Droop coefficient adaptive regulation method and system for multi-terminal flexible HVDC power transmission system

Abstract

This application relates to the field of multi-terminal flexible DC transmission system equipment, specifically to a method and system for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The method includes: real-time monitoring and acquisition of local electrical quantities; inputting the electrical quantities as input variables to a main fuzzy logic controller; outputting an initial adjustment amount for the droop coefficient; inputting the absolute values ​​of the real-time power margin deviation and DC voltage deviation to the corresponding auxiliary fuzzy logic controllers to obtain a power margin weighting factor and a DC voltage weighting factor, which are then used to weight and correct the initial adjustment amount to obtain the droop coefficient adjustment amount; adding the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and performing amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient; applying the method to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics, thus forming a hierarchical intelligent decision-making system.

Description

Technical Field

[0001] This application relates to the field of multi-terminal flexible DC transmission system equipment, and specifically to a method and system for adaptive adjustment of droop coefficient in multi-terminal flexible DC transmission systems. Background Technology

[0002] Modular multilevel converter (MMC-MTDC) multi-terminal flexible direct current transmission systems are an important technical means to realize large-scale cross-regional transmission of renewable energy and grid interconnection. In this system, coordinating multiple converter stations to achieve reasonable power distribution and maintain DC voltage stability is one of the core control challenges. Droop control strategies are widely used and have high engineering application value because they can achieve autonomous coordination and automatic power distribution among multiple converter stations without relying on high-speed inter-station communication.

[0003] However, as system operating conditions become increasingly complex, the inherent defects of traditional droop control methods are gradually exposed, mainly in the following two aspects: First, traditional droop control uses a pre-set fixed droop coefficient, which cannot adapt to the dynamic changes in the actual operating state of the system. When the system experiences transient processes such as power fluctuations, master station shutdown, or sudden changes in renewable energy power, the control response with a fixed coefficient often struggles to balance DC voltage control accuracy and power distribution balance. This static parameter setting method makes it difficult to achieve a dynamic balance between voltage stability and power distribution under complex operating conditions.

[0004] Secondly, the power margin of each converter station in the system changes dynamically with the operating mode, source load status, and equipment status. However, traditional droop control is based on a fixed power allocation ratio and cannot reflect the real-time carrying capacity of the converter stations. This may cause some converter stations to exceed their limits due to sudden power increases, or even trigger overload protection actions, exacerbating system operation risks and threatening the safety and stability of the entire power transmission system. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an adaptive adjustment method and system for the droop coefficient in a multi-terminal flexible DC transmission system. When adjusting the droop coefficient, the introduced power margin deviation influence factor can change the problem of uneven power distribution, allowing converter stations with large power margins to receive more power, while converter stations with small power margins avoid power over-limit issues. The introduced DC voltage threshold deviation influence factor can improve the operational stability of the DC voltage.

[0006] In a first aspect, the present invention provides a method for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The system includes a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller. The method includes the following steps: S1. Monitor and acquire local electrical quantities in real time, including system DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation of the converter station. S2. The acquired DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation are input as input variables to the main fuzzy logic controller; the main fuzzy logic controller performs inference based on the preset fuzzy rule base and outputs the preliminary adjustment amount of the droop coefficient; S3. Input the real-time power margin deviation into the first auxiliary fuzzy logic controller to obtain the power margin weighting factor; input the absolute value of the DC voltage deviation into the second auxiliary fuzzy logic controller to obtain the DC voltage weighting factor; use the DC voltage weighting factor and the power margin weighting factor to perform weighted correction on the preliminary adjustment amount to obtain the droop coefficient adjustment amount; S4. Add the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and perform amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient, ensuring that it is within the preset safe operating range; S5. Apply the final real-time droop coefficient to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics.

[0007] As a further limitation of the technical solution of the present invention, step S1 includes: S11. Measure the real-time voltage value of the DC bus of the converter station and obtain the rated reference value of the system DC voltage; S12. Calculate the system DC voltage deviation based on the real-time voltage value and the rated reference value; S13. Perform differential calculation on the DC voltage deviation, or calculate the difference of the DC voltage deviation within a continuous sampling period to obtain the DC voltage deviation change rate; S14. Measure the real-time transmission power P of the converter station and obtain the maximum allowable transmission power of the converter station. ; S15. Based on the real-time transmission power P and the maximum allowable transmission power Calculate the real-time power margin deviation of the converter station. The calculation formula is: Among them, the real-time power margin deviation reflects the relative power margin based on the rated capacity.

[0008] By employing clearly defined measurement and calculation procedures, the accuracy and real-time performance of the input signals are ensured. In particular, the calculation of the DC voltage deviation rate of change provides the controller with crucial information for predicting system dynamic trends. By using relative values ​​to define the power margin deviation, a unified and comparable margin measurement standard is provided for converter stations of different capacities and operating points.

[0009] As a further limitation of the technical solution of the present invention, in S2, the main fuzzy logic controller performs inference according to a preset fuzzy rule base, specifically including the following steps: S21. Input variables, including DC voltage deviation. DC voltage deviation change rate and real-time power margin deviation Based on their respective predefined membership functions, they are converted into corresponding fuzzy quantities; S22. Input the obtained fuzzy quantity into the preset fuzzy rule base; synthesize the initial adjustment amount of the output droop coefficient through fuzzy logic operation. The fuzzy quantity; S23. Convert the fuzzy amount of the obtained preliminary adjustment into an output value, i.e., the preliminary adjustment amount of the droop coefficient, through a defuzzification method. .

[0010] As a further limitation of the technical solution of the present invention, in S21, converting the input variable into the corresponding fuzzy quantity specifically includes the following operations: For each precise value of the input variable, the following operations are performed respectively: S21a. Map the precise value onto the corresponding normalized universe of discourse; S21b. For all fuzzy subsets defined by the variable, calculate the membership degree of the precise value belonging to each fuzzy subset; the membership degree is obtained by querying the respective predefined membership degree function, and the value is in the interval [0,1]. S21c. All the obtained fuzzy subsets and membership pairs together constitute the fuzzy quantity corresponding to the input variable.

[0011] By employing standard steps such as domain mapping, membership query, and fuzzy quantity formation, continuous physical quantities are accurately converted into semantic information that can be processed by fuzzy rules.

[0012] As a further limitation of the technical solution of the present invention, in S22, the preliminary adjustment amount of the output droop coefficient is synthesized through fuzzy logic operation. The fuzzy quantity specifically includes the following steps: S22a. Traverse each rule in the preset fuzzy rule base and match the input fuzzy quantity with the preconditions of the rule; for each rule, use the minimum operation or product operation to calculate the activation strength of the rule. S22b. For each activated rule, the membership function of the output fuzzy subset specified in the rule conclusion is subjected to a minimum or scaling operation using the activation intensity to obtain the modified output fuzzy set. S22c: The output fuzzy sets obtained after modifying all activated rules through S22b are synthesized using the maximum value operation, and aggregated into a single total output fuzzy set; this set is the initial adjustment amount of the droop coefficient. The amount of ambiguity.

[0013] As a further limitation of the technical solution of the present invention, in S23, the fuzzy value of the obtained preliminary adjustment amount is converted into an output value through a defuzzification method, specifically using the center method. The steps include: S23a, Output variables The universe of discourse is discretized with a preset precision to obtain a series of sampling points. ; S23b, For each sampling point Calculate the membership value in the total output fuzzy set synthesized in step S22. And calculate the weighted sum of all sampling points and the sum of membership degrees; S23c. Divide the weighted sum by the sum of membership degrees to obtain the final precise output value, which is the initial adjustment amount of the droop coefficient. The calculation formula is:

[0014] In the formula, N is the total number of sampling points.

[0015] As a further limitation of the technical solution of the present invention, in S3, the power margin weighting factor is obtained by the first auxiliary fuzzy logic controller. Specifically, it includes: S31a. The input precise quantity, i.e. the real-time power margin deviation, is converted into the corresponding fuzzy quantity according to the membership function predefined for the first auxiliary fuzzy logic controller. S32a. The obtained fuzzy quantity is input into the fuzzy rule base of the first auxiliary fuzzy logic controller, and the output variable, namely the power margin weighting factor, is synthesized through fuzzy logic operation. The fuzzy quantity; S33a, the obtained power margin weighting factor The fuzzy quantity is converted into an accurate power margin weighting factor through a defuzzification method. The value of .

[0016] As a further limitation of the technical solution of the present invention, in S3, the DC voltage weighting factor is obtained by the second auxiliary fuzzy logic controller. Specifically, it includes: S31b: Convert the input precise quantity, i.e., the absolute value of the DC voltage deviation, into the corresponding fuzzy quantity according to the membership function predefined for the second auxiliary fuzzy logic controller; S32b: Input the obtained fuzzy quantity into the fuzzy rule base of the second auxiliary fuzzy logic controller, and synthesize the output variable, namely the DC voltage weighting factor, through fuzzy logic operation. The fuzzy quantity; S33b, DC voltage weighting factor The fuzzy quantities are converted into DC voltage weighting factors through a defuzzification method. The value of .

[0017] As a further limitation of the technical solution of the present invention, in S4, the real-time droop coefficient is... Amplitude limiting is performed to obtain the final real-time droop coefficient. The steps include: Will The coefficient is compared with the preset lower and upper limits of the droop coefficient, and the final real-time droop coefficient is output according to the following formula. :

[0018] in, This is the lower limit of the droop coefficient. This is the upper limit of the droop coefficient.

[0019] Converter stations that can utilize droop control strategies can rationally allocate transmission power based on their real-time power margins when dealing with various operating conditions, thereby maintaining stable system operation and reducing voltage deviation. At the same time, the parameters used in the control strategy are all local system parameters and do not depend on the inter-station communication system.

[0020] Thirdly, the technical solution of the present invention also provides a multi-terminal flexible DC transmission system, including: at least three converter stations, wherein at least one converter station is configured as a constant DC voltage control station, and at least two converter stations are configured as droop control stations; The droop control station includes an adaptive controller that integrates a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller, and is configured to perform the method as described in the first aspect.

[0021] As a further limitation of the technical solution of the present invention, the adaptive controller specifically includes: The monitoring module is configured to monitor and acquire local electrical quantities in real time; The main fuzzy decision module, whose function corresponds to the main fuzzy logic controller, is configured to input the acquired DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation as input variables to the main fuzzy logic controller; the main fuzzy logic controller performs inference based on the preset fuzzy rule base and outputs the preliminary adjustment amount of the droop coefficient; The auxiliary evaluation and correction module integrates the functions of the first auxiliary fuzzy logic controller and the second auxiliary fuzzy logic controller. It is configured to input the real-time power margin deviation into the first auxiliary fuzzy logic controller to obtain the power margin weighting factor; input the absolute value of the DC voltage deviation into the second auxiliary fuzzy logic controller to obtain the DC voltage weighting factor; and use the DC voltage weighting factor and the power margin weighting factor to perform weighted correction on the preliminary adjustment amount to obtain the droop coefficient adjustment amount. The coefficient synthesis and limiting module is configured to add the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and to perform limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient, ensuring that it is within the preset safe operating range. The parameter output module is configured to send the final real-time droop coefficient as a control parameter to the droop controller of the converter station via the internal communication bus or shared memory of the converter station.

[0022] As can be seen from the above technical solutions, this application has the following advantages: By combining the comprehensive decision-making of the main fuzzy controller with the collaborative correction of the two auxiliary controllers, the traditional static mode with a fixed droop coefficient is changed, enabling the control system to respond in real time to complex changes in system operating conditions. This method can intelligently adjust the dynamic priority between maintaining DC voltage stability and ensuring fair power distribution—two sometimes conflicting objectives—based on the severity of the DC voltage deviation and the real-time power margin of the converter station, thereby achieving optimal overall system performance under complex operating conditions. The division of labor between the main fuzzy controller and the two auxiliary fuzzy controllers is clearly defined, forming a hierarchical intelligent decision-making system.

[0023] Fuzzy logic control strategies can mimic the human brain's fuzzy reasoning and fuzzy decision-making, making them more suitable for complex on-site applications compared to traditional control algorithms.

[0024] When adjusting the droop coefficient, the introduced power margin deviation factor can change the problem of uneven power distribution. Converter stations with large power margins can be allocated more power, while converter stations with small power margins can avoid power over-limit problems. The introduced DC voltage threshold deviation factor can improve the operating stability of DC voltage. Attached Figure Description

[0025] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying 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.

[0026] Figure 1This is a flowchart illustrating the method provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the control structure of the technical solution of the present invention.

[0028] Figure 3 This is a schematic diagram of the membership function of the input variable DC voltage deviation.

[0029] Figure 4 This is a schematic diagram of the membership function of the input variable DC voltage deviation rate of change.

[0030] Figure 5 This is a schematic diagram of the membership function of the real-time power margin deviation of the input variable.

[0031] Figure 6 This is a schematic diagram of the membership function of the change value of the droop coefficient of the output variable. Detailed Implementation

[0032] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0034] A large-scale new energy base has been equipped with a ±500kV multi-terminal flexible DC transmission system (MMC-MTDC), which includes four converter stations: Converter station A: Located in the center of the wind farm cluster, it serves as a constant DC voltage control station, undertaking the function of maintaining the DC voltage reference of the system, with a rated transmission power of ±1500MW; Converter stations B / C: Both are droop control stations, connected to photovoltaic power stations in different areas, with a rated transmission power of ±1000MW; Converter station D: droop control station, connected to the load center of a coastal city, with a rated transmission power of ±1200MW.

[0035] During system operation, the following typical operating conditions are often encountered: Fluctuations in renewable energy power: Photovoltaic power surges at midday, while wind power fluctuates randomly at night; Sudden load change: Industrial load in coastal cities surged by 600MW during the morning peak period; Equipment Transient Status: Converter station A is out of service due to a valve-side reactor failure, and the system needs to quickly switch to converter station D to temporarily assume the constant voltage control function.

[0036] Traditional fixed sagging coefficient control (sagging coefficients for stations B / C / D are set to 0.05, 0.05, and 0.06 respectively). The above-mentioned operating conditions have significant drawbacks: when the power of new energy sources increases sharply, converter stations B / C frequently trigger overload protection due to insufficient power margin; when the load changes abruptly, the DC voltage deviation overshoot far exceeds the allowable range. The adaptive regulation system based on the method of this application can effectively solve the above problems, which will be explained in detail below with specific operating conditions.

[0037] like Figure 1 As shown in the figure, this invention provides an adaptive adjustment method for the droop coefficient of a multi-terminal flexible DC transmission system. The method mainly consists of two steps. The first step is a fuzzy logic control algorithm, which aims to analyze the current state of the system and determine the adjustment strategy for the droop coefficient using local electrical quantities. The second step introduces a power margin deviation influence factor and a voltage threshold deviation influence factor to obtain a piecewise control strategy based on these two influence factors to achieve adaptive adjustment of the droop coefficient. The system includes a fuzzy controller, specifically a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller. The basic parameter configurations of each controller are shown in Table 1. The method includes the following steps: S1. Monitor and acquire local electrical quantities in real time, including system DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation of the converter station. S2. The acquired DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation are input as input variables to the main fuzzy logic controller; the main fuzzy logic controller performs inference based on the preset fuzzy rule base and outputs the preliminary adjustment amount of the droop coefficient; S3. Input the real-time power margin deviation into the first auxiliary fuzzy logic controller to obtain the power margin weighting factor; input the absolute value of the DC voltage deviation into the second auxiliary fuzzy logic controller to obtain the DC voltage weighting factor; use the DC voltage weighting factor and the power margin weighting factor to perform weighted correction on the preliminary adjustment amount to obtain the droop coefficient adjustment amount; S4. Add the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and perform amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient, ensuring that it is within the preset safe operating range; S5. Apply the final real-time droop coefficient to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics.

[0038] Table 1: Basic Parameters of the Controller

[0039] In some embodiments, step S1 includes: S11. Measure the real-time voltage value of the DC bus of the converter station and obtain the rated reference value of the system DC voltage; S12. Calculate the system DC voltage deviation based on the real-time voltage value and the rated reference value; S13. Perform differential calculation on the DC voltage deviation, or calculate the difference of the DC voltage deviation within a continuous sampling period to obtain the DC voltage deviation change rate; S14. Measure the real-time transmission power P of the converter station and obtain the maximum allowable transmission power of the converter station. ; S15. Based on the real-time transmission power P and the maximum allowable transmission power Calculate the real-time power margin deviation of the converter station. The calculation formula is: Among them, the real-time power margin deviation reflects the relative power margin based on the rated capacity.

[0040] In this embodiment of the invention, the rule design principles of the fuzzy rule base are as follows: when Greater than the first set value and This indicates that when the power margin is sufficient, the output is greater than the first absolute value. Prioritize voltage restoration; when This indicates a severe lack of power margin, even If the value is greater than the first set value, the output should be less than the second absolute value or a negative value. To prevent converter station overload; when Less than the second set value but This indicates that when the voltage is rapidly deviating from the rated value, the absolute value of the output set range is... To implement preventative control measures.

[0041] The fuzzy rule base contains rules that comprehensively consider the coupling relationship between DC voltage state and power margin state. Some core rules are as follows: like It is PB and It is PB and is ZO, then It's PB; like It is PB and It is NS and If it's PS, then It's Photoshop; like It's ZO and It's ZO and is ZO, then It’s ZO; like It is NB and It is PM and If it is NB, then It's NB; like It is NS and It is NB and If it is NS, then It is NM.

[0042] In some embodiments, in S2, the main fuzzy logic controller performs inference based on a preset fuzzy rule base, specifically including the following sub-steps: S21. Input precise quantities, including DC voltage deviation. DC voltage deviation change rate and real-time power margin deviation Based on their respective predefined membership functions, they are converted into corresponding fuzzy quantities; here, DC voltage deviation... The domain of discourse is divided into 5 fuzzy subsets: {Negative Large (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Large (PB)}; The DC voltage deviation change rate The domain of discourse is divided into 5 fuzzy subsets: {NB, NS, ZO, PS, PB}; The real-time power margin deviation The domain of discourse is divided into 7 fuzzy subsets: {NB, NM, NS, ZO, PS, PM, PB}.

[0043] It should be noted that the first step in constructing a fuzzy logic controller is to determine the number of fuzzy subsets for the input and output variables. The fuzzy controller in this application is a three-input, single-output control model. The fuzzy inference rules for three inputs are relatively complex. To balance control effectiveness with the complexity of the fuzzy rules, the voltage deviation influence factor is controlled by two inputs, so the number of fuzzy subsets for both the DC voltage deviation and the rate of change of the DC voltage deviation is set to 5. The power margin influence factor has only a single input, the real-time power margin, so the number of fuzzy subsets for the real-time power margin is set to 7. Similarly, the number of fuzzy subsets for the single-output droop coefficient change value is also set to 7.

[0044] For example, when =ZO, when the voltage deviation tends to stabilize, the fuzzy rule table is shown in Table 2; Table 2: Fuzzy rule table when =ZO

[0045] when =PS, when the voltage deviation is increasing, the fuzzy rule table is shown in Table 3; Table 3: =Fuzzy rule table during PS

[0046] In this step, converting the input precise quantity into the corresponding fuzzy quantity specifically includes the following operations: For each precise input value, execute the following: S21a. Map the precise value to the corresponding normalized universe of discourse; the universe of discourse matching adopts a linear transformation formula, taking the DC voltage deviation ΔU as an example, the transformation formula is:

[0047] in, This is the bias value. This is the gain coefficient, used to convert the actual... The value is mapped to its standard universe of discourse.

[0048] S21b: For all fuzzy subsets defined by the variable, calculate the membership degree of the precise value belonging to each fuzzy subset; the membership degree is obtained by querying the respective predefined membership degree function, and the value is in the interval [0, 1]; in S21b, for the triangular membership degree function, the membership degree is calculated by linear interpolation; for the Sigmoid or Z-type membership degree function, the membership degree is obtained by direct function calculation.

[0049] S21c. All the obtained (fuzzy subset, membership degree) pairs together constitute the fuzzy quantity corresponding to the precise input variable.

[0050] S22. Input the obtained fuzzy quantity into the preset fuzzy rule base; synthesize the initial adjustment amount of the output droop coefficient through fuzzy logic operation. The fuzzy quantity; in this step, the initial adjustment amount of the output droop coefficient is synthesized through fuzzy logic operations. The fuzzy quantity specifically includes the following steps: S22a. Traverse each rule in the preset fuzzy rule base and match the input fuzzy quantity with the preconditions of the rule; for each rule, use the minimum operation or product operation to calculate the activation strength of the rule. It should be noted here that for a rule with multiple connected preconditions, the activation strength... The calculation formula is:

[0051] in, These are the membership degrees of the input precise value on the corresponding fuzzy subset specified by the rule preconditions.

[0052] The "minimum operation" refers to: dividing the upper boundary of the membership function of the output fuzzy subset according to the activation strength. Truncation is performed; the scaling operation refers to multiplying all values ​​of the membership function of the output fuzzy subset by the activation intensity proportionally. .

[0053] The "maximum operation" refers to comparing the membership values ​​of all modified output fuzzy sets point by point on the universe of discourse of the output variable and taking the maximum value to form the synthesized total output fuzzy set.

[0054] Suppose two rules are activated: Rule 1: If ΔU is PB and If it's PB, then... It's PB. (Activation intensity) = 0.8).

[0055] Rule 2: If ΔU is PS and If it's Photoshop, then... It's PS. (Activation intensity) = 0.5).

[0056] S22b. For each activated rule, the membership function of the output fuzzy subset specified in the rule conclusion is subjected to a minimum or scaling operation using the activation intensity to obtain the modified output fuzzy set. S22c: The output fuzzy sets obtained after modifying all activated rules through S22b are synthesized using the maximum value operation, and aggregated into a single total output fuzzy set; this set is the initial adjustment amount of the droop coefficient. The amount of ambiguity.

[0057] S23. Convert the fuzzy amount of the obtained preliminary adjustment into an output value, i.e., the preliminary adjustment amount of the droop coefficient, through a defuzzification method. In this step, the fuzzy values ​​of the initial adjustment are converted into output values ​​using a defuzzification method. Specifically, the center method is employed, and the steps include: S23a, Output variables The universe of discourse is discretized with a preset precision to obtain a series of sampling points. ; S23b, For each sampling point Calculate the membership value in the total output fuzzy set synthesized in step S22. And calculate the weighted sum of all sampling points and the sum of membership degrees; S23c. Divide the weighted sum by the sum of membership degrees to obtain the final precise output value, which is the initial adjustment amount of the droop coefficient. The calculation formula is:

[0058] In the formula, N is the total number of sampling points.

[0059] In S23a, the precision of the discretization sampling is not less than 1 / 100 of the domain range of the output variable.

[0060] Prior to S23b, a validity verification step was included: determining whether the sum of membership degrees was zero; if it was zero, it was determined that no valid fuzzy rule was activated, and the default adjustment amount was output. =0, to avoid division by zero errors.

[0061] The defuzzification method alternatively adopts the maximum average method, and the specific steps are as follows: Find the set of all points with the maximum membership value in the synthesized total output fuzzy set in step S22; calculate the arithmetic mean of all points in this set; use this arithmetic mean as the precise output value. Output.

[0062] There are two input variables for the DC voltage deviation influence factor. The two-input fuzzy control rule for the DC voltage deviation influence factor is established as follows: (1) When the DC voltage deviation of the system is small and there is no trend of further change, that is, when it is small, the system does not need to strengthen the control of DC voltage and the droop coefficient should remain as constant as possible. (2) When the DC voltage deviation of the system is small but the DC voltage fluctuation tends to continue to increase, that is, the DC voltage is changing rapidly in a direction away from the DC voltage reference value, the system needs to strengthen the control capability of the DC voltage in advance and increase the droop coefficient slightly. (3) When the DC voltage deviation of the system is small, but the DC voltage fluctuation has a tendency to continue to decrease, that is, the DC voltage is changing rapidly toward the DC voltage reference value. In order to avoid the control range of the constant DC voltage control port and the droop control port from overlapping, the droop coefficient is slightly reduced. (4) The fuzzy control rules when the DC voltage deviation of the system is large are similar to those when the DC voltage deviation of the system is small, except that the change value of the droop coefficient is fine-tuned under the premise of increasing the droop coefficient.

[0063] Based on this fuzzy control relationship, a power margin deviation influence factor is added. By comprehensively analyzing the fuzzy control rules of the power margin deviation factor and the DC voltage deviation influence factor on the droop coefficient change, a two-input fuzzy control rule for the power margin deviation factor and the voltage deviation influence factor is obtained: (1) When the DC voltage deviation of the system is large and the power margin is sufficient, the droop factor is increased to maintain the DC voltage. (2) When the power margin of the system is insufficient for the normal power distribution of the system, the droop factor needs to be reduced to ensure the power distribution characteristics of the system; (3) When the DC voltage deviation of the system is small and approximately zero or the power margin is zero, a fixed droop factor should be maintained.

[0064] The fuzzy set obtained by the fuzzy inference controller is clarified to obtain the change value of the droop coefficient. This change value is added to the initial droop coefficient of the system to obtain the real-time changing droop coefficient.

[0065] In some embodiments, in S3, the power margin weighting factor α is obtained by the first auxiliary fuzzy logic controller, specifically including: S31a. The precise input quantity, i.e., the real-time power margin deviation, is converted into a corresponding fuzzy quantity according to the membership function predefined for the first auxiliary fuzzy logic controller; this step includes: converting the precise input quantity into a fuzzy quantity. Values ​​are mapped to their normalized universe of discourse; the membership function is queried and calculated. The membership degree of the value belongs to each fuzzy subset (NB, NM, NS, ZO, PS, PM, PB); forming a fuzzy quantity consisting of (fuzzy subset, membership degree) pairs.

[0066] S32a. The obtained fuzzy quantity is input into the fuzzy rule base of the first auxiliary fuzzy logic controller, and the output variable, namely the power margin weighting factor, is synthesized through fuzzy logic operation. The fuzzy quantity; This step specifically includes: inputting... The fuzzy value is matched with the rules in the rule base, and the activation strength of each rule is calculated by taking the smaller value. The activation intensity is used to perform a minimization or scaling operation on the membership function of the output fuzzy subset (VS, S, M, L, VL) corresponding to the rule conclusion; The output fuzzy sets of all activated rules are combined using the maximum value operation to obtain... The amount of ambiguity.

[0067] S33a, the obtained power margin weighting factor The fuzzy quantity is converted into an accurate power margin weighting factor through a defuzzification method. The value of . In this step, it is converted into a power margin weighting factor through a defuzzification method. The value is specifically implemented using the central method, and the steps include: Output variables The universe of discourse is discretized with a preset precision to obtain a series of sampling points. ; For each sampling point Calculate the membership value in the total output fuzzy set synthesized in step S32a. And calculate the weighted sum of all sampling points and the sum of membership degrees; Dividing the weighted sum by the sum of membership degrees yields the final, accurate power margin weighting factor. The value of is calculated using the following formula:

[0068] In the formula, P represents the total number of sampling points.

[0069] The root is to determine whether the sum of membership degrees is less than a very small positive threshold. In this embodiment of the invention, the value is 0.0001; if so, the current fuzzy inference result is determined to be invalid, and it is the power margin weight factor. Assign a preset security default value , where 0 < 1.

[0070] The defuzzification method alternatively adopts the maximum average method, and the specific steps are as follows: Find the set of all points with the maximum membership value in the synthesized total output fuzzy set in step S32a; calculate the arithmetic mean of all points in this set; use this arithmetic mean as the precise power margin weighting factor. Output the value.

[0071] The rule table for the first auxiliary fuzzy logic controller, as shown in Table 4, is designed to assess the feasibility of power adjustment. A larger power margin results in a larger weighting factor, allowing for greater adjustments; insufficient margin strongly suppresses adjustments.

[0072] Table 4: Rule Table for the First Auxiliary Fuzzy Logic Controller

[0073] Correspondingly, the rule table for the second auxiliary fuzzy logic controller is shown in Table 5. In S3, the DC voltage weighting factor is obtained by the second auxiliary fuzzy logic controller. Specifically, it includes: S31b: Convert the input precise quantity, i.e., the absolute value of the DC voltage deviation, into the corresponding fuzzy quantity according to the membership function predefined for the second auxiliary fuzzy logic controller; S32b: Input the obtained fuzzy quantity into the fuzzy rule base of the second auxiliary fuzzy logic controller, and synthesize the output variable, namely the DC voltage weighting factor, through fuzzy logic operation. The fuzzy quantity; S33b, DC voltage weighting factor The fuzzy quantities are converted into DC voltage weighting factors through a defuzzification method. The value of .

[0074] Table 5: Rule Table for the Second Auxiliary Fuzzy Logic Controller

[0075] The specific calculation process is similar to that of the second auxiliary fuzzy logic controller, and will not be elaborated here.

[0076] In this embodiment of the invention, in S4, the real-time droop coefficient is... Amplitude limiting is performed to obtain the final real-time droop coefficient. The steps include: Will Compared with the preset lower limit of the droop coefficient and upper limit value Compare the results and output the final real-time droop coefficient using the following formula. :

[0077] In this embodiment of the invention, For the rated maximum transmission power (1000MW for stations B / C, 1200MW for station D), the droop factor limiting range, upper limit value. =0.1 lower limit value =0.02 The center-based discretization sampling precision is 0.001, with a default adjustment value. =0. (Combined) Figures 2-6 ,in, Figure 2 This is a schematic diagram of the control structure. Figure 3 This is a schematic diagram of the membership function of DC voltage deviation. Figure 4 This is a schematic diagram of the membership function of the DC voltage deviation change rate. Figure 5 This is a schematic diagram of the membership function for real-time power margin deviation. Figure 6 This is a schematic diagram illustrating the membership function of the droop coefficient variation value. A specific example is shown below: Operating Condition 1: Sudden Increase in Photovoltaic Power (Insufficient Power Margin of Converter Stations B / C) 1. Initial operating condition Time: 12:00, the solar radiation intensity surged at noon, and the power of the photovoltaic power station connected to converter station B / C increased from 400MW to 1200MW; Initial parameters: Real-time transmission power P of converter station B B =1100 MW (10% over rated), real-time maximum allowable transmission power =1150MW, ΔU=0.03pu, =0.01pu / s.

[0078] 2. Adaptive Adjustment Steps In step S1, local electrical quantity acquisition is performed, mainly including: Real-time voltage of DC bus at converter station B =515kV, rated reference value =500kV, calculate ΔU=(515-500) / 500=0.03pu; Within the continuous sampling period (T=2ms), ΔU is 0.03pu and 0.02998pu respectively. Calculate... =0.01 pu / s; calculate =(1150-1100) / 1000=0.05pu.

[0079] In step S2, the reasoning process of the main fuzzy logic controller is as follows: Input variable fuzzification: ΔU=0.03pu corresponds to a PS subset membership degree of 0.8 and a ZO subset membership degree of 0.2; =0.01 pu / s corresponds to a PS subset membership degree of 0.9; =0.05pu corresponds to a membership degree of 0.7 for the PS subset and 0.3 for the ZO subset; Fuzzy rule matching: Activate the core rule if ΔU is PS, It's Photoshop. If it is PS, then the initial adjustment amount is PS, and the activation intensity is calculated using the minimum operation = min(0.8, 0.9, 0.7) = 0.7; Defuzzification: The initial adjustment amount is calculated to be 0.015 using the center method. .

[0080] Step S3 involves weighted correction of the auxiliary controller, specifically including: First auxiliary fuzzy logic controller: =0.05pu (PS range), matching rule if If it's PS, then It's VL, defuzzified to get =0.9; Second auxiliary fuzzy logic controller: =0.03pu (PS interval), matching rule if If it's PS, then It is S, defuzzified to obtain =0.3; Weighted adjustment: ΔK = Initial adjustment amount × ( ×0.6+ (×0.4)≈0.01 .

[0081] S4 performs droop coefficient synthesis and amplitude limiting, specifically including: Initial droop coefficient of converter station B =0.05, real-time droop coefficient =0.05+0.01=0.06, which falls within the range of [0.02,0.1], so no amplitude limiting is needed. The final value is... =0.06.

[0082] S5 is the parameter application step, which will =0.06 is sent to the droop controller of converter station B, which reduces its power allocation coefficient and the actual transmission power drops from 1100MW to 1050MW, avoiding overload protection triggering; at the same time, the DC voltage deviation drops from 0.03pu to 0.02pu, meeting the stability requirements.

[0083] Operating Condition 2: Converter Station A is out of service (emergency system voltage adjustment) 1. Initial operating condition Time: 14:30, Converter station A tripped due to a valve-side reactor fault, the system lost the constant voltage control station, and automatically switched to converter station D to temporarily assume the constant voltage function; Initial parameters: The system DC voltage drops sharply to 470kV (ΔU=-0.06pu, NB interval), dΔU / dt=-0.04pu / s (NB interval), and the real-time transmission power of converter station D is [not specified]. PD =800 MW , Pmax , D =1200 MW , = (1200-800) / 1200≈0.33pu (PM range).

[0084] 2. Key processes of adaptive adjustment Electrical quantity acquisition: ΔU = -0.06 pu, =-0.04pu / s, =0.33pu; Main fuzzy inference: Activation rule if ΔU is NB, It's NB. If it is PM, then the initial adjustment amount is PB. Defuzzifying, we get the initial adjustment amount as 0.03. ; Auxiliary correction: =0.33pu (PM range). =1.0 (VL); =0.06pu (PM range); =0.7 (M); ΔK = Initial adjustment amount × (1.0 × 0.6 + 0.7 × 0.4) ≈ 0.026 ; Coefficient composition: =0.06 + 0.026 = 0.086 (not exceeding the upper limit), final =0.086; Regulation effect: The droop coefficient of converter station D is improved, the voltage support capability is enhanced, the system DC voltage rises from 470kV to 485kV within 100ms (ΔU=-0.03pu), and stabilizes at 490kV after 300ms (ΔU=-0.02pu), avoiding system voltage collapse.

[0085] This invention also provides a multi-terminal flexible DC transmission system, comprising: at least three converter stations, wherein at least one converter station is configured as a constant DC voltage control station, and at least two converter stations are configured as droop control stations; The droop control station includes an adaptive controller, which integrates a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller, and is configured to perform the method described in the above embodiments.

[0086] The adaptive controller specifically includes: The monitoring module is configured to monitor and acquire local electrical quantities in real time; The main fuzzy decision module, whose function corresponds to the main fuzzy logic controller, is configured to input the acquired DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation as input variables to the main fuzzy logic controller; the main fuzzy logic controller performs inference based on the preset fuzzy rule base and outputs the preliminary adjustment amount of the droop coefficient; The auxiliary evaluation and correction module integrates the functions of the first auxiliary fuzzy logic controller and the second auxiliary fuzzy logic controller. It is configured to input the real-time power margin deviation into the first auxiliary fuzzy logic controller to obtain the power margin weighting factor; input the absolute value of the DC voltage deviation into the second auxiliary fuzzy logic controller to obtain the DC voltage weighting factor; and use the DC voltage weighting factor and the power margin weighting factor to perform weighted correction on the preliminary adjustment amount to obtain the droop coefficient adjustment amount. The coefficient synthesis and limiting module is configured to add the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and to perform limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient, ensuring that it is within the preset safe operating range. The parameter output module is configured to send the final real-time droop coefficient as a control parameter to the droop controller of the converter station via the internal communication bus or shared memory of the converter station.

[0087] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system, characterized in that, The system includes a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller. The method includes the following steps: S1. Monitor and acquire local electrical quantities in real time, including system DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation of the converter station. S2. The acquired DC voltage deviation, DC voltage deviation change rate, and real-time power margin deviation are input as input variables to the main fuzzy logic controller; the main fuzzy logic controller performs inference based on the preset fuzzy rule base and outputs the preliminary adjustment amount of the droop coefficient; S3. Input the real-time power margin deviation into the first auxiliary fuzzy logic controller to obtain the power margin weighting factor; input the absolute value of the DC voltage deviation into the second auxiliary fuzzy logic controller to obtain the DC voltage weighting factor; use the DC voltage weighting factor and the power margin weighting factor to perform weighted correction on the preliminary adjustment amount to obtain the droop coefficient adjustment amount; S4. Add the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and perform amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient, ensuring that it is within the preset safe operating range; S5. Apply the final real-time droop coefficient to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics.

2. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 1, characterized in that, The steps in S1 include: S11. Measure the real-time voltage value of the DC bus of the converter station and obtain the rated reference value of the system DC voltage; S12. Calculate the system DC voltage deviation based on the real-time voltage value and the rated reference value; S13. Perform differential calculation on the DC voltage deviation, or calculate the difference of the DC voltage deviation within a continuous sampling period to obtain the DC voltage deviation change rate; S14. Measure the real-time transmission power P of the converter station and obtain the maximum allowable transmission power of the converter station. ; S15. Based on the real-time transmission power P and the maximum allowable transmission power Calculate the real-time power margin deviation of the converter station. The calculation formula is: Among them, the real-time power margin deviation reflects the relative power margin based on the rated capacity.

3. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 2, characterized in that, In S2, the main fuzzy logic controller performs inference based on a preset fuzzy rule base, specifically including the following steps: S21. Input variables, including DC voltage deviation. DC voltage deviation change rate and real-time power margin deviation Based on their respective predefined membership functions, they are converted into corresponding fuzzy quantities; S22. Input the obtained fuzzy quantity into the preset fuzzy rule base; synthesize the initial adjustment amount of the output droop coefficient through fuzzy logic operation. The fuzzy quantity; S23. Convert the fuzzy amount of the obtained preliminary adjustment into an output value, i.e., the preliminary adjustment amount of the droop coefficient, through a defuzzification method. .

4. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 3, characterized in that, In S21, converting the input variable into the corresponding fuzzy quantity specifically includes the following operations: For each precise value of the input variable, perform the following: S21a. Map the precise value onto the corresponding normalized universe of discourse; S21b. For all fuzzy subsets defined by the variable, calculate the membership degree of the precise value belonging to each fuzzy subset; the membership degree is obtained by querying the respective predefined membership degree function, and the value is in the interval [0,1]. S21c. All the obtained fuzzy subsets and membership pairs together constitute the fuzzy quantity corresponding to the input variable.

5. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 4, characterized in that, In S22, the initial adjustment amount of the output droop coefficient is synthesized through fuzzy logic operations. The fuzzy quantity specifically includes the following steps: S22a. Traverse each rule in the preset fuzzy rule base and match the input fuzzy quantity with the preconditions of the rule; for each rule, use the minimum operation or product operation to calculate the activation strength of the rule. S22b. For each activated rule, the membership function of the output fuzzy subset specified in the rule conclusion is subjected to a minimum or scaling operation using the activation intensity to obtain the modified output fuzzy set. S22c: The output fuzzy sets obtained after modifying all activated rules through S22b are synthesized using the maximum value operation, and aggregated into a single total output fuzzy set; this set is the initial adjustment amount of the droop coefficient. The amount of ambiguity.

6. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 5, characterized in that, In S23, the fuzzy values ​​of the initial adjustment are converted into output values ​​through a defuzzification method. Specifically, the central method is used, and the steps include: S23a, Output variables The universe of discourse is discretized with a preset precision to obtain a series of sampling points. ; S23b, For each sampling point Calculate the membership value in the total output fuzzy set synthesized in step S22. And calculate the weighted sum of all sampling points and the sum of membership degrees; S23c. Divide the weighted sum by the sum of membership degrees to obtain the final precise output value, which is the initial adjustment amount of the droop coefficient. The calculation formula is: In the formula, N is the total number of sampling points.

7. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 6, characterized in that, In S3, the power margin weighting factor α is obtained from the first auxiliary fuzzy logic controller, specifically including: S31a. The input precise quantity, i.e. the real-time power margin deviation, is converted into the corresponding fuzzy quantity according to the membership function predefined for the first auxiliary fuzzy logic controller. S32a. The obtained fuzzy quantity is input into the fuzzy rule base of the first auxiliary fuzzy logic controller, and the output variable, namely the power margin weighting factor, is synthesized through fuzzy logic operation. The fuzzy quantity; S33a, the obtained power margin weighting factor The fuzzy quantity is converted into an accurate power margin weighting factor through a defuzzification method. The value of .

8. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 7, characterized in that, In S3, the DC voltage weighting factor is obtained by the second auxiliary fuzzy logic controller. Specifically, it includes: S31b: Convert the input precise quantity, i.e., the absolute value of the DC voltage deviation, into the corresponding fuzzy quantity according to the membership function predefined for the second auxiliary fuzzy logic controller; S32b: Input the obtained fuzzy quantity into the fuzzy rule base of the second auxiliary fuzzy logic controller, and synthesize the output variable, namely the DC voltage weighting factor, through fuzzy logic operation. The fuzzy quantity; S33b, DC voltage weighting factor The fuzzy quantities are converted into DC voltage weighting factors through a defuzzification method. The value of .

9. The adaptive adjustment method for droop coefficient in a multi-terminal flexible DC transmission system according to claim 8, characterized in that, In S4, the real-time droop coefficient Amplitude limiting is performed to obtain the final real-time droop coefficient. The steps include: Real-time sagging coefficient The coefficient is compared with the preset lower and upper limits of the droop coefficient, and the final real-time droop coefficient is output according to the following formula. : in, This is the lower limit of the droop coefficient. This is the upper limit of the droop coefficient.

10. A multi-terminal flexible DC transmission system, characterized in that, include: At least three converter stations, of which at least one converter station is configured as a constant DC voltage control station and at least two converter stations are configured as droop control stations; The droop control station includes an adaptive controller, which integrates a main fuzzy logic controller, a first auxiliary fuzzy logic controller, and a second auxiliary fuzzy logic controller, and is configured to perform the method as described in any one of claims 1 to 9.

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