A method and system for coordinated design of main circuit and control of a static synchronous compensator without electrolytic capacitor

Abstract

The application provides a method and system for co-design of main circuit and control of a static synchronous compensator without electrolytic capacitor, and belongs to the technical field of control optimization. In view of the problems of isolation of main circuit parameters and control design and poor bus transient stability caused by a film capacitor, the application integrates LCL filter parameters and double closed-loop PI gain into a unified optimization space. By introducing a joint ITAE dynamic evaluation operator, an improved sparrow search algorithm is used for multi-objective optimization of component volume and dynamic performance, and TOPSIS is combined to screen the optimal parameters. The method compensates for the transient fluctuation of the direct-current bus by using an active damping mechanism, while realizing the limit miniaturization of the magnetic component, significantly improving the dynamic response and stability of the system under extreme disturbance, prolonging the service life of the equipment, and having important engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of static synchronous condenser control optimization technology, and particularly relates to a method and system for the coordinated design of main circuit and control of static synchronous condensers for electrolytic capacitor-free operation. Background Technology

[0002] With the rapid evolution of new power systems, static synchronous condensers (STATCOMs), as core equipment for dynamic reactive power and voltage support and power quality management in the power grid, play a crucial role in improving system transient stability. Three-phase pulse width modulation (PWM) converters, as their core topology, are responsible for the rapid exchange of reactive power with the grid. Their power quality, transient response speed, and the physical lifespan of their main circuit components directly determine the operational safety and voltage stability of the distribution network or grid-connected generation system.

[0003] To effectively filter out pulse-width modulation harmonics generated by high-power, high-frequency switching operations and reduce pollution to grid-connected current power quality, passive LCL low-pass filters are typically required on the grid side of converters. LCL filters have become the mainstream choice due to their excellent high-frequency attenuation capabilities. However, in traditional industrial design and development processes, the passive components (inductors, capacitors) of the main circuit and the active digital signal controllers (PI controllers, damping control loops) at the back end are usually designed independently, adopting a "serial, isolated" design pattern. That is, design engineers first calculate the filter parameter combination that meets ripple suppression requirements in isolation, relying on empirical formulas and trial-and-error methods. Subsequently, control engineers tune the controller PI gain to meet the crossover frequency and phase margin requirements on this fixed physical base.

[0004] However, from a control theory perspective, LCL filters not only introduce extremely strong high-frequency attenuation but also introduce a pair of inherent high-frequency resonant poles. This traditional, fragmented design approach neglects the combined effect of the main circuit's frequency domain characteristics and the controller's gain domain. This makes it difficult for the system to achieve a globally optimal solution between the two mutually constraining objectives of "minimizing the volume and cost of passive components (total inductance)" and "optimizing transient dynamic response speed (low integral error)," thus limiting the improvement of overall performance.

[0005] Furthermore, static synchronous condensers are gradually replacing bulky and easily aging electrolytic capacitors with thin-film capacitors. However, thin-film capacitors have extremely small capacitance. When a grid fault causes a voltage drop or the system issues a large-scale reactive power jump command, the DC bus voltage will experience severe transient fluctuations. Under such dynamic transformer conditions, the static gain parameters originally tuned for a specific rated operating point by the controller will produce a severe zero-pole mismatch with the physical model. This mismatch not only reduces the system phase margin but can also induce low-frequency circulating current oscillations during transient processes, seriously threatening the grid connection safety of the synchronous condenser.

[0006] Traditional static synchronous condensers rely heavily on large-capacity electrolytic capacitors to stabilize the DC bus voltage. While large electrolytic capacitors provide ample energy buffering, they suffer from drawbacks such as bulkiness, significant heat generation due to equivalent series resistance (ESR), and limited lifespan. Extending equipment lifespan necessitates replacing the DC-side capacitors with film capacitors. However, reducing capacitor capacity leads to a significant decrease in the system's charge-discharge time constant, resulting in increased transient voltage drops on the bus and consequently affecting the system's closed-loop stability. Therefore, a method is urgently needed that can collaboratively design parameters from both the physical layer model and the digital control layer to balance the physical constraints of film capacitor implementation with multiple performance indicators under complex operating conditions. Summary of the Invention

[0007] Objective: To address the technical problems in existing technologies, such as the disconnect between main circuit parameters and control layer design, and the vulnerability of bus transient stability caused by thin-film capacitors, this invention aims to provide a method and system for the collaborative design of the main circuit and control of a static synchronous condenser (STATCOM) without electrolytic capacitors. This method breaks through the bottleneck of traditional "serial design" by incorporating the physical parameters of the LCL filter and the proportional-integral gain of the dual closed-loop controller into a unified high-dimensional optimization space. It also introduces a joint ITAE dynamic evaluation operator based on multi-point discrete voltage conditions, aiming to achieve a comprehensive optimal balance between minimizing the volume of passive components, maximizing global dynamic stability, and suppressing bus transient fluctuations. This provides a theoretical basis and automated design means for the development of long-life STATCOMs.

[0008] This invention first provides a method for the coordinated design of the main circuit and control of a static synchronous condenser without electrolytic capacitors, comprising the following steps:

[0009] Step 1: Build the main circuit of the static synchronous condenser and the physical model of the dual closed-loop controller consisting of the voltage outer loop and the current inner loop. The physical model of the dual closed-loop controller covers the grid impedance link, the physical parameters of the passive layer and the digital gain configuration of the active control layer.

[0010] Step 2: Define a high-dimensional collaborative parameter space that includes the main circuit parameters of the inductor-capacitor-inductor LCL filter and the proportional-integral (PI) gain of the dual-loop controller, and establish a dynamic performance evaluation mechanism and a passive component cost evaluation function based on multi-point discrete transformer operating conditions in the control program of the dual-loop controller.

[0011] Step 3: Through a multi-objective intelligent evolutionary update operator, an adaptive global search and non-dominated dimensionality reduction sorting are performed on the high-dimensional cooperative parameter space to capture the Pareto front solution set that takes into account both physical volume and dynamic stability.

[0012] Step 4: Introduce an approximation ideal solution sorting algorithm based on multi-attribute decision-making, perform multi-criteria objective decision-making on the Pareto front solution set, select the optimal parameter combination, and map the optimal parameter combination to the dual closed-loop controller to drive the main circuit switching transistor to perform active damping control.

[0013] In step 1, the main circuit of the static synchronous condenser includes a three-phase AC power supply, a grid equivalent impedance, an AC-side LCL low-pass filter, a three-phase pulse width modulation (PWM) converter, and a DC-side bus thin-film capacitor. ;

[0014] The three-phase AC power supply is connected to the input terminal of the AC-side LCL low-pass filter via the grid equivalent impedance. The output terminal of the AC-side LCL low-pass filter is connected to the AC side of the three-phase pulse width modulation (PWM) converter. The DC-side bus thin-film capacitor... It is connected in parallel to both ends of the DC bus of the three-phase pulse width modulation (PWM) converter;

[0015] The AC-side LCL type low-pass filter includes a grid-side filter inductor. Inductor on the converter side and AC side low capacitance film capacitors For each phase (each phase refers to any one of the independent A, B, or C phase branches in a three-phase circuit), the grid-side filter inductor One end is connected to the three-phase AC power supply via the grid's equivalent impedance, and the other end is connected to the converter-side inductor. One end and the low capacitance film capacitor on the AC side One end of the common connection, the converter-side inductor The other end is connected to the corresponding phase AC terminal of the three-phase pulse width modulation (PWM) converter, with low-capacitance thin-film capacitors on the AC side of each phase. The other ends are connected to form the neutral point of a star connection;

[0016] The state-space equation of the main circuit of the static synchronous condenser is characterized as follows:

[0017] (1),

[0018] in, For grid-side three-phase current, The three-phase current on the converter side. These are the three-phase voltages of the AC side capacitors. A is the system state matrix, and B is the input control matrix. Let d be the system input vector, and d represent the derivative;

[0019] DC bus voltage V dc transient change Δ The relationship between the reactive power Q on the AC side is as follows:

[0020] (2),

[0021] Among them, C dc For DC-side bus thin-film capacitors, The transient rate of change of the DC bus voltage. For input power, This refers to the output power.

[0022] Step 2 includes the following steps:

[0023] Step 2.1, define the collaborative parameter combination vector as a 7-dimensional real vector covering the intersection of the physical base and the control operator. ;in, and These are the proportional gain and integral gain of the voltage outer loop, respectively. and These are the proportional gain and integral gain of the inner current loop, respectively.

[0024] Step 2.2: Establish a first optimization objective function f1 for achieving miniaturized design of the main circuit components. The first optimization objective function uses the total magnetic component volume of the LCL filter as the evaluation index, and its physical characterization formula is:

[0025] (1);

[0026] Step 2.3: Establish a second objective function to achieve dynamic performance optimization across all operating conditions. By injecting three discrete bus voltage conditions representing typical transient processes of a static synchronous condenser into the physical model in parallel, namely the bus transient undervoltage condition caused by grid short-circuit dip, the model is improved. Steady-state operating conditions at rated operating point and the transient overvoltage condition of the busbar caused by a sudden decrease in reactive load. Using the integral of time multiplied by the absolute value of error (ITAE) as the dynamic evaluation operator under various operating conditions, a multi-point joint evaluation mechanism is constructed by calculating the cumulative degradation degree of the error at each sampling point. The calculation formula is as follows:

[0027] (2),

[0028] This represents the second objective function used to achieve dynamic performance optimization across all operating conditions. This represents a dynamic evaluation index based on the integration of the joint time multiplied by the absolute value of the error under multi-point discrete voltage conditions, where t is the time weight, used to increase the penalty for the residual error of the system over time. This represents the absolute value of the specific error term.

[0029] Step 2.3 also includes establishing physical security threshold constraints, specifically including the following judgment criteria:

[0030] First criterion: Screening for inherent resonant frequencies, calculating the physical inherent resonant frequency of the LCL filter in the main circuit. :

[0031] (3),

[0032] Determine whether the stability criterion is satisfied. ,in The switching sampling frequency of the control system;

[0033] Second criterion: Transient response amplitude screening to determine the maximum transient fluctuation rate of the DC bus voltage under various discrete voltage conditions. Does it meet the requirements? The safety boundary;

[0034] If the current collaborative parameter combination vector does not satisfy any of the above criteria, then a maximum penalty operation is performed on the objective function, and the current collaborative parameter combination vector is excluded from the effective solution space.

[0035] In step 3, the multi-objective intelligent evolutionary update operator adopts an improved sparrow search mechanism, specifically including the following steps:

[0036] Step 3.1: Initialize the optimization population by generating an initial particle array through a multidimensional Gaussian distribution, where each particle represents a set of cooperative parameter vectors;

[0037] Step 3.2: Divide the population into three levels of roles: discoverers responsible for expanding the global search dimension, joiners responsible for enhancing local convergence accuracy, and vigilants responsible for escaping local optimum traps.

[0038] Step 3.3: Obtain the objective function vector corresponding to each particle in the population through a parallel simulation engine. ,in, The objective function value is optimized to prioritize the total volume of magnetic components. The second objective function value is optimized for dynamic performance under all operating conditions; and a Pareto front library is constructed and maintained in real time by performing non-dominated sorting based on congestion distance.

[0039] In step 3.2, the location update strategy for the discoverer adopts an adaptive exponential decay weight update law, calculated as follows:

[0040] (4),

[0041] in, For the discoverer particle in the next iteration At position 1 in dimension k, It represents the position of the discoverer particle in dimension k at the current iteration t, where i is the current generation. MaxIter is a random factor controlling the step size; exp is the maximum number of generations;

[0042] The joiner's position update strategy utilizes the inner product of multidimensional matrices to achieve fast convergence to the current Pareto optimal region. The calculation formula is as follows:

[0043] (5),

[0044] in, Let the position of the joining particle in the k-th dimension be the position of the particle in the next iteration t+1. Let the position of the joining particle in the k-th dimension be given in the current iteration t. This is the optimal position found in the current population. L .

[0045] In step 4, the algorithm for ranking the approximate ideal solution based on multi-attribute decision-making includes the following steps:

[0046] Step 4.1: Construct a weighted normalized decision matrix and perform geometric normalization on the multidimensional heterogeneous indices of the Pareto front set. The calculation formula is as follows:

[0047] (6),

[0048] in The normalized standard value, The original objective function values ​​are set in the Pareto front;

[0049] Step 4.2, determine the multi-objective positive ideal solution vector. With negative ideal solution vector And calculate the Euclidean distance from each solution vector to the positive and negative ideal solutions. and ;

[0050] The determination of the multi-objective positive ideal solution vector With negative ideal solution vector The calculation formula is:

[0051] (7),

[0052] (8),

[0053] in, and These are the optimal values ​​of the multi-objective positive ideal solution vector on the first optimization objective and the optimal values ​​on the second optimization objective, respectively. and These are the worst values ​​of the negative ideal solution vector for the multi-objective objectives on the first and second objectives, respectively. and These are the normalized standard values ​​of the first and second optimization objectives corresponding to the i-th solution in the Pareto front set, respectively. To find the minimum value operator, To find the maxima operator;

[0054] The Euclidean distances from each solution vector to the positive ideal solution and from each solution vector to the negative ideal solution are calculated using the following formulas:

[0055] (9),

[0056] (10)

[0057] in, Let be the Euclidean distance from the i-th solution vector to the multi-objective positive ideal solution vector. Let be the Euclidean distance from the i-th solution vector to the multi-objective negative ideal solution vector;

[0058] Step 4.3: Calculate the relative closeness evaluation index C by the distance ratio between the positive and negative ideal solutions. i :

[0059] (11),

[0060] in Let be the Euclidean distance from the solution vector to the positive ideal solution vector. It is the Euclidean distance from the solution vector to the negative ideal solution vector;

[0061] Step 4.4: Search for the Pareto front set with the highest relative proximity. The parameter set is used as the final determined collaborative optimization parameter solution.

[0062] The design features aimed at eliminating electrolytic capacitors are reflected in the following: the DC bus capacitor of the main circuit is physically set as a thin-film capacitor with a physically set minimum capacitance value to avoid the aging failure risk of traditional electrolytic capacitors; under this physical boundary constraint, the AC side filter thin-film capacitor C... f The search space is set in the microfarad range of [1μF, 20μF]. Through collaborative optimization, the LCL filtering characteristics and control gain of the AC side are highly matched, thereby compensating for the lack of passive energy storage on the DC side.

[0063] The control strategy employs an active damping control loop, achieving high gain in the inner current loop through collaborative optimization. The provided active feedback path provides real-time dynamic compensation for bus voltage drops caused by transient disturbances in the power grid due to the extremely small filter capacitor.

[0064] In step 4, among the selected optimal parameter combinations, the integral gain of the voltage outer loop is... Through optimized configuration, the integral gain of the outer voltage loop is made capable of compensating for voltage deviations caused by drastic changes in reactive power, ensuring that the system has zero steady-state error in steady state.

[0065] The present invention also provides a co-design system for the main circuit and control of a static synchronous condenser oriented towards electrolytic capacitor-free operation, implemented according to the method, comprising:

[0066] The physical parameter description module stores the mathematical representation and constraint range of the physical parameters of the LCL filter and DC bus; the algorithm optimization module calls the multi-objective optimization core based on the improved sparrow search operator to perform large-scale parallel computing to search for the optimal cooperative parameter vector; the cooperative decision module performs multi-dimensional objective sorting and filtering of the search results through the TOPSIS algorithm; and the controller parameter mapping module writes the finally obtained optimal physical matching gain into the dual closed-loop controller.

[0067] The present invention has the following beneficial effects:

[0068] (1) By constructing a design base for the main circuit and control, this invention breaks the boundary between the physical design of the main circuit and the digital control tuning compared with the traditional isolated design scheme. It reveals the coupling mechanism between the physical parameters of the LCL filter and the active damping capability of the controller from the global system level, effectively reducing the waste of resources and the risk of system pole mismatch caused by design redundancy.

[0069] (2) To address the physical problem that the DC bus voltage of electrolytic capacitor-free STATCOM is highly susceptible to grid disturbances, a joint evaluation operator based on multi-point weighted ITAE was invented. This operator ensures that the system still has sufficient phase margin under extreme transformer conditions (such as grid voltage drops to 0.8 pu), solving the industry problem that microfarad-level thin-film capacitors are difficult to stabilize DC voltage.

[0070] (3) Miniaturization of the STATCOM main circuit was achieved. The current inner loop gain was obtained through collaborative optimization. It provides an extremely high fast crossover frequency. This active dynamic damping mechanism physically compensates for the high-frequency harmonic risk caused by the reduction of the filter inductance, enabling the device to meet stringent power quality requirements in a smaller physical size. Attached Figure Description

[0071] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0072] Figure 1 This is a schematic diagram of the collaborative parameter optimization design process for electrolytic capacitor-free implementation in an embodiment of the present invention.

[0073] Figure 2 This is a diagram of the main circuit topology of a static synchronous condenser for electrolytic capacitor-free operation in an embodiment of the present invention.

[0074] Figure 3 These are the Pareto front distribution map and TOPSIS decision point indication map of the 7-dimensional cooperative parameter space generated in this embodiment of the invention.

[0075] Figure 4 This is a comparison chart of the transient step response of the bus voltage after adopting the co-optimized parameters under three typical discrete DC voltage operating conditions.

[0076] Figure 5 This is a comparison chart of the robustness of STATCOM under transient undervoltage conditions (600V) in the embodiments of the present invention, showing the difference between the collaborative design method of the present invention and the traditional serial design in terms of DC bus voltage response. Detailed Implementation

[0077] like Figure 1 As shown, this invention provides a high-dimensional main circuit and control co-design method for electrolytic capacitor-free static synchronous condensers. Its core physical logic lies in decoupling and then fusing the physical characteristics of the LCL filter with the dynamic bandwidth of the digital controller, achieving a cross-dimensional performance improvement by actively compensating for the lack of passive energy storage. In the physical modeling of step 1, the following is first constructed... Figure 2The diagram shows the main circuit topology for a capacitor-free Static Synchronous Condenser (STATCOM). The main circuit includes a three-phase AC power supply, grid equivalent impedance, an AC-side LCL low-pass filter, a three-phase pulse-width modulation (PWM) converter (VSC), and a DC-side bus thin-film capacitor. The LCL low-pass filter consists of a grid-side filter inductor. Inductor on the converter side It consists of low-capacitance thin-film capacitors on the AC side. Based on Figure 2 The physical topology shown is represented by the state-space equations of STATCOM as follows:

[0078] (1),

[0079] in, For grid-side three-phase current, This refers to the three-phase current on the converter side. This represents the three-phase voltage of the AC-side capacitor. A is the system state matrix, representing the grid-side filter inductor. Inductor on the converter side and filter capacitor C f The physical coupling relationship. B is the input control matrix. This is the system input vector. In traditional serial designs, engineers typically assume the DC bus voltage V... dc The value is constant, thus simplifying the small-signal model. However, in the absence of electrolytic capacitors (i.e., C...), f Under the design goal of being at the microfarad level, the DC voltage V dc The dynamic process of the AC side and the exchange process of reactive current on the AC side are strongly nonlinearly coupled. According to the law of charge conservation, the bus voltage V dc transient change Δ The relationship between the reactive power Q on the AC side is as follows:

[0080] (2),

[0081] Among them, C dc For DC-side bus thin-film capacitors, V dc This is the DC bus voltage. The transient rate of change of the bus voltage. For input power, This refers to the output power. When the system aims for a long lifespan, C... dc From the millifarad level (electrolytic capacitors)

[0082] When compressed to the microfarad level (film capacitors), the same power disturbance will cause voltage fluctuations of a much larger magnitude.

[0083] In step 2 of this embodiment, this nonlinear coupling is made explicit through a 7-dimensional parameter space. The system performs multi-objective optimization, aiming to find a golden set of parameters that balances compact size with fast dynamic response.

[0084] To address the bus stability challenges posed by microfarad-level thin-film capacitors, step 2.3 establishes three extreme transformer testing points. Among them, The lowest voltage point (600V) was simulated when a 20% amplitude drop on the grid side caused a sudden surge in reactive power demand and a violent drain of energy from the bus. The highest operating point (850V) when the system detects the edge of the DC overvoltage protection threshold was simulated.

[0085] In the intelligent optimization process of step 3, this embodiment uses the improved Sparrow Search Algorithm (NS-SSA). During algorithm initialization, the search space is projected onto a standardized 7-dimensional hypercube. In each iteration, the 50 individuals are divided into "discoverers" (20%), "joiners" (70%), and "early warnings" (10%). Discoverers attempt to find the optimal frequency domain matching point between the inductor-capacitor combination of the LCL filter and the controller gain by performing a wide-range Lévy flight search; joiners achieve a fine-grained uniform distribution of the solution set by continuously perturbing the non-dominated individuals in the Pareto library.

[0086] After 30 deep iterations, the system obtained the following: Figure 3 The Pareto front is shown in the figure. Each point in the figure represents a set of physically realizable STATCOM parameter configurations. It can be seen that as the first objective f1 (total inductance) continuously decreases, the second objective f2 (transient error integral) exhibits a significant nonlinear upward trend. This physically corresponds to the evolution process of "decreasing inductance leading to decreased damping capacity and increased bus ripple." The red-starred points calculated by the decision layer using the TOPSIS algorithm represent the minimum passive component volume achievable under the premise of ensuring a phase margin >45° at all discrete operating points.

[0087] Through the TOPSIS algorithm decision in step 4, the optimal global cooperative parameter vector is finally extracted and compared with the traditional serial design scheme. This invention, using only thin-film capacitors, reduces the total inductance of the LCL filter to approximately 40% of the traditional design value, while simultaneously improving the power quality of the grid-connected current. This "physical deficit, algorithmic compensation" approach gives this invention a significant competitive advantage in commercial applications: it not only significantly reduces the raw material cost of STATCOM, but also extends the expected lifespan of the equipment from the traditional 5-8 years to over 15 years through the replacement of thin-film capacitors.

[0088] The physical implementation mechanism of "active damping" in this embodiment of the invention is as follows: In the current loop of the discrete digital control system, the filter capacitor C is extracted by an algorithm. f The high-frequency feedback current icf is multiplied by the optimized virtual resistance gain Rv (which is linearly positively correlated with Kpi) and then added to the control quantity Uref. This mechanism manifests on the Bode plot as the introduction of a controlled phase compensation stage at the inherent resonant peak of the LCL, smoothing out the originally undamped spike. This approach of trading physical margin for software gain is the core technological foundation for realizing electrolytic capacitor-free STATCOM.

[0089] like Figure 4 The simulation verification waveforms show that, when faced with a severe disturbance where reactive power jumps from 0 to 10 kVar, the bus voltage V of the system using the proposed co-design parameters is [value missing]. dc Although there was an initial voltage drop of approximately 45V, thanks to the optimized high-ratio gain K of the inner current loop... pi The system provides wideband ride-through capability (cutoff frequency increased to 1.2kHz), enabling it to rapidly establish active dynamic support within 15ms and pull the bus voltage back to its rated value. This ultra-fast response characteristic makes the synchronous condenser highly valuable for managing voltage transient flicker in distribution networks.

[0090] contrast Figure 5 As can be seen, under the transient undervoltage condition (600V) of STATCOM, the system using the traditional serial design suffers from severe parameter mismatch between the control gain tuning and the physical model, resulting in a severe voltage overshoot at the initial stage of system startup, with a peak value approaching 1200V. Subsequently, the system completely loses stability and collapses, with the DC bus voltage rapidly dropping to near 0V accompanied by continuous chaotic fluctuations. In contrast, the system using the NS-SSAPareto co-design method of this invention exhibits extremely high damping characteristics and robustness in its bus voltage. The voltage curve rises smoothly without significant overshoot, and it stabilizes at the 600V reference voltage after approximately 0.04s and maintains stable operation. This result fully demonstrates the significant superiority of co-design in solving the stability issues of electrolytic capacitor-free STATCOM under extreme operating conditions, as well as the universality and advanced nature of this technology in the development of core equipment for future power systems. The mathematical derivations and experimental analyses described in this specification are intended to demonstrate the technical feasibility and progressiveness of the co-design method of this invention, and not to limit the scope of protection of this invention. All equivalent modifications or system extensions based on this method that fall within the scope of the technical solution of this invention should be included within the scope of the claims of this invention.

[0091] This invention provides a method and system for the coordinated design of the main circuit and control of a static synchronous condenser without electrolytic capacitors. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for the coordinated design of main circuit and control of a static synchronous condenser oriented towards electrolytic capacitor-free operation, characterized in that, Includes the following steps: Step 1: Build the main circuit of the static synchronous condenser and the physical model of the dual closed-loop controller consisting of the voltage outer loop and the current inner loop. The physical model of the dual closed-loop controller covers the grid impedance link, the physical parameters of the passive layer and the digital gain configuration of the active control layer. Step 2: Define a high-dimensional collaborative parameter space that includes the main circuit parameters of the inductor-capacitor-inductor LCL filter and the proportional-integral (PI) gain of the dual-loop controller, and establish a dynamic performance evaluation mechanism and a passive component cost evaluation function based on multi-point discrete transformer operating conditions in the control program of the dual-loop controller. Step 3: Through multi-objective intelligent evolution update operators, adaptive global search and non-dominated dimensionality reduction sorting are performed on the high-dimensional cooperative parameter space to capture the Pareto front solution set that takes into account both physical volume and dynamic stability. Step 4: Introduce an approximation ideal solution sorting algorithm based on multi-attribute decision-making, perform multi-criteria objective decision-making on the Pareto front solution set, select the optimal parameter combination, and map the optimal parameter combination to the dual closed-loop controller to drive the main circuit switching transistor to perform active damping control.

2. The method according to claim 1, characterized in that, In step 1, the main circuit of the static synchronous condenser includes a three-phase AC power supply, a grid equivalent impedance, an AC-side LCL low-pass filter, a three-phase pulse width modulation (PWM) converter, and a DC-side bus thin-film capacitor. ; The three-phase AC power supply is connected to the input terminal of the AC-side LCL low-pass filter via the grid equivalent impedance. The output terminal of the AC-side LCL low-pass filter is connected to the AC side of the three-phase pulse width modulation (PWM) converter. The DC-side bus thin-film capacitor... It is connected in parallel to both ends of the DC bus of the three-phase pulse width modulation (PWM) converter; The AC-side LCL type low-pass filter includes a grid-side filter inductor. Inductor on the converter side and AC side low capacitance film capacitors ; For each phase, the grid-side filter inductor One end is connected to the three-phase AC power supply via the grid's equivalent impedance, and the other end is connected to the converter-side inductor. One end and the low capacitance film capacitor on the AC side One end of the common connection, the converter-side inductor The other end is connected to the corresponding phase AC terminal of the three-phase pulse width modulation (PWM) converter, with low-capacitance thin-film capacitors on the AC side of each phase. The other ends are connected to form the neutral point of a star connection; The state-space equation of the main circuit of the static synchronous condenser is characterized as follows: （1）， in, For grid-side three-phase current, The three-phase current on the converter side. These are the three-phase voltages of the AC side capacitors. A is the system state matrix, and B is the input control matrix. Let d be the system input vector, and d represent the derivative; DC bus voltage V dc The relationship between the transient change and the reactive power Q on the AC side is as follows: （2）， Among them, C dc For DC side bus thin film capacitors, The transient rate of change of the DC bus voltage. For input power, This refers to the output power.

3. The method according to claim 2, characterized in that, Step 2 includes the following steps: Step 2.1, define the collaborative parameter combination vector as a 7-dimensional real vector covering the intersection of the physical base and the control operator. ;in, and These are the proportional gain and integral gain of the voltage outer loop, respectively. and These are the proportional gain and integral gain of the inner current loop, respectively. Step 2.2: Establish a first optimization objective function f1 for achieving miniaturized design of the main circuit components. The first optimization objective function uses the total magnetic component volume of the LCL filter as the evaluation index, and its physical characterization formula is: （1）； Step 2.3: Establish a second objective function to achieve dynamic performance optimization across all operating conditions. By injecting three discrete bus voltage conditions representing typical transient processes of a static synchronous condenser into the physical model in parallel, namely the bus transient undervoltage condition caused by grid short-circuit dip, the model is improved. Steady-state operating conditions at rated operating point and the transient overvoltage condition of the busbar caused by a sudden decrease in reactive load. Using the integral of the absolute value of the error multiplied by the time as the dynamic evaluation operator under each working condition, a multi-point joint evaluation mechanism is constructed by calculating the cumulative degradation degree of the error at each sampling point. The calculation formula is as follows: （2）， This represents the second objective function used to achieve dynamic performance optimization across all operating conditions. This represents the dynamic evaluation index based on the integration of the joint time multiplied by the absolute value of the error under multi-point discrete voltage conditions, where t is the time weight. This represents the absolute value of the specific error term.

4. The method according to claim 3, characterized in that, Step 2.3 also includes establishing physical security threshold constraints, specifically including the following judgment criteria: First criterion: Screening for inherent resonant frequencies, calculating the physical inherent resonant frequency of the LCL filter in the main circuit. : （3）， Determine whether the stability criterion is satisfied. ,in The switching sampling frequency of the control system; Second criterion: Transient response amplitude screening to determine the maximum transient fluctuation rate of the DC bus voltage under various discrete voltage conditions. Does it meet the requirements? The safety boundary; If the current collaborative parameter combination vector does not satisfy any criterion, then a maximum penalty operation is performed on the objective function, excluding the current collaborative parameter combination vector from the effective solution space.

5. The method according to claim 4, characterized in that, In step 3, the multi-objective intelligent evolutionary update operator adopts an improved sparrow search mechanism, specifically including the following steps: Step 3.1: Initialize the optimization population by generating an initial particle array through a multidimensional Gaussian distribution, where each particle represents a set of cooperative parameter vectors; Step 3.2: Divide the population into three levels of roles: discoverers responsible for expanding the global search dimension, joiners responsible for enhancing local convergence accuracy, and vigilants responsible for escaping local optimum traps. Step 3.3: Obtain the objective function vector corresponding to each particle in the population through a parallel simulation engine. ,in, The objective function value is optimized to prioritize the total volume of magnetic components. The second objective function value is optimized for dynamic performance under all operating conditions; and a Pareto front library is constructed and maintained in real time by performing non-dominated sorting based on congestion distance.

6. The method according to claim 5, characterized in that, In step 3.2, the location update strategy for the discoverer adopts an adaptive exponential decay weight update law, calculated as follows: （4）， in, For the discoverer particle in the next iteration At position 1 in dimension k, It represents the position of the discoverer particle in dimension k at the current iteration t, where i is the current generation. MaxIter is a random factor controlling the step size; exp is the maximum number of generations; The joiner's position update strategy utilizes the inner product of multidimensional matrices to achieve fast convergence to the current Pareto optimal region. The calculation formula is as follows: （5）， in, Let the position of the joining particle in the k-th dimension be the position of the particle in the next iteration t+1. Let the position of the joining particle in the k-th dimension be given in the current iteration t. This is the optimal position found in the current population. L .

7. The method according to claim 6, characterized in that, In step 4, the algorithm for ranking the approximate ideal solution based on multi-attribute decision-making includes the following steps: Step 4.1: Construct a weighted normalized decision matrix and perform geometric normalization on the multidimensional heterogeneous indices of the Pareto front set. The calculation formula is as follows: （6）， in The normalized standard value, The original objective function values ​​are set in the Pareto front; Step 4.2, determine the multi-objective positive ideal solution vector. With negative ideal solution vector And calculate the Euclidean distance from each solution vector to the positive and negative ideal solutions. and ; The determination of the multi-objective positive ideal solution vector With negative ideal solution vector The calculation formula is: （7）， （8）， in, and These are the optimal values ​​of the multi-objective positive ideal solution vector on the first optimization objective and the optimal values ​​on the second optimization objective, respectively. and These are the worst values ​​of the negative ideal solution vector for the multi-objective objectives on the first and second objectives, respectively. and These are the normalized standard values ​​of the first and second optimization objectives corresponding to the i-th solution in the Pareto front set, respectively. To find the minimum value operator, To find the maxima operator; Step 4.3: Calculate the relative closeness evaluation index C by the distance ratio between the positive and negative ideal solutions. i ; Step 4.4: Search for the Pareto front set with the highest relative proximity. The parameter set is used as the final determined collaborative optimization parameter solution.

8. The method according to claim 7, characterized in that, In step 4.2, the Euclidean distance from each solution vector to the positive ideal solution and the Euclidean distance from each solution vector to the negative ideal solution are calculated using the following formulas: （9）， （10）， in, Let be the Euclidean distance from the i-th solution vector to the multi-objective positive ideal solution vector. Let be the Euclidean distance from the i-th solution vector to the negative ideal solution vector of the multi-objective problem.

9. The method according to claim 8, characterized in that, In step 4.3, the calculation formula is: （11）， in Let be the Euclidean distance from the solution vector to the positive ideal solution vector. It is the Euclidean distance from the solution vector to the negative ideal solution vector.

10. A main circuit and control co-design system for electrolytic capacitor-free static synchronous condenser implemented according to any one of claims 1 to 9, characterized in that, include: The physical parameter description module stores the mathematical representation and constraint range of the physical parameters of the LCL filter and DC bus; the algorithm optimization module calls the multi-objective optimization core based on the improved sparrow search operator to perform large-scale parallel computing to search for the optimal cooperative parameter vector; the cooperative decision module performs multi-dimensional objective sorting and filtering of the search results through the TOPSIS algorithm; and the controller parameter mapping module writes the finally obtained optimal physical matching gain into the dual closed-loop controller.

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