Virtual impedance design method based on VUFP adaptive control

Abstract

The invention discloses a virtual impedance design method based on VUFP adaptive control. The method comprises the following steps: collecting reactive power output by an inverter and calculating a reactive power error and an error change rate; a fuzzy controller and a variable universe scaling factor are constructed, the fuzzy controller dynamically adjusts PI parameters according to the reactive power error and the change rate of the reactive power error, the variable universe scaling factor adjusts a quantization factor and a scaling factor of the fuzzy controller according to the size of the reactive power error, and the universe range of the fuzzy controller is adjusted in real time; and calculating a virtual impedance value according to the adjusted PI parameter, and dynamically compensating the line impedance, thereby realizing uniform distribution of reactive power and circulating current suppression. According to the method, the fuzzy value of the virtual impedance is fitted by using the VUFP theory, the line impedance is dynamically compensated, uniform distribution of reactive power is realized, and the self-adaptability and the control precision of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual impedance design, and particularly to a virtual impedance design method based on VUFP adaptive control. Background Art

[0002] When low-voltage microgrid inverters operate in parallel, due to the inconsistency of line impedances among inverters, the traditional droop control will cause uneven distribution of reactive power, resulting in a circulating current problem and reducing the system stability. Currently, the methods of using a fixed-value virtual inductor or PI regulating virtual impedance are often adopted to solve this problem, but these methods have disadvantages such as limited accuracy and poor adaptability. Summary of the Invention

[0003] In order to overcome the defects and deficiencies existing in the prior art, the present invention provides a virtual impedance design method based on VUFP adaptive control. By analyzing the error and error change rate between the theoretical value and the actual value of the reactive power output by the inverter, the present invention uses the VUFP theory to fit the fuzzy value of the virtual impedance, dynamically compensates the line impedance, and realizes the uniform distribution of reactive power. This strategy combines fuzzy control and variable universe thought, optimizes the PI parameters and adjusts the universe of discourse of the fuzzy controller in real time, significantly improving the self-adaptability and control accuracy of the system.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] The present invention provides a virtual impedance design method based on VUFP adaptive control, including the following steps:

[0006] Collect the reactive power output by the inverter and calculate the reactive power error and error change rate;

[0007] Construct a fuzzy controller and a variable universe stretching factor. The fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate, and the variable universe stretching factor adjusts the quantization factor and scale factor of the fuzzy controller according to the magnitude of the reactive power error, and adjusts the universe of discourse range of the fuzzy controller in real time;

[0008] Calculate the virtual impedance value according to the adjusted PI parameters and dynamically compensate the line impedance to achieve the uniform distribution of reactive power and suppression of circulating current.

[0009] As a preferred technical solution, collecting the reactive power output by the inverter and calculating the reactive power error and error change rate specifically includes:

[0010] Two inverters sample their own output voltage and current to obtain reactive power, obtain the average value through low-bandwidth communication, and compare it with the reactive power actually output by themselves to obtain the reactive power error.

[0011] As a preferred technical solution, the fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate, specifically including:

[0012] According to the change ranges of the error and the error change rate, a fuzzy universe of discourse and membership functions are established, and through fuzzy processing, the accurate values of the input variables are converted into fuzzy values. The input and output universes of discourse in fuzzy quantization are both set to [-1, 1]. After defuzzifying the obtained fuzzy quantities, the accurate values of the PI increment are obtained, realizing the adaptive adjustment of the PI parameters.

[0013] As a preferred technical solution, the membership function adopts a triangular membership function.

[0014] As a preferred technical solution, the fuzzy rule table of the fuzzy controller is designed according to the fuzzy subsets of the reactive power error and the error change rate.

[0015] As a preferred technical solution, the fuzzy subsets include negative large, negative small, zero, positive small, and positive large.

[0016] As a preferred technical solution, the variable universe scaling factor adjusts the quantization factor and the proportionality factor of the fuzzy controller according to the magnitude of the reactive power error, specifically expressed as:

[0017]

[0018] where a, a c , β are the scaling factors of the variable universe control output, is the quantization factor in the fuzzy controller, represents the proportionality factor in the fuzzy controller, K e , K ec are the corrected quantization factors, and K p , K i are the corrected proportionality factors.

[0019] As a preferred technical solution, the scaling factor adopts the form of a proportional exponential function or a logarithmic function.

[0020] The present invention also provides a virtual impedance design system based on VUFP adaptive control, including: a reactive power acquisition module, an error calculation module, a fuzzy controller construction module, a variable universe scaling factor construction module, a virtual impedance value calculation module, and a line control module;

[0021] The reactive power acquisition module is used to acquire the reactive power output by the inverter;

[0022] The error calculation module is used to calculate the reactive power error and the error change rate;

[0023] The fuzzy controller construction module is used to construct a fuzzy controller, and the fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its rate of change;

[0024] The variable universe scaling factor construction module is used to construct a variable universe scaling factor, and the variable universe scaling factor adjusts the quantization factor and scale factor of the fuzzy controller according to the magnitude of the reactive power error, and adjusts the universe range of the fuzzy controller in real time;

[0025] The virtual impedance value calculation module is used to calculate the virtual impedance value according to the adjusted PI parameters;

[0026] The line control module is used to dynamically compensate the line impedance based on the virtual impedance value to achieve uniform distribution of reactive power and suppression of circulating current.

[0027] The present invention also provides a computer device, including a processor and a memory for storing programs executable by the processor. When the processor executes the programs stored in the memory, the virtual impedance design method based on VUFP adaptive control as described above is implemented.

[0028] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0029] Verified by the MATLAB / Simulink simulation platform, the present invention can quickly achieve uniform distribution of reactive power under different line impedance conditions and effectively suppress circulating current. Compared with the traditional fixed virtual impedance and adaptive virtual impedance strategies, the present invention has higher stability and adaptability. Brief Description of the Drawings

[0030] Figure 1 It is a block diagram of the virtual impedance design based on VUFP adaptive control of the present invention;

[0031] Figure 2 It is a working flow chart of the fuzzy controller of the present invention;

[0032] Figure 3 It is a three-dimensional rule graph of the output variable of the fuzzy controller of the present invention;

[0033] Figure 4 It is a three-dimensional rule graph of the scaling factor a of the present invention;

[0034] Figure 5 It is a simulation schematic diagram of the virtual impedance design based on VUFP adaptive control of the present invention;

[0035] Figure 6 It is a control block diagram after the inverter of the present invention introduces virtual impedance;

[0036] Figure 7 It is a schematic diagram of the operation result of the fixed virtual impedance strategy of the present invention;

[0037] Figure 8 Schematic diagram of the operation result of the adaptive virtual impedance strategy of the present invention;

[0038] Figure 9 Schematic diagram of the operation result of the virtual impedance based on the VUFP adaptive control of the present invention. Detailed implementation manners

[0039] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] Embodiment 1

[0041] As Figure 1 shown, this embodiment provides a virtual impedance design method based on VUFP adaptive control, which uses VUFP (variable universe fuzzy PID algorithm) control to improve the virtual impedance. Its control strategy is divided into two parts: fuzzy control and variable universe. In order to solve the problem of complex parameter adjustment of the PI controller in the adaptive virtual impedance, a fuzzy controller is first introduced to optimize the PI controller, and the fuzzy controller is used to dynamically adjust the PI parameters. At the same time, the quantization parameter and the proportional parameter of the fuzzy controller are implemented with online dynamic matching, so that the universe range shrinks and expands in real time according to the system state error. This dynamic boundary adjustment method can effectively overcome the adjustment lag defect of the traditional fixed universe and significantly improve the working condition adaptability of the fuzzy controller;

[0042] This embodiment is used for the uniform distribution of reactive power and the suppression of circulating current in the parallel system of low-voltage microgrid inverters. By dynamically compensating the line impedance, the circulating current is effectively suppressed and the uniform distribution of reactive power is achieved, and the stability is significantly improved. The specific steps are as follows:

[0043] S1: Collect the reactive power output by the inverter and calculate the reactive power error (e) and the error change rate (ec);

[0044] Specifically, two inverters sample their own output voltage and current to obtain reactive power, use low-bandwidth communication to obtain the average value, and compare it with the reactive power actually output by themselves, so as to obtain the reactive power error e;

[0045] S2: Construct a fuzzy controller and a variable universe stretching factor. The fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate, and the variable universe stretching factor is used to adjust the universe range of the fuzzy controller in real time;

[0046] As Figure 2As shown, the inputs of the fuzzy controller are the reactive power error (e) and the error change rate (ec), and the outputs are the increments of the PI parameters (ΔK p , ΔK i ). The actually collected reactive power error e and error change rate ec are numerically quantified to become E and EC, and then ΔK p , ΔK i are obtained;

[0047] Specifically, according to the variation ranges of the error and the error change rate, a fuzzy universe of discourse and membership functions are established, and through fuzzy processing, the accurate values of the input variables are converted into fuzzy values. The input and output universes of discourse in fuzzy quantization are both set to [-1, 1]. The obtained fuzzy quantity is defuzzified to obtain the accurate value of the PI increment, thereby realizing the adaptive adjustment of the PI parameters;

[0048] As Figure 3 shown, the fuzzy controller uses triangular membership functions and a fuzzy rule table for control. The fuzzy rule table of the fuzzy controller is designed according to the fuzzy subsets of the reactive power error (e) and the error change rate (ec). The fuzzy subsets include negative big (NB), negative small (NS), zero (ZO), positive small (PS), and positive big (PB);

[0049] As shown in Table 1 and Table 2 below, the fuzzy control rules of ΔK p , ΔK i are designed

[0050] Table 1 Fuzzy control rule table of ΔK p

[0051]

[0052]

[0053] Table 2 Fuzzy control rule table of ΔK i

[0054]

[0055] In this embodiment, the variable universe scaling factor dynamically adjusts the quantization factors (K e , K ec ) and the scale factors (K p , K i ) of the fuzzy controller according to the magnitude of the reactive power error (e) to achieve the adaptive adjustment of the universe of discourse. The calculation of the variable universe scaling factor adopts a method combining a fuzzy controller or a functional type with a fuzzy type, and the scaling factors of the input variables adopt the form of a proportional exponential function or a logarithmic function.

[0056] The quantization factors (K e, K ec ), and the adjustment formula for the scale factor (K p , K i ) is as follows:

[0057]

[0058] Among them, a, a c , and β are the scaling factors of the variable universe control output, is the quantization factor in the fuzzy controller, represents the scale factor in the fuzzy controller, K e , K ec are the revised quantization factors, and K p , K i are the revised scale factors;

[0059] When the reactive power error e increases, the universe is expanded by increasing the scaling factor, and at the same time, the fuzzy rules are also applicable to a larger range; similarly, when the reactive power error e decreases, the universe is contracted by decreasing the scaling factor, which can greatly increase the number of fuzzy control rules near the zero point, thereby improving the accuracy of the system when the error is very small.

[0060] As Figure 4 shown, for the scaling factors a, a c of the input variables, a fuzzy controller is used for calculation, with the reactive power error and the error change rate as the input variables of the controller. The output fuzzy universe of a, a c takes [0, 1] and is represented by 4 fuzzy subsets {ZO zero, S small, M medium, L large}. The scaling factors a, a c adopt the same control rules. As shown in Table 3 below, the scaling factors a, a c are determined by two parameters, the reactive power error e and the error change rate ec. For example: if e is NB and ec is NB, then a is L, and a c Similarly, the following control rule table is obtained:

[0061] Table 3 Control rule table of the scaling factor a

[0062]

[0063] S3: Calculate the virtual impedance value according to the adjusted PI parameters and dynamically compensate the line impedance to achieve uniform distribution of reactive power and suppression of circulating current;

[0064] As Figure 5As shown, the fuzzy controller and variable universe scaling factor are implemented through the MATLAB / Simulink simulation platform to verify the control effect of the virtual impedance strategy. A virtual impedance simulation model is built on the MATLAB / Simulink platform, including two parallel inverters, an AC bus, and a load. By simulating and analyzing the reactive power distribution and circulating current magnitude under different virtual impedance strategies, such as Figure 6 As shown, after introducing virtual impedance into the inverter, the control process is realized.

[0065] Such as Figure 7 , Figure 8 and Figure 9 As shown, the simulation results are analyzed as follows:

[0066] Such as Figure 7 As shown, a simulation is carried out under the fixed virtual impedance control strategy (Strategy 1). To filter out the high-order harmonics in the inverter output current, the fundamental wave in the sampled current is extracted through a band-pass filter, and the voltage drop across the virtual inductor can be obtained by introducing a fixed-value virtual inductor. In this simulation, the value of the virtual inductor is taken as 0.003 mH. From 0 to 1 s, the reactive powers of Inverters 1 and 2 are 530 Var and 380 Var respectively, and the peak value of the circulating current is 2 A. After suddenly adding a load, from 1 to 2 s, the reactive powers of Inverters 1 and 2 are 1030 Var and 730 Var respectively, and the peak value of the circulating current rises to 3 A. It can be seen that the reactive power distribution of the two inverters is uneven, and the circulating current will increase with the increase of the load;

[0067] Such as Figure 8 As shown, a simulation is carried out under the adaptive virtual impedance control strategy (Strategy 2). Using the reactive power information transmitted by low-bandwidth communication, after PI regulation, the virtual impedance Zv is output, so as to achieve the equal sharing of reactive power. From 0 to 1 s, the reactive powers of Inverters 1 and 2 are evenly shared at 0.23 s, with a magnitude of 500 Var, and the peak value of the circulating current is 1 A. After suddenly adding a load, from 1 to 2 s, the reactive power continues to be evenly shared, with a magnitude of 1000 Var, and the peak value of the circulating current is 1.27 A. Comparing with the results of the fixed-value virtual impedance in Strategy 1, it can be seen that the reactive power is evenly shared, and the magnitude of the circulating current is also controlled within a suitable range.

[0068] Such as Figure 9 As shown, a simulation is carried out on the adaptive virtual impedance under VUFP control. The reactive power is evenly distributed after 0.13 s, reaching a stable state faster than Strategy 2. In addition, the peak value of the circulating current is 0.9 A from 0 to 1 s and 1.3 A from 1 to 2 s. It can be seen that the circulating current is less than that of Strategy 2 before and after adding the load. Thus, under the VUFP adaptive control strategy, the reactive power distribution reaches a stable state faster, and the circulating current is further reduced.

[0069] In this embodiment, by analyzing the error and the error change rate between the theoretical value and the actual value of the reactive power output by the inverter, the fuzzy value of the virtual impedance is fitted using the VUFP (variable universe fuzzy PID) theory, and the line impedance is dynamically compensated to achieve uniform distribution of reactive power with high stability.

[0070] Embodiment 2

[0071] This embodiment provides a virtual impedance design system based on VUFP adaptive control for implementing the virtual impedance design method based on VUFP adaptive control in Embodiment 1 above. The system includes: a reactive power acquisition module, an error calculation module, a fuzzy controller construction module, a variable universe stretching factor construction module, a virtual impedance value calculation module, and a line control module;

[0072] In this embodiment, the reactive power acquisition module is used to acquire the reactive power output by the inverter;

[0073] In this embodiment, the error calculation module is used to calculate the reactive power error and the error change rate;

[0074] In this embodiment, the fuzzy controller construction module is used to construct a fuzzy controller, and the fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate;

[0075] In this embodiment, the variable universe stretching factor construction module is used to construct a variable universe stretching factor, and the variable universe stretching factor adjusts the quantization factor and the proportional factor of the fuzzy controller according to the magnitude of the reactive power error, and adjusts the universe range of the fuzzy controller in real time;

[0076] In this embodiment, the virtual impedance value calculation module is used to calculate the virtual impedance value according to the adjusted PI parameters;

[0077] In this embodiment, the line control module is used to dynamically compensate the line impedance based on the virtual impedance value to achieve uniform distribution of reactive power and suppression of circulating current.

[0078] Embodiment 3

[0079] This embodiment provides a computing device, which can be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal devices with a display function. The computing device includes a processor and a memory. When the processor executes the programs stored in the memory, the virtual impedance design method based on VUFP adaptive control in Embodiment 1 is implemented.

[0080] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A virtual impedance design method based on VUFP adaptive control, characterized in that: The steps include: Collect the reactive power output by the inverter and calculate the reactive power error and error change rate; Construct a fuzzy controller and a variable domain expansion factor. The fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate. The variable domain expansion factor adjusts the quantization factor and proportional factor of the fuzzy controller according to the size of the reactive power error, and adjusts the domain range of the fuzzy controller in real time. The virtual impedance value is calculated based on the adjusted PI parameters and the line impedance is dynamically compensated to achieve uniform distribution of reactive power and suppression of circulating current.

2. The virtual impedance design method based on VUFP adaptive control according to claim 1, characterized in that: Collect the reactive power output by the inverter and calculate the reactive power error and error change rate, including: The two inverters sample their own output voltage and current to obtain reactive power, use low-bandwidth communication to obtain the average value, and compare it with their actual output reactive power to obtain the reactive power error.

3. The virtual impedance design method based on VUFP adaptive control according to claim 1, characterized in that: The fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its rate of change, including: According to the variation range of error and error change rate, the fuzzy domain and membership function are established, and the exact value of the input variable is converted into a fuzzy value through fuzzification processing. The input and output domains in fuzzy quantization are set to [-1, 1]. The obtained fuzzy quantity is defuzzified to obtain the exact value of the PI increment, thereby realizing adaptive adjustment of PI parameters.

4. The virtual impedance design method based on VUFP adaptive control according to claim 3 is characterized in that: The membership function adopts a triangle membership function.

5. The virtual impedance design method based on VUFP adaptive control according to claim 3 is characterized in that: The fuzzy rule table of the fuzzy controller is designed based on the fuzzy subsets of reactive power error and error change rate.

6. The virtual impedance design method based on VUFP adaptive control according to claim 5, characterized in that: The fuzzy subsets include negative large, negative small, zero, positive small and positive large.

7. The virtual impedance design method based on VUFP adaptive control according to claim 1, characterized in that: The variable universe expansion factor adjusts the quantization factor and proportional factor of the fuzzy controller by the size of the reactive power error, which is specifically expressed as: Among them, a, a c , β is the scaling factor of the variable domain control output, is the quantization factor in the fuzzy controller, represents the proportional factor in the fuzzy controller, K e , K ec is the modified quantization factor, K p , K i is the corrected scaling factor.

8. The virtual impedance design method based on VUFP adaptive control according to claim 1, characterized in that: The scaling factor takes the form of a proportional exponential function or a logarithmic function.

9. A virtual impedance design system based on VUFP adaptive control, characterized in that: include: Reactive power acquisition module, error calculation module, fuzzy controller construction module, variable domain expansion factor construction module, virtual impedance value calculation module, line control module; The reactive power acquisition module is used to acquire the reactive power output by the inverter; The error calculation module is used to calculate reactive power error and error change rate; The fuzzy controller building module is used to build a fuzzy controller, and the fuzzy controller dynamically adjusts the PI parameters according to the reactive power error and its change rate; The variable universe scaling factor construction module is used to construct a variable universe scaling factor, which adjusts the quantization factor and the proportional factor of the fuzzy controller according to the size of the reactive power error, and adjusts the universe range of the fuzzy controller in real time; The virtual impedance value calculation module is used to calculate the virtual impedance value according to the adjusted PI parameters; The line control module is used to dynamically compensate the line impedance based on the virtual impedance value to achieve uniform distribution of reactive power and suppression of circulating current.

10. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the virtual impedance design method based on VUFP adaptive control as claimed in any one of claims 1 to 8 is implemented.

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