control inputs. In one embodiment of this application, a linear state-space model is used to describe the synchronous condenser excitation system and its dynamic interaction with the power grid. This model has the advantages of simple form and high computational efficiency, making it particularly suitable for real-time control applications.

[0024] This mathematical model can be a linear state-space model.

[0025] In one alternative implementation, the mathematical model described above is modeled using a first-order transfer function to describe the input and output relationship of the synchronous condenser excitation system. The input of the synchronous condenser excitation system is the excitation current, and the output of the synchronous condenser excitation system is voltage and frequency.

[0026] Specifically, the mathematical model of a synchronous condenser excitation system is based on the system's dynamic characteristics and is typically simplified using a first- or second-order system model to describe the input-output relationship of the synchronous condenser excitation system. It is assumed that the system input is the excitation current. The output is voltage. V and frequency f The embodiments of this application use a first-order transfer function for modeling: (1) in, It is the system's state variable. It is the control input (excitation current) ), It is the system output (voltage) V and frequency f ), matrices A, B, C, and D are the dynamic matrices of the system, representing the state-space model of the system.

[0027] Matrices A, B, C, and D are the dynamic matrices of the system, and are obtained as follows: The state vector of the camera excitation system is: (2) The control input for the synchronous condenser excitation system is: (3) Control output of the synchronous condenser excitation system: (4) In the excitation system of a synchronous condenser, the dynamic relationship between excitation current, frequency, and voltage can be described by establishing the differential equation of the power system as follows (5): (5) Matrix A represents the coupling relationships between system states. By linearizing the system's differential equations, the mutual influences between state variables are derived. Voltage and frequency are affected by the excitation current; changes in the excitation current cause changes in grid voltage and frequency. The elements of matrix A can be represented by partial derivatives: (6) Matrix B represents the effect of the control input (excitation current) on the system state. This is achieved by controlling the system's control input. Analyzing the changes, we obtain the elements of matrix B: (7) Matrix C describes how the system state maps to the output. Voltage and frequency are the system outputs, and matrix C represents how the state variables affect the output, expressed as: (8) In the formula: .

[0028] Matrix D represents the direct effect of the control input on the output. In a synchronous condenser excitation system, the direct effect of the excitation current on voltage and frequency is usually small, so matrix D is usually zero.

[0029] (9) 102. Based on the above mathematical model, construct an objective function, which includes at least: an output deviation term, a control input change rate term, and a commutation failure suppression term.

[0030] Specifically, the MPC prediction model can be constructed using the aforementioned mathematical model. The goal of the MPC control method mentioned in this application embodiment is to minimize the system's objective function by optimizing the control strategy. The objective function typically includes factors such as control deviation, system stability, and energy consumption. The key to the MPC method is to use the model to predict future states and then generate control inputs based on the optimization results.

[0031] In this embodiment, a finite-time-domain prediction model can be used to predict the future behavior of the system. Assume the prediction time domain is... Each time step controls the time domain. Each time step. At each time step... k The controller calculates the future The system output at each time step is used to minimize the objective function by adjusting the control input.

[0032] In order to predict future system states, the controller uses the current state. and control input Predictive models calculate future moments. The output is represented as: (10) in, It is a moment t+k The predicted output, It is the current state. From time t arrive t+k- 1 control input sequence, Δ t It is the sampling period.

[0033] Furthermore, a predictive model can be used to establish the MPC objective function. The optimization objective of MPC is to minimize the objective function. This includes output deviation term, control input change rate term, and commutation failure suppression term.

[0034] 1. Output Deviation Term: Represents the deviation between the system output and the desired target, ensuring the system voltage... V and frequency f Approaching the set value.

[0035] (11) in, It is the target output. Q It is a weighting matrix used to adjust the penalty for output deviation.

[0036] 2. Control input change rate term: The rate of change of the control input will affect the stability of the system. A large rate of change may cause the system to oscillate.

[0037] Optionally, the aforementioned control input rate of change term can be implemented by weighting and penalizing the rate of change of the excitation current. By limiting the rate of change of the excitation current, the stability of the system can be improved.

[0038] For example, the control input rate of change term can be: (12) in, R It is a penalty coefficient that controls the rate of change of input.

[0039] 3. Commutation Failure Suppression: Commutation failure usually leads to distortion of the current waveform, therefore additional optimization terms are needed to suppress commutation failure. To avoid commutation failure, the controller needs to ensure that the excitation current does not cause excessive current deviation or voltage fluctuation.

[0040] In one alternative implementation, the commutation failure suppression term is implemented by penalizing at least one of the excitation current deviation and the frequency deviation. In one implementation, the commutation failure suppression term is expressed as: (13) in, This indicates the above-mentioned excitation current deviation. This indicates the aforementioned frequency deviation. P and Q f It is the corresponding weighted matrix.

[0041] Furthermore, in practical optimization problems, physical constraints must also be considered, reflecting the safety limitations of physical equipment and system operation. In this embodiment, constraints may include at least those on the control input (excitation current) and the system output (grid voltage): (14) in, I min and I max These represent the lower and upper limits of the excitation current of the synchronous condenser, respectively, which are determined by the physical capabilities of the excitation system. V min and V max The allowable voltage fluctuation range of the power grid is a requirement for ensuring its safe and stable operation. Incorporating these constraints into the optimization problem ensures that the control commands calculated by the controller are both practically feasible and safe.

[0042] By integrating the above factors into a single objective function, the MPC controller can calculate the future control input based on the current state and the system model. The optimal sequence is obtained. Through rolling optimization, the optimal input is recalculated in each control cycle, ensuring the system maintains its optimal operating state. Ultimately, the optimization problem of MPC can be expressed as: (15) This objective function can simultaneously optimize the system's output accuracy, control input changes, and suppress commutation failures.

[0043] Expanding equation (7), we get the following equation: (16) The weighting coefficients Q, P, and R are determined as follows: The goal of a synchronous condenser excitation system is to control the voltage of the power grid. V (t) and frequency f (t), therefore Q The expression is as follows: (17) in, Q V and Q f The underscores (_) represent the deviation weights for voltage and frequency, respectively. Q V and Q f The setting is based on the system's emphasis on voltage and frequency control.

[0044] weight matrix R Used to control changes in the input (excitation current) and avoid over-adjustment or excessively drastic control input. Larger... R This value will result in a smoother control input to the system, preventing the system from adjusting or oscillating too quickly. It is set to: (18) in R If Control the rate of change of the excitation current.

[0045] weight matrix P The term used to suppress commutation failure indicates the system's level of concern regarding commutation failure. It is typically dynamically increased when the risk of commutation failure is detected. P The weights are adjusted to enhance system stability control and prevent the impact of commutation failure on the power grid.

[0046] During normal operation, P It can be set to: (19) P If It is related to the excitation current in the objective function.I f Weighting coefficients related to the rate of change.

[0047] However, when the risk of commutation failure is detected, P The weight can be increased dynamically: (20) Where λ is the multiplier of the increase. When the risk of commutation failure is detected, λ is greater than 1, thereby enhancing the control over system stability.

[0048] In an optional implementation, the method further includes: Online assessment of commutation failure risk; When a risk of commutation failure is detected, the weight of the aforementioned commutation failure suppression term in the objective function is dynamically increased.

[0049] In this embodiment, the risk level of commutation failure can also be assessed online, and the weight of the commutation failure suppression term in the objective function can be dynamically adjusted according to the risk level. This enables the controller to have adaptive capabilities, intelligently switching its control priority when the system is at different levels of danger, thereby demonstrating stronger robustness and effectiveness in ensuring system safety.

[0050] Specifically, in each control cycle, the model predictive controller 100 not only collects variables such as voltage and frequency for state prediction, but also collects or calculates indicators for risk assessment. The model predictive controller 100 integrates an online commutation failure risk assessment module. This module analyzes system operating data in real time according to preset rules or algorithms to determine whether there is a high risk of commutation failure. The assessment can be based on one or more physical quantities closely related to commutation failure.

[0051] As a specific implementation method, risk assessment can be based on at least one of the following indicators: 1. Voltage sag depth: When the effective voltage V at the synchronous condenser's grid connection point is lower than a preset threshold (e.g., 0.8 times the rated voltage), a commutation failure risk is considered to exist. The deeper the voltage sag, the higher the risk level.

[0052] 2. Duration of grid voltage drop: When the voltage remains below the threshold for a duration exceeding a preset time threshold (e.g., 100 milliseconds), the risk is considered significantly increased. The longer the duration, the greater the reactive power deficit accumulated at the converter station, and the higher the probability of commutation failure.

[0053] 3. Converter bus voltage waveform distortion rate: The total harmonic distortion rate is calculated by performing a Fast Fourier Transform or other harmonic analysis methods on the converter bus voltage waveform. When this distortion rate exceeds a preset threshold, it indicates that the voltage waveform deviates significantly from a sine wave, which will directly affect the success rate of the commutation process, and therefore can be identified as a high-risk condition.

[0054] Understandably, the risk assessment module can be implemented as a logical judgment unit. Optionally, fuzzy logic or machine learning algorithms can be introduced to comprehensively evaluate multiple indicators and output a continuous risk level index.

[0055] Following the evaluation, the process branches out in different directions based on the assessment results: If the judgment result is "yes," meaning the system currently has a high risk of commutation failure (e.g., a severe remote three-phase short-circuit fault is detected causing a sharp voltage drop), the process will execute an increase in weight. P If the judgment result is "no," meaning the system is operating normally or only experiences minor disturbances, and the risk of commutation failure is low, then conventional weights can be used. P .

[0056] Optional, increase weight P After the fault is cleared and the grid voltage is restored, the risk assessment module can again determine that the risk has decreased, and the weighting is adjusted accordingly. P It then returns to its normal value, and the controller returns to its normal fine adjustment mode.

[0057] 103. In each control cycle, based on the collected real-time system operation data as the initial state, the target control input sequence is obtained by solving the optimization problem of the above objective function within the preset prediction time domain.

[0058] 104. Based on the above target control input sequence, adjust the excitation current of the above synchronous condenser excitation system in the current cycle.

[0059] Specifically, by minimizing the objective function, the MPC controller calculates the optimal control input sequence for future time steps. This refers to the aforementioned target control input sequence, which is executed within each control cycle. The target control input sequence is applied to the excitation system to regulate the excitation current and ensure stable grid operation. Based on the rolling optimization principle of model predictive control, although the aforementioned steps can calculate the entire future control time domain... The control sequence is defined, but only the first control input value in the sequence is applied to the controlled object.

[0060] In each control cycle, MPC recalculates the optimal control strategy based on the new state and prediction results, ensuring that the system is always in an optimal operating state. This "prediction-optimization-application-remeasurement" cycle continues continuously, enabling the controller to continuously adjust the control strategy according to the actual changes in the system, thus exhibiting good robustness to disturbances and model uncertainties.

[0061] Through the above steps, the method of this embodiment can achieve stable, rapid, and precise control of the excitation current of the synchronous condenser. During normal grid fluctuations or minor disturbances, the output deviation term and the control input rate of change term play a dominant role, effectively maintaining voltage and frequency stability. When the grid suffers a serious fault that could lead to commutation failure, the commutation failure suppression term becomes significant, driving the controller to take stronger support actions, prioritizing system safety, thereby achieving the beneficial effect of suppressing commutation failure.

[0062] The method in this application embodiment has at least the following positive effects: This application proposes for the first time the application of model predictive control (MPC) in a synchronous condenser excitation system, thereby optimizing the control strategy. MPC can consider the future behavior of the system and adjust the excitation current in real time by predicting future states and outputs, thus effectively improving the dynamic response capability and stability of the system.

[0063] This application specifically designs a suppression strategy for commutation failure in HVDC systems. By adding a commutation failure suppression term to the objective function, real-time monitoring of current and frequency changes, and timely adjustment of the control input when the risk of commutation failure is detected, commutation failure is prevented from occurring.

[0064] By incorporating output deviation, control input variation, and commutation failure suppression as multiple objectives into the objective function of the MPC controller, comprehensive optimization of multiple objectives is achieved. This not only improves the system output accuracy but also effectively controls the rate of input change, ensuring system stability.

[0065] Based on the description of the foregoing method embodiments, this application also provides an apparatus for optimizing a synchronous condenser excitation system based on model predictive control.

[0066] Figure 3 This is a schematic diagram of a device for optimizing a synchronous condenser excitation system based on model predictive control, provided as an embodiment of this application. Figure 3 As shown, the device 300 for optimizing the synchronous condenser excitation system based on model predictive control includes: The model building module 310 is used to build a mathematical model for model predictive control that describes the dynamic characteristics of the synchronous condenser excitation system. The mathematical model characterizes the dynamic relationship between the excitation current as the control input and the grid voltage and frequency as the control output. The objective function construction module 320 is used to construct an objective function based on the mathematical model. The objective function includes at least: an output deviation term aimed at making the system output approach the target value, a control input change rate term aimed at smoothing the control input, and a commutation failure suppression term aimed at suppressing commutation failure. The optimization solution module 330 is used to obtain the target control input sequence by solving the optimization problem of the objective function within a preset prediction time domain, based on the collected real-time system operation data as the initial state in each control cycle. The control application module 340 is used to apply the first control input value in the target control input sequence to the synchronous condenser excitation system to complete the adjustment of the excitation current of the current cycle.

[0067] It is understood that the relevant content concerning each module in the above-mentioned device has been described in detail in the foregoing method embodiments, and specific details can be found in the method embodiments; that is, the device 300 for optimizing a synchronous condenser excitation system based on model predictive control provided in this application can perform the following... Figure 1 Any steps in the illustrated embodiments will not be described in detail here.

[0068] In one embodiment, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, causes the processor to perform any of the steps in the above method embodiments.

[0069] 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 non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0070] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method of optimizing an excitation system of a phase modifier based on model predictive control, characterized in that, The method comprises: establishing a mathematical model describing dynamic characteristics of an excitation system of a phase modifier for model predictive control, the mathematical model representing a dynamic relationship between an excitation current as a control input and a grid voltage and frequency as control outputs; based on the mathematical model, constructing an objective function, the objective function at least including: an output deviation term, a control input rate of change term, and a commutation failure suppression term; in each control cycle, based on collected real-time system operation data as an initial state, obtaining a target control input sequence by solving an optimization problem of the objective function in a preset prediction time domain; based on the target control input sequence, adjusting the excitation current of the current cycle of the excitation system of the phase modifier.

2. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 1 wherein, The commutation failure suppression term is realized by imposing a penalty on at least one of the excitation current deviation and the frequency deviation.

3. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 2 wherein, The commutation failure suppression term is expressed as: wherein denotes the above-mentioned excitation current deviation, denotes the above-mentioned frequency deviation, P and Q f is the corresponding weighting matrix.

4. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 1 wherein, The method further comprises: online assessment of commutation failure risk; when detecting the commutation failure risk, dynamically increasing the weight of the commutation failure suppression term in the objective function.

5. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 4 wherein, The online assessment of the commutation failure risk comprises: based on at least one of the grid voltage drop depth, the voltage drop duration, or the converter bus voltage waveform distortion rate, to assess the risk level.

6. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 1 wherein, The mathematical model is a linear state space model.

7. The method of model predictive control based optimization of exciter system of a phase modifier as claimed in claim 1 wherein, Solving the optimization problem of the objective function is also subject to physical constraints on at least one of the excitation current and the grid voltage.

8. The method of model predictive control based optimization of exciter system of a generator as claimed in claim 1 wherein, The mathematical model uses a first-order transfer function to model the input and output relationship of the excitation system of the phase modifier, the input of the excitation system of the phase modifier being the excitation current, and the output of the excitation system of the phase modifier being the voltage and the frequency.

9. The method of model predictive control based optimization of a generator excitation system as claimed in claim 8, wherein, The control input rate of change term is realized by weighted penalty on the rate of change of the excitation current.

10. An apparatus for optimizing excitation system of a phase modifier based on model predictive control, characterized in that, It comprises: a model establishing module for establishing a mathematical model describing dynamic characteristics of an excitation system of a phase modifier for model predictive control, the mathematical model representing a dynamic relationship between an excitation current as a control input and a grid voltage and frequency as control outputs; an objective function constructing module for constructing an objective function based on the mathematical model, the objective function at least including: an output deviation term, a control input rate of change term, and a commutation failure suppression term; an optimization solving module for, in each control cycle, based on collected real-time system operation data as an initial state, obtaining a target control input sequence by solving an optimization problem of the objective function in a preset prediction time domain; a control applying module for, based on the target control input sequence, adjusting the excitation current of the current cycle of the excitation system of the phase modifier.

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