Virtual inertia fuzzy self-adaptive new energy inverter frequency active support method

Abstract

The invention belongs to the technical field of power systems and automation thereof, and relates to a virtual inertia fuzzy self-adaptive new energy inverter frequency active support method. Aiming at the problem of power grid frequency stability caused by new energy power generation intermittency and volatility, the method comprises the following steps: simulating inertia characteristics of a synchronous generator through a direct current side capacitance control strategy; detecting the frequency of the power grid by using a power synchronous ring, and converting a power difference value into a frequency deviation; designing a fuzzy control module, and adjusting the coefficient of the virtual inertia controller in real time according to the frequency deviation to optimize the frequency response; an inverter current given value is adjusted according to the output of the fuzzy control module, and active and reactive power output is calculated and controlled; relevant parameters are monitored in real time, the performance of the self-adaptive control module is evaluated, and parameter optimization response of the fuzzy controller is adjusted. The frequency stability and reliability of the power grid can be effectively improved, the control flexibility and precision of the inverter are improved, and new energy efficient grid-connected power generation is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and automation thereof, and in particular relates to a virtual inertia fuzzy adaptive new energy inverter frequency active support method. Background Art

[0002] In modern power systems, with the rapid development of renewable energy power generation technology, the penetration rate of renewable energy inverters in power grids continues to increase. However, renewable energy power generation is intermittent and volatile, which brings many challenges to the stability of power grid frequency. In traditional power systems, synchronous generators play a key role in maintaining the stability of power grid frequency by virtue of their own inertia and frequency regulation capabilities. However, most renewable energy inverters convert electric energy through power electronic devices, and their own inertia is small, making it difficult for them to provide effective frequency support for the power grid like synchronous generators. When there is a sudden load change in the power grid or a fluctuation in renewable energy power generation, it is easy to cause the grid frequency deviation to exceed the allowable range, affecting the safe and stable operation of the power system.

[0003] At present, some existing frequency control methods for new energy inverters have certain limitations. Although some methods try to simulate the inertia characteristics of synchronous generators, they lack the accuracy and flexibility of inertia simulation. For example, some fixed-parameter inertia simulation strategies cannot be dynamically adjusted according to the real-time changes in grid frequency, and are difficult to adapt to complex and changeable grid conditions. Moreover, during the power regulation process, there is a lack of precise control over the inverter output power, and it is impossible to respond to small changes in grid frequency in a timely and effective manner, resulting in insufficient timeliness and accuracy in frequency regulation.

[0004] In addition, existing control methods often lack an adaptive adjustment mechanism and cannot automatically optimize control parameters when grid operating conditions change, making the control performance of the system unstable under different operating conditions. For example, when the grid frequency characteristics or load characteristics change, fixed control parameters may not guarantee that the system can still maintain a good frequency response, thus affecting the stability of the grid frequency.

[0005] In view of the defects of the prior art, the present invention proposes a new energy inverter frequency active support method based on virtual inertia fuzzy adaptation. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to propose a virtual inertia fuzzy adaptive new energy inverter frequency active support method based on virtual inertia fuzzy adaptive new energy inverter frequency active support method, which is used to solve the grid frequency stability problem caused by the intermittent and volatility of new energy power generation after the new energy inverter is connected to the grid.

[0007] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0008] The virtual inertia fuzzy adaptive new energy inverter frequency active support method includes the following steps:

[0009] S1. Virtual inertia simulation, using the DC side capacitor control strategy, simulates the inertia characteristics of the synchronous generator by real-time adjusting the charging and discharging process of the inverter DC side capacitor;

[0010] S2. Frequency deviation detection: Based on the virtual inertia simulation results of S1, the power synchronization ring is used to detect the grid frequency in real time, and the power difference is obtained by comparing the power difference with the grid frequency given value. The power difference is analyzed and the power imbalance reflected by the power difference is converted into frequency deviation, providing accurate frequency change data for fuzzy adaptive control.

[0011] S3. Fuzzy adaptive control, design fuzzy control module, the input of fuzzy control module is the frequency deviation obtained in S2, and the output is proportional term coefficient and differential term coefficient; define fuzzy sets of input and output variables and formulate fuzzy rule tables; obtain real-time frequency deviation, adjust virtual inertia controller coefficient in real time according to real-time frequency deviation, and realize dynamic optimization of inverter frequency response through fuzzy reasoning and clarification;

[0012] S4. Inverter output power adjustment, adjusting the inverter current given value according to the output of the fuzzy control module, adjusting the inverter current given value, calculating the active power and reactive power according to the adjusted current given value, and achieving power output adjustment by changing the current given value to support the grid frequency;

[0013] S5. Response and optimization of the adaptive control module. Real-time monitoring of the frequency of the fuzzy controller and the inverter output power. Evaluation of the performance of the adaptive control module. Based on the results of the performance evaluation, adjustment of the parameters of the fuzzy controller is performed to optimize the response of the fuzzy controller.

[0014] Furthermore, in S1, the method of simulating the inertia characteristics of the synchronous generator using the DC link capacitor control strategy includes:

[0015] S11. Determine the capacitor control target, deeply analyze the impact of the grid frequency change law on the stability of the power system, set the normal range of the grid frequency, when the grid frequency is lower than the normal range, it indicates that the grid is facing a power shortage and the inverter needs to provide additional active power to support the frequency. At this time, the capacitor discharge rate should be increased; when the grid frequency is higher than the normal range, the grid may have excess power, and the inverter needs to reduce the active power output, that is, increase the capacitor charging rate; when the frequency is within the normal range, ensure the balance of capacitor charging and discharging to maintain the stability of the inverter output power and avoid unnecessary interference to the grid;

[0016] S12. Grid frequency monitoring and judgment: select a frequency sensor with high accuracy and fast response, install it at the connection point between the grid and the inverter, obtain the real-time grid frequency, and set the frequency change threshold;

[0017] S13. Adjust the capacitor charge and discharge rate according to the frequency change:

[0018] For new energy inverters, the DC side capacitor is a key component; the energy storage formula of the capacitor is: In the formula, C is the capacitance value, Vdc is the DC voltage; at the same time, the relationship between power and energy is: Derivative of the capacitor energy storage formula yields It can be seen that by controlling the change rate of the DC side voltage The active power output of the inverter can be adjusted;

[0019] Based on the above theoretical basis, the capacitance control simulation method includes:

[0020] When the grid frequency decreases, the discharge rate of the capacitor is increased. According to the energy storage formula of the capacitor The relationship between power and energy By increasing the rate of change of the DC voltage To increase active power output;

[0021] in, k 1 is the proportional coefficient, Δf is the frequency deviation, Δf=fmin-f, fmin is the set lower limit of the grid frequency, and f is the actual grid frequency; this process is similar to the inertia characteristic of a synchronous generator releasing kinetic energy to increase active power output when the frequency drops, thereby providing virtual inertia for the grid and enhancing the system's resistance to frequency drops;

[0022] When the grid frequency rises, the charging rate of the capacitor increases; that is, the To reduce the output of active power;

[0023] During the increase of grid frequency k 2 is the proportionality coefficient, Δf=f-fmax, fmax is the set upper limit of the grid frequency;

[0024] The grid frequency is within the normal range, i.e. Maintaining the inverter output power stability and avoiding unnecessary interference to the power grid is in line with the characteristics of synchronous generators maintaining stable output during normal operation.

[0025] Further, the S2 includes:

[0026] S21. Grid frequency detection and data acquisition: Based on the simulation results of step S1, the grid frequency is measured at the connection point between the grid and the inverter. The power synchronization ring has the characteristics of high precision and fast response, and can track the slight changes of the grid frequency in real time to ensure that the acquired frequency data is accurate and reliable. At the same time, the actual output power of the inverter is measured by the power measurement device, and the preset power reference value is obtained;

[0027] S22. Calculate and analyze the power difference. Calculate the power difference, which reflects the degree of deviation between the actual output power of the inverter and the expected output power. Analyze the power difference to determine its positive and negative sign and size to determine the direction and degree of power imbalance.

[0028] Among them, the power difference ΔP, ΔP = Pact-Pref;

[0029] In the formula, Pact is the measured actual output power of the inverter, and Pref is the preset power reference value; when ΔP>0, it means that the actual output power is greater than the reference power, resulting in an increase in the grid frequency; when ΔP<0, the frequency decreases;

[0030] S23. Power difference and frequency deviation conversion, converting the power imbalance reflected by the power difference into frequency deviation; the conversion process involves the power-frequency characteristic relationship, which is achieved through a pre-established empirical formula, which is expressed as:

[0031] Δferr=kp 2 ΔP+f-fref;

[0032] f is the actual grid frequency (the grid frequency measured at the connection point between the grid and the inverter); fref is the grid frequency given value; Δferr is the frequency deviation; kp 2 is the proportionality factor determined according to the characteristics of the power grid;

[0033] The above formula comprehensively considers the influence of power difference on frequency deviation and the direct deviation between actual frequency and given frequency, making the calculation of frequency deviation more accurate and comprehensive;

[0034] When the power difference is large, ΔP, the influence of the power difference ΔP on the frequency deviation will also increase accordingly. 2 The ΔP term is reflected in the frequency deviation calculation; at the same time, the f-fref term directly reflects the deviation of the actual frequency from the given frequency.

[0035] Furthermore, the S3 specifically includes:

[0036] S31. Construction of fuzzy control module architecture. The fuzzy control module includes key components such as fuzzification interface, fuzzy rule base, fuzzy inference engine and clarification interface. The fuzzification interface converts the precise input signal (frequency deviation) into fuzzy quantity so that it can match the rules in the fuzzy rule base. The fuzzy rule base stores a series of predefined fuzzy rules that describe the fuzzy relationship between input and output. The fuzzy inference engine performs inference calculation based on the input fuzzy quantity and fuzzy rules to obtain fuzzy output. The clarification interface converts the fuzzy output into precise control signals (proportional term coefficient and differential term coefficient) for actual control operations.

[0037] S32. Definition of fuzzy sets and membership functions. For the input variable Δferr, its fuzzy set is defined as: {negative large (NB), negative medium (NM), zero (ZO), positive medium (PM), positive large (PB)};

[0038] According to the range of frequency deviation in actual operation of the power grid, the interval of each fuzzy set is determined:

[0039] When Δferr<-a, the membership is NB; when -a≤Δferr<-b, the membership is NM, and so on; at the same time, the corresponding fuzzy sets are defined for the output variables (proportional term coefficient Kp and differential term coefficient Kd): {Very small (VS), small (S), medium (M), large (L), very large (VL)};

[0040] S33. According to the system's requirements for frequency regulation and rich control experience, a fuzzy rule table is formulated. For example, when Δferr is NB, in order to quickly increase the frequency, Kp should be increased to VL, and Kd should also be increased to VL, so that the controller can respond quickly when the frequency drops sharply, thereby enhancing the stability of the system;

[0041] S34. Real-time adjustment. After the frequency deviation is obtained in real time, it is subjected to fuzzification, fuzzy reasoning and clarity processing in turn; it is converted into a fuzzy quantity through fuzzification processing, and then fuzzy reasoning is performed according to the fuzzy rule table to obtain the fuzzy output of the proportional term coefficient Kp and the differential term coefficient Kd, and finally the fuzzy output is converted into the precise proportional term coefficient K′p and differential term coefficient K′d values ​​through clarity processing; the precise value will adjust the coefficient of the virtual inertia controller in real time, so that the inverter can dynamically optimize its frequency response according to the change of the grid frequency, and effectively improve the system's adaptability and stability to frequency fluctuations; when the grid frequency drops, by adjusting the precise proportional term coefficient K′p and differential term coefficient K′d, the active power output of the inverter can be increased to slow down the frequency decline trend; when the frequency rises, corresponding adjustments are made to suppress the frequency increase to ensure that the grid frequency always fluctuates within a reasonable range.

[0042] Further, the S4 includes:

[0043] S41. Calculate the given value of the d-axis current, based on the proportional term coefficient K′p and the differential term coefficient K′d output by the fuzzy control module, and combine the voltage equation of the inverter in the dq coordinate system to calculate the d-axis;

[0044] The calculation formula of the d-axis current given value idref is:

[0045]

[0046] Among them, id 0 is the initial d-axis current given value, id 0 It is a reference value pre-set according to the inverter's rated power, grid voltage and other system parameters; K' P The (Δferr) part indicates that idref is adjusted according to the size of the current frequency deviation Δferr. When the frequency deviation is large, K′ P It will be adjusted to a suitable value according to the fuzzy rule table (such as increasing to VL when the frequency deviation is negative NB), so that idref will increase or decrease accordingly to change the active power output. The first part considers how fast the frequency deviation changes. If the frequency deviation changes rapidly (i.e. If the frequency deviation is large, K′d will also be adjusted according to the fuzzy rules (for example, when the frequency deviation change rate is large and negative, K′d increases), and idref will be further adjusted to enable the inverter to respond to frequency changes faster;

[0047] S42. Calculation of the q-axis current given value. The calculation of the q-axis current given value is relatively simple. When no special reactive power adjustment is performed, the q-axis current given value iqref is equal to the initial axis current given value iq 0 , iq 0 It is the initial setting value based on the reactive power demand of the system. When the reactive power of the grid is balanced, iq 0 Set to a value that makes the inverter reactive output zero or close to zero;

[0048] S43. Calculate the active power and reactive power of the inverter based on the accurately calculated d-axis and q-axis current given values:

[0049] Active power P, P = 1.5udidref, ud is the d-axis voltage, which is related to the grid voltage and inverter control mode;

[0050] Reactive power Q, Q = 1.5uqiqref, uq is the q-axis voltage;

[0051] By changing idref and iqref, independent control of active power P and reactive power Q can be achieved; when the grid frequency decreases, idref will increase, causing P to increase, and the inverter will inject more active power into the grid to slow down the frequency decline trend; if the grid reactive power demand changes, such as insufficient reactive power (increased inductive reactive power demand), iqref can be increased to increase Q, meet the grid reactive power demand, maintain grid voltage stability, and indirectly support grid frequency stability; throughout the process, the inverter continuously and dynamically adjusts the current set value according to the real-time changes in grid frequency and reactive power demand, thereby accurately controlling power output and effectively supporting stable grid frequency operation.

[0052] Further, the S5 includes:

[0053] S51 real-time monitoring of the frequency of the fuzzy control module and the active power P and reactive power Q output by the inverter;

[0054] S52. Calculation of performance evaluation index: Design a performance index function J to evaluate the performance of the adaptive control module. J is expressed as:

[0055]

[0056] f is the actual grid frequency; fref is the grid frequency given value; Pref and Qref are the reference values ​​of active power P and reactive power Q, respectively, which are determined according to grid operation requirements, inverter rated capacity, load characteristics and other factors; λ 1 and λ 2 is the weight coefficient, which is used to weigh the importance of frequency deviation, active power deviation and reactive power deviation in the performance index;

[0057] By calculating the value of the performance index J, the performance of the control module can be quantitatively evaluated; if the J value is small, it means that the system performs well in frequency control, active power P and reactive power Q regulation, and the control effect is ideal; on the contrary, if the value J is large, it means that the system has performance problems and needs further optimization;

[0058] S53. Parameter adjustment and optimization strategy implementation, adjust the parameters of the fuzzy controller according to the performance evaluation results; if the J value is too large, analyze the frequency deviation (f-fref) 2 , Active power deviation (P-Pref) 2 and reactive power deviation (Q-Qref) 2 Contribution to performance index J;

[0059] If the contribution of frequency deviation is large, it means that the frequency control effect is not good. At this time, the reduction range of proportional term coefficient and differential term coefficient when the frequency deviation is positive PB should be appropriately increased;

[0060] Similar analysis and adjustment methods are also used for situations where active power or reactive power deviations contribute greatly. After adjusting the parameters, the frequency and power data are continuously monitored in real time, the performance indicators are recalculated, a closed-loop feedback control is formed, and the response of the fuzzy controller is continuously optimized to gradually improve the system performance and ultimately achieve a satisfactory control effect, ensuring that the inverter can effectively support the grid frequency and maintain power balance under various operating conditions.

[0061] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0062] By simulating the inertia characteristics of synchronous generators and dynamically adjusting the inverter output, it can effectively cope with the intermittent and volatility of renewable energy power generation, quickly compensate for power shortages or reduce excess power, and accurately control grid frequency fluctuations within a very small range, thereby enhancing the overall frequency stability and reliability of the grid and providing a solid guarantee for the safe and stable operation of various power equipment.

[0063] For inverter control, the fuzzy adaptive control mechanism greatly improves its control flexibility and accuracy. No matter what frequency change conditions it faces, the inverter can quickly and accurately adjust the output power according to the real-time frequency deviation. It can not only provide strong support when the frequency fluctuates greatly, but also respond sensitively to small frequency deviations, making the inverter an active guardian of grid frequency stability, effectively improving its adaptability and control efficiency in complex grid environments.

[0064] In terms of power output control, high-precision independent control of active power and reactive power is achieved. By accurately calculating and adjusting the current set value, the inverter output power is ensured to accurately match the grid demand, maintain power balance, and significantly improve the power quality. This can effectively prevent equipment from being damaged by power fluctuations in scenarios such as industrial production and commercial operations that have strict requirements on power quality, improve production efficiency, and reduce operating costs.

[0065] This technology has effectively promoted the efficient grid-connected power generation of new energy. It has solved the key bottleneck problem of new energy access to the power grid, improved the compatibility and reliability of new energy power generation systems in the power grid, promoted the large-scale application of new energy in the power system, accelerated the transformation of the energy structure to a green and sustainable direction, helped the power industry achieve energy conservation and emission reduction goals, and laid a solid foundation for building a clean, low-carbon, safe and efficient energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The logical block diagram of the frequency active support method for new energy inverters based on virtual inertia fuzzy self-adaptation;

[0067] Figure 2 The logic block diagram of the method for simulating the inertia characteristics of a synchronous generator using a DC-side capacitor control strategy;

[0068] Figure 3 It is a logic block diagram of the frequency deviation detection method;

[0069] Figure 4 It is the logic block diagram of the fuzzy adaptive control method;

[0070] Figure 5 A logic block diagram of an inverter output power adjustment method;

[0071] Figure 6 The logical block diagram of the response and optimization method of the adaptive control module. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0073] like Figure 1 As shown, the virtual inertia fuzzy adaptive new energy inverter frequency active support method includes the following steps:

[0074] S1. Virtual inertia simulation, using the DC side capacitor control strategy, simulates the inertia characteristics of the synchronous generator by real-time adjusting the charging and discharging process of the inverter DC side capacitor;

[0075] S2. Frequency deviation detection: Based on the virtual inertia simulation results of S1, the power synchronization ring is used to detect the grid frequency in real time, and the power difference is obtained by comparing the power difference with the grid frequency given value. The power difference is analyzed and the power imbalance reflected by the power difference is converted into frequency deviation, providing accurate frequency change data for fuzzy adaptive control.

[0076] S3. Fuzzy adaptive control, design fuzzy control module, the input of fuzzy control module is the frequency deviation obtained in S2, and the output is proportional term coefficient and differential term coefficient; define fuzzy sets of input and output variables and formulate fuzzy rule tables; obtain real-time frequency deviation, adjust virtual inertia controller coefficient in real time according to real-time frequency deviation, and realize dynamic optimization of inverter frequency response through fuzzy reasoning and clarification;

[0077] S4. Inverter output power adjustment, adjusting the inverter current given value according to the output of the fuzzy control module, adjusting the inverter current given value, calculating the active power and reactive power according to the adjusted current given value, and achieving power output adjustment by changing the current given value to support the grid frequency;

[0078] S5. Response and optimization of the adaptive control module. Real-time monitoring of the frequency of the fuzzy controller and the inverter output power. Evaluation of the performance of the adaptive control module. Based on the results of the performance evaluation, adjustment of the parameters of the fuzzy controller is performed to optimize the response of the fuzzy controller.

[0079] like Figure 2 As shown, in S1, the method of simulating the inertia characteristics of the synchronous generator by using the DC link capacitor control strategy includes:

[0080] S11. Determine the capacitor control target, deeply analyze the impact of the grid frequency change law on the stability of the power system, set the normal range of the grid frequency, when the grid frequency is lower than the normal range, it indicates that the grid is facing a power shortage and the inverter needs to provide additional active power to support the frequency. At this time, the capacitor discharge rate should be increased; when the grid frequency is higher than the normal range, the grid may have excess power, and the inverter needs to reduce the active power output, that is, increase the capacitor charging rate; when the frequency is within the normal range, ensure the balance of capacitor charging and discharging to maintain the stability of the inverter output power and avoid unnecessary interference to the grid;

[0081] S12. Grid frequency monitoring and judgment: select a frequency sensor with high accuracy and fast response, install it at the connection point between the grid and the inverter, obtain the real-time grid frequency, and set the frequency change threshold;

[0082] S13. Adjust the capacitor charge and discharge rate according to the frequency change:

[0083] For new energy inverters, the DC side capacitor is a key component; the energy storage formula of the capacitor is: In the formula, C is the capacitance value, Vdc is the DC voltage; at the same time, the relationship between power and energy is: Derivative of the capacitor energy storage formula yields It can be seen that by controlling the change rate of the DC side voltage The active power output of the inverter can be adjusted, which is the theoretical basis for using the DC side capacitor control strategy to simulate the inertia characteristics of the synchronous generator;

[0084] Based on the above theoretical basis, the capacitance control simulation method includes:

[0085] When the grid frequency decreases, the discharge rate of the capacitor increases. According to the energy storage formula of the capacitor The relationship between power and energy By increasing the rate of change of the DC voltage To increase active power output;

[0086] in, k 1is the proportional coefficient, Δf is the frequency deviation, Δf=fmin-f, fmin is the set lower limit of the grid frequency, and f is the actual grid frequency; this process is similar to the inertia characteristic of a synchronous generator releasing kinetic energy to increase active power output when the frequency drops, thereby providing virtual inertia for the grid and enhancing the system's resistance to frequency drops;

[0087] When the grid frequency rises, the charging rate of the capacitor increases; that is, the To reduce the output of active power;

[0088] During the increase of grid frequency k 2 is the proportionality coefficient, Δf=f-fmax, fmax is the set upper limit of the grid frequency; this process is similar to the synchronous generator absorbing electrical energy and storing it as kinetic energy when the frequency rises, thereby reducing the active power output, simulating the inertia characteristics of the synchronous generator, and helping to suppress the increase in grid frequency;

[0089] The grid frequency is within the normal range, i.e. Maintaining the inverter output power stability and avoiding unnecessary interference to the power grid is in line with the characteristics of synchronous generators maintaining stable output during normal operation.

[0090] like Figure 3 As shown, the S2 includes:

[0091] S21. Grid frequency detection and data acquisition: Based on the simulation results of step S1, the grid frequency is measured at the connection point between the grid and the inverter. The power synchronization ring has the characteristics of high precision and fast response, and can track the slight changes of the grid frequency in real time to ensure that the acquired frequency data is accurate and reliable. At the same time, the actual output power of the inverter is measured by the power measurement device, and the preset power reference value is obtained;

[0092] S22. Calculate and analyze the power difference. Calculate the power difference. The power difference reflects the degree of deviation between the actual output power of the inverter and the expected output power. It is an important intermediate quantity for the subsequent frequency deviation calculation. Analyze the power difference to determine its positive and negative sign and size to determine the direction and degree of power imbalance.

[0093] Among them, the power difference ΔP, ΔP = Pact-Pref;

[0094] In the formula, Pact is the measured actual output power of the inverter, and Pref is the preset power reference value; when ΔP>0, it means that the actual output power is greater than the reference power, resulting in an increase in the grid frequency; when ΔP<0, the frequency decreases;

[0095] S23. Power difference and frequency deviation conversion, converting the power imbalance reflected by the power difference into frequency deviation; the conversion process involves the power-frequency characteristic relationship, which is achieved through a pre-established empirical formula, which is expressed as:

[0096] Δferr=kp 2 ΔP+f-fref;

[0097] f is the actual grid frequency (the grid frequency measured at the connection point between the grid and the inverter); fref is the grid frequency given value; Δferr is the frequency deviation; kp 2 is the proportionality factor determined according to the characteristics of the power grid;

[0098] The above formula comprehensively considers the influence of the power difference on the frequency deviation and the direct deviation between the actual frequency and the given frequency, making the calculation of the frequency deviation more accurate and comprehensive.

[0099] When the power difference is large, ΔP, the influence of the power difference ΔP on the frequency deviation will also increase accordingly. 2 The ΔP term is reflected in the frequency deviation calculation; at the same time, the f-fref term directly reflects the deviation of the actual frequency from the given frequency.

[0100] like Figure 4 As shown, the S3 specifically includes:

[0101] S31. Construction of fuzzy control module architecture. The fuzzy control module includes key components such as fuzzification interface, fuzzy rule base, fuzzy inference engine and clarification interface. The fuzzification interface converts the precise input signal (frequency deviation) into fuzzy quantity so that it can match the rules in the fuzzy rule base. The fuzzy rule base stores a series of predefined fuzzy rules that describe the fuzzy relationship between input and output. The fuzzy inference engine performs inference calculation based on the input fuzzy quantity and fuzzy rules to obtain fuzzy output. The clarification interface converts the fuzzy output into precise control signals (proportional term coefficient and differential term coefficient) for actual control operations.

[0102] S32. Definition of fuzzy sets and membership functions. For the input variable Δferr, its fuzzy set is defined as: {negative large (NB), negative medium (NM), zero (ZO), positive medium (PM), positive large (PB)};

[0103] According to the range of frequency deviation in actual operation of the power grid, the interval of each fuzzy set is determined:

[0104] When Δferr<-a, the membership is NB; when -a≤Δferr<-b, the membership is NM, and so on; at the same time, the corresponding fuzzy sets are defined for the output variables (proportional term coefficient Kp and differential term coefficient Kd): {Very small (VS), small (S), medium (M), large (L), very large (VL)};

[0105] S33. According to the system's requirements for frequency regulation and rich control experience, a fuzzy rule table is formulated. For example, when Δferr is NB, in order to quickly increase the frequency, Kp should be increased to VL, and Kd should also be increased to VL, so that the controller can respond quickly when the frequency drops sharply, thereby enhancing the stability of the system. The complete table of fuzzy rules is shown in Table 1:

[0106]

[0107]

[0108] S34. Real-time adjustment. After the frequency deviation is obtained in real time, it is subjected to fuzzification, fuzzy reasoning and clarity processing in turn; it is converted into a fuzzy quantity through fuzzification processing, and then fuzzy reasoning is performed according to the fuzzy rule table to obtain the fuzzy output of the proportional term coefficient Kp and the differential term coefficient Kd, and finally the fuzzy output is converted into the precise proportional term coefficient K′p and differential term coefficient K′d values ​​through clarity processing; the precise value will adjust the coefficient of the virtual inertia controller in real time, so that the inverter can dynamically optimize its frequency response according to the change of the grid frequency, and effectively improve the system's adaptability and stability to frequency fluctuations; when the grid frequency drops, by adjusting the proportional term coefficient K′p and the differential term coefficient K′d, the active power output of the inverter can be increased to slow down the frequency decline trend; when the frequency rises, corresponding adjustments are made to suppress the frequency increase to ensure that the grid frequency always fluctuates within a reasonable range.

[0109] like Figure 5 As shown, the S4 includes:

[0110] S41. Calculate the given value of the d-axis current, based on the proportional term coefficient K′p and the differential term coefficient K′d output by the fuzzy control module, and combine the voltage equation of the inverter in the dq coordinate system to calculate the d-axis;

[0111] The calculation formula of the d-axis current given value idref is:

[0112]

[0113] Among them, id 0 is the initial d-axis current given value, id 0 It is a reference value pre-set according to the inverter's rated power, grid voltage and other system parameters; K' PThe (Δferr) part indicates that idref is adjusted according to the size of the current frequency deviation Δferr. When the frequency deviation is large, K′ P It will be adjusted to a suitable value according to the fuzzy rule table (such as increasing to VL when the frequency deviation is negative NB), so that idref will increase or decrease accordingly to change the active power output. The first part considers how fast the frequency deviation changes. If the frequency deviation changes rapidly (i.e. If the frequency deviation is large, K′d will also be adjusted according to the fuzzy rules (for example, when the frequency deviation change rate is large and negative, K′d increases), and idref will be further adjusted to enable the inverter to respond to frequency changes faster;

[0114] S42. Calculation of the q-axis current given value. The calculation of the q-axis current given value is relatively simple. When no special reactive power adjustment is performed, the q-axis current given value iqref is equal to the initial axis current given value iq 0 , iq 0 It is the initial setting value based on the reactive power demand of the system. When the reactive power of the grid is balanced, iq 0 Set to a value that makes the inverter reactive output zero or close to zero;

[0115] S43. Calculate the active power and reactive power of the inverter based on the accurately calculated d-axis and q-axis current given values:

[0116] Active power P, P = 1.5udidref, ud is the d-axis voltage, which is related to the grid voltage and inverter control mode;

[0117] Reactive power Q, Q = 1.5uqiqref, uq is the q-axis voltage;

[0118] By changing idref and iqref, independent control of active power P and reactive power Q can be achieved; when the grid frequency decreases, idref will increase, causing P to increase, and the inverter will inject more active power into the grid to slow down the frequency decline trend; if the grid reactive power demand changes, such as insufficient reactive power (increased inductive reactive power demand), iqref can be increased to increase Q, meet the grid reactive power demand, maintain grid voltage stability, and indirectly support grid frequency stability; throughout the process, the inverter continuously and dynamically adjusts the current set value according to the real-time changes in grid frequency and reactive power demand, thereby accurately controlling power output and effectively supporting stable grid frequency operation.

[0119] like Figure 6 As shown, the S5 includes:

[0120] S51 real-time monitoring of the frequency of the fuzzy control module and the active power P and reactive power Q output by the inverter;

[0121] S52. Calculation of performance evaluation index: Design a performance index function J to evaluate the performance of the adaptive control module. J is expressed as:

[0122]

[0123] f is the actual grid frequency; fref is the grid frequency given value; Pref and Qref are the reference values ​​of active power P and reactive power Q, respectively, which are determined according to grid operation requirements, inverter rated capacity, load characteristics and other factors; λ 1 and λ 2 is the weight coefficient, which is used to weigh the importance of frequency deviation, active power deviation and reactive power deviation in the performance index;

[0124] By calculating the value of the performance index J, the performance of the control module can be quantitatively evaluated; if the J value is small, it means that the system performs well in frequency control, active power P and reactive power Q regulation, and the control effect is ideal; on the contrary, if the value J is large, it means that the system has performance problems and needs further optimization;

[0125] S53. Parameter adjustment and optimization strategy implementation, adjust the parameters of the fuzzy controller according to the performance evaluation results; if the J value is too large, analyze the frequency deviation (f-fref) 2 , Active power deviation (P-Pref) 2 and reactive power deviation (Q-Qref) 2 Contribution to performance index J;

[0126] If the frequency deviation contribution is large, it means that the frequency control effect is not good. For example, when the frequency deviation is large and positive for a long time (that is, the frequency is higher than the reference value), it may be that the adjustment of the proportional term coefficient and the differential term coefficient in the fuzzy rule table when the frequency rises is not effective enough. At this time, the reduction range of the proportional term coefficient and the differential term coefficient when the frequency deviation is positive PB can be appropriately increased;

[0127] For situations where the active power or reactive power deviation contributes greatly, similar analysis and adjustment methods are also adopted; if the active power is lower than the reference value for a long time, check whether the correlation coefficient in the conversion process between the power difference and the frequency deviation (such as the empirical formula in) is appropriate, or adjust the rules related to active power regulation in the fuzzy rule table; after adjusting the parameters, continue to monitor the frequency and power data in real time, recalculate the performance indicators, form a closed-loop feedback control, and continuously optimize the response of the fuzzy controller to gradually improve the system performance and ultimately achieve a satisfactory control effect, ensuring that the inverter can effectively support the grid frequency and maintain power balance under various working conditions.

[0128] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. Virtual inertia fuzzy adaptive new energy inverter frequency active support method, characterized in that: The following steps are involved: S1. Virtual inertia simulation, using the DC side capacitor control strategy, simulates the inertia characteristics of the synchronous generator by real-time adjusting the charging and discharging process of the inverter DC side capacitor; S2. Frequency deviation detection: Based on the virtual inertia simulation results of S1, the power synchronization ring is used to detect the grid frequency in real time, and the power difference is obtained by comparing the power difference with the grid frequency given value. The power difference is analyzed and the power imbalance reflected by the power difference is converted into frequency deviation, providing accurate frequency change data for fuzzy adaptive control. S3. Fuzzy adaptive control, design fuzzy control module, the input of fuzzy control module is the frequency deviation obtained in S2, and the output is proportional term coefficient and differential term coefficient; define fuzzy sets of input and output variables and formulate fuzzy rule table; Obtain the real-time frequency deviation, adjust the virtual inertia controller coefficient in real time according to the real-time frequency deviation, and dynamically optimize the inverter frequency response through fuzzy reasoning and clarification; S4. Inverter output power adjustment, adjusting the inverter current given value according to the output of the fuzzy control module, adjusting the inverter current given value, calculating the active power and reactive power according to the adjusted current given value, and achieving power output adjustment by changing the current given value to support the grid frequency; S5. Response and optimization of the adaptive control module. Real-time monitoring of the frequency of the fuzzy controller and the inverter output power. Evaluation of the performance of the adaptive control module. Based on the results of the performance evaluation, adjustment of the parameters of the fuzzy controller is performed to optimize the response of the fuzzy controller.

2. The virtual inertia fuzzy adaptive new energy inverter frequency active support method according to claim 1 is characterized in that: In S1, the method of simulating the inertia characteristics of the synchronous generator using the DC link capacitor control strategy includes: S11. Determine the capacitor control target, deeply analyze the impact of the grid frequency change law on the stability of the power system, set the normal range of the grid frequency, when the grid frequency is lower than the normal range, it indicates that the grid is facing a power shortage and the inverter needs to provide additional active power to support the frequency. At this time, the capacitor discharge rate should be increased; when the grid frequency is higher than the normal range, the grid has excess power, and the inverter needs to reduce the active power output, that is, increase the capacitor charging rate; when the frequency is within the normal range, ensure the balance of capacitor charging and discharging to maintain the stability of the inverter output power and avoid unnecessary interference to the grid; S12. Grid frequency monitoring and judgment: select a frequency sensor with high accuracy and fast response, install it at the connection point between the grid and the inverter, obtain the real-time grid frequency, and set the frequency change threshold; S13. Adjust the capacitor charge and discharge rate according to the frequency change: For new energy inverters, the DC side capacitor is a key component; the energy storage formula of the capacitor is: In the formula, C is the capacitance value, Vdc is the DC voltage; at the same time, the relationship between power and energy is: Derivative of the capacitor energy storage formula yields It can be seen that by controlling the change rate of the DC side voltage The active power output of the inverter can be adjusted; Based on the above theoretical basis, the capacitance control simulation method includes: When the grid frequency decreases, the discharge rate of the capacitor is increased. According to the energy storage formula of the capacitor The relationship between power and energy By increasing the rate of change of the DC voltage To increase active power output; in, k1 is the proportional coefficient, Δf is the frequency deviation, Δf=fmin-f, fmin is the set lower limit of the grid frequency, and f is the actual grid frequency; this process is similar to the inertia characteristic of a synchronous generator that releases kinetic energy to increase active power output when the frequency drops, thereby providing virtual inertia for the grid and enhancing the system's resistance to frequency drops; When the grid frequency rises, the charging rate of the capacitor increases; that is, the To reduce the output of active power; During the increase of grid frequency k2 is the proportionality coefficient, Δf=f-fmax, fmax is the set upper limit of the grid frequency; The grid frequency is within the normal range, i.e. Maintaining the inverter output power stability and avoiding unnecessary interference to the power grid is in line with the characteristics of synchronous generators maintaining stable output during normal operation.

3. The virtual inertia fuzzy adaptive new energy inverter frequency active support method according to claim 1 is characterized in that: The S2 includes: S21. Grid frequency detection and data acquisition: Based on the simulation results of step S1, the grid frequency is measured at the connection point between the grid and the inverter. The power synchronization ring has the characteristics of high precision and fast response, and can track the slight changes of the grid frequency in real time to ensure that the acquired frequency data is accurate and reliable. At the same time, the actual output power of the inverter is measured by the power measurement device, and the preset power reference value is obtained; S22. Calculate and analyze the power difference. Calculate the power difference, which reflects the degree of deviation between the actual output power of the inverter and the expected output power. Analyze the power difference to determine its positive and negative sign and size to determine the direction and degree of power imbalance. Among them, the power difference ΔP, ΔP = Pact-Pref; In the formula, Pact is the measured actual output power of the inverter, and Pref is the preset power reference value; when ΔP>0, it means that the actual output power is greater than the reference power, resulting in an increase in the grid frequency; when ΔP<0, the frequency decreases; S23. Power difference and frequency deviation conversion, converting the power imbalance reflected by the power difference into frequency deviation; the conversion process involves the power-frequency characteristic relationship, which is achieved through a pre-established empirical formula, which is expressed as: Δferr=kp2ΔP+f-fref; f is the actual grid frequency; fref is the grid frequency given value; Δferr is the frequency deviation; kp2 is the proportional coefficient determined according to the grid characteristics; The above formula comprehensively considers the influence of power difference on frequency deviation and the direct deviation between actual frequency and given frequency, making the calculation of frequency deviation more accurate and comprehensive; When the power difference ΔP is large, the influence of the power difference ΔP on the frequency deviation will also increase accordingly, which is reflected in the frequency deviation calculation through the kp2ΔP term; at the same time, the f-fref term directly reflects the deviation between the actual frequency and the given frequency.

4. The virtual inertia fuzzy adaptive new energy inverter frequency active support method according to claim 1 is characterized in that: The S3 specifically includes: S31. Fuzzy control module architecture is built. The fuzzy control module includes fuzzification interface, fuzzy rule base, fuzzy inference engine and clarification interface. The fuzzification interface converts the precise input frequency deviation into fuzzy quantity so that it can match the rules in the fuzzy rule base. The fuzzy rule base stores a series of predefined fuzzy rules, which describe the fuzzy relationship between input and output. The fuzzy inference engine performs inference calculation based on the input fuzzy quantity and fuzzy rules to obtain fuzzy output. The clarification interface converts the fuzzy output into precise proportional term coefficient and differential term coefficient for actual control operation. S32. Definition of fuzzy sets and membership functions. For the input variable Δferr, its fuzzy set is defined as: {negative large (NB), negative medium (NM), zero (ZO), positive medium (PM), positive large (PB)}; According to the range of frequency deviation in actual operation of the power grid, the interval of each fuzzy set is determined: When Δferr<-a, the membership is NB; when -a≤Δferr<-b, the membership is NM, and so on; at the same time, the corresponding fuzzy sets are defined for the proportional term coefficient Kp and the differential term coefficient Kd: {Very small (VS), small (S), medium (M), large (L), very large (VL)}; S33. According to the system's requirements for frequency regulation and rich control experience, a fuzzy rule table is formulated. When Δferr is NB, in order to quickly increase the frequency, Kp should be increased to VL, and Kd should also be increased to VL, so that the controller can respond quickly when the frequency drops sharply, thereby enhancing the stability of the system; S34. Real-time adjustment. After the frequency deviation is obtained in real time, it is subjected to fuzzification, fuzzy reasoning and clarity processing in turn; it is converted into a fuzzy quantity through fuzzification processing, and then fuzzy reasoning is performed according to the fuzzy rule table to obtain the fuzzy output of the proportional term coefficient Kp and the differential term coefficient Kd, and finally the fuzzy output is converted into the precise proportional term coefficient K′p and differential term coefficient K′d values ​​through clarity processing; the precise value will adjust the coefficient of the virtual inertia controller in real time, so that the inverter can dynamically optimize its frequency response according to the change of the grid frequency, and effectively improve the system's adaptability and stability to frequency fluctuations; when the grid frequency drops, by adjusting the precise proportional term coefficient K′p and differential term coefficient K′d, the active power output of the inverter can be increased to slow down the frequency decline trend; when the frequency rises, corresponding adjustments are made to suppress the frequency increase to ensure that the grid frequency always fluctuates within a reasonable range.

5. The virtual inertia fuzzy adaptive new energy inverter frequency active support method according to claim 1 is characterized in that: The S4 includes: S41. Calculate the given value of the d-axis current, based on the proportional term coefficient K′p and the differential term coefficient K′d output by the fuzzy control module, and combine the voltage equation of the inverter in the dq coordinate system to calculate the d-axis; The calculation formula of the d-axis current given value idref is: Wherein, id0 is the initial d-axis current given value, id0 is the reference value pre-set according to the rated power of the inverter and the grid voltage system parameters; K′ P The (Δferr) part indicates that idref is adjusted according to the size of the current frequency deviation Δferr. When the frequency deviation is large, K′ P It will be adjusted to a suitable value according to the fuzzy rule table (e.g., when the frequency deviation is negative large NB, it will be increased to VL), so that idref will be increased or decreased accordingly to change the active power output; The first part takes into account the speed of frequency deviation change. If the frequency deviation changes rapidly, K′d will also be adjusted according to the fuzzy rules, and idref will be further adjusted to enable the inverter to respond to frequency changes faster. S42. Calculation of the q-axis current given value. The calculation of the q-axis current given value is relatively simple. When no special reactive power adjustment is performed, the q-axis current given value iqref is equal to the initial axis current given value iq0. iq0 is the initial setting value based on the reactive power demand of the system. When the reactive power of the power grid is balanced, iq0 is set to a value that makes the reactive output of the inverter zero or close to zero. S43. Calculate the active power and reactive power of the inverter based on the accurately calculated d-axis and q-axis current given values: Active power P, P = 1.5udidref, ud is the d-axis voltage, which is related to the grid voltage and inverter control mode; Reactive power Q, Q = 1.5uqiqref, uq is the q-axis voltage; By changing idref and iqref, independent control of active power P and reactive power Q can be achieved; when the grid frequency decreases, idref will increase, causing P to increase, and the inverter will inject more active power into the grid to slow down the frequency decline trend; if the grid reactive power demand changes, such as insufficient reactive power, iqref can be increased to increase Q, meet the grid reactive power demand, maintain grid voltage stability, and indirectly support grid frequency stability; throughout the process, the inverter continuously and dynamically adjusts the current set value according to the real-time changes in grid frequency and reactive power demand, thereby accurately controlling power output and effectively supporting stable grid frequency operation.

6. The virtual inertia fuzzy adaptive new energy inverter frequency active support method according to claim 1 is characterized in that: The S5 includes: S51 real-time monitoring of the frequency of the fuzzy control module and the active power P and reactive power Q output by the inverter; S52. Calculation of performance evaluation index: Design a performance index function J to evaluate the performance of the adaptive control module. J is expressed as: f is the actual grid frequency; fref is the grid frequency given value; Pref and Qref are the reference values ​​of active power P and reactive power Q, respectively, which are determined according to grid operation requirements, inverter rated capacity and load characteristics; λ1 and λ2 are weight coefficients, which are used to weigh the importance of frequency deviation, active power deviation and reactive power deviation in performance indicators; By calculating the value of the performance index J, the performance of the control module can be quantitatively evaluated; if the J value is small, it means that the system performs well in frequency control, active power P and reactive power Q regulation, and the control effect is ideal; on the contrary, if the value J is large, it means that the system has performance problems and needs further optimization; S52. Parameter adjustment and optimization strategy implementation, adjust the parameters of the fuzzy controller according to the performance evaluation results; if the J value is too large, analyze the frequency deviation (f-fref) 2, active power deviation (P-Pref) 2 and reactive power deviation (Q-Qref) 2 Contribution to performance index J; If the contribution of frequency deviation is large, it means that the frequency control effect is not good, and the reduction range of proportional term coefficient and differential term coefficient should be appropriately increased when the frequency deviation is positive PB; Similar analysis and adjustment methods are also used for situations where active power or reactive power deviations contribute greatly. After adjusting the parameters, the frequency and power data are continuously monitored in real time, the performance indicators are recalculated, a closed-loop feedback control is formed, and the response of the fuzzy controller is continuously optimized to gradually improve the system performance and ultimately achieve a satisfactory control effect, ensuring that the inverter can effectively support the grid frequency and maintain power balance under various operating conditions.

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