Island microgrid frequency active support control method and system

Abstract

The application discloses an island micro-grid frequency active support control method and system, comprising new energy inverter DC side inertia simulation control and virtual inertia fuzzy adaptive control two parts. Wherein, the new energy inverter DC side inertia simulation control provides virtual inertia to island micro-grid by using DC side capacitor release / absorption energy to simulate the inertia characteristics of synchronous generator rotor, to realize the frequency regulation ability similar to synchronous generator. The virtual inertia fuzzy adaptive control method designs fuzzy adaptive control rules based on frequency response interval, and the virtual inertia is adaptively set, which can slow down the system frequency deviation rate and accelerate the system frequency recovery, overcoming the problem that the traditional control method cannot consider the system frequency change rate and frequency recovery speed.

Description

Technical Field

[0001] This invention relates to the field of inverter control, and in particular to a method and system for active frequency support control of an islanded microgrid. Background Technology

[0002] The high proportion of renewable energy generation connected to isolated microgrids leads to a significant increase in the power electronics characteristics of these microgrids. The inertia level of the microgrid system continuously declines, and due to the lack of stable frequency and voltage support from the main grid, isolated microgrids face severe risks of voltage and frequency instability, with frequency stability being particularly prominent. On the one hand, low inertia increases the rate of frequency change; on the other hand, low inertia shifts the frequency minimum point downwards. When these two frequency indicators reach thresholds, protection devices are triggered, resulting in generator and load shedding operations. The resulting chain reaction may cause frequency instability or even collapse of the entire system. Therefore, it is urgent to improve the frequency transient stability of low-inertia microgrids with high renewable energy penetration. Furthermore, traditional virtual inertia control strategies based on DC-side capacitor voltage control of renewable energy inverters cannot achieve DC-side capacitor voltage recovery, and traditional virtual inertia control cannot simultaneously address system frequency offset and recovery characteristics. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an active frequency support control method and system for islanded microgrids, which can effectively prevent the frequency from rising or falling rapidly in the initial stage of load change and accelerate the recovery of microgrid frequency in the frequency recovery stage, thereby improving the frequency transient response characteristics of microgrids.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a frequency active support control method for an islanded microgrid, wherein the islanded microgrid includes a new energy power generation unit, a DC-DC converter, a DC-side capacitor, and a DC-AC inverter connected in sequence; the method includes the following steps:

[0005] S1, Collect the output current i of the DC-AC inverter o DC-AC inverter output capacitor voltage u o Microgrid bus three-phase voltage u busa u busb u busc The three-phase voltage u of the microgrid bus busa u busb u busc The angular frequency ω of the bus voltage is obtained through a phase-locked loop. bus and phase angle θ g Calculate the AC bus frequency deviation Δω: Δω = ω n -ω bus ; where ω nIndicates the rated angular frequency;

[0006] S2, the output current i of the DC-AC inverter o DC-AC inverter output capacitor voltage u o Perform abc / dq coordinate transformations respectively to obtain the output current i. o d-axis and q-axis currents i d i q and output capacitor voltage u o d-axis and q-axis voltages U gd U gq ;

[0007] S3. Convert the AC bus frequency deviation Δω and its derivative. Using these inputs as the first and second fuzzy controllers respectively, the output proportional coefficient k of the first fuzzy controller is obtained. p The output differential coefficient k of the second fuzzy controller d ; will k p Multiply by Δω to obtain the first product, then k d Multiply The second product is obtained, and the first and second products are added together to obtain the DC-side capacitor voltage change compensation amount Δu. dc ;

[0008] S4. Convert the DC-AC inverter DC-side capacitor voltage u dc Subtract the voltage command value u ref The difference plus Δu dc The difference e is obtained. u , will e u The signal is fed into a PI controller, and the PI controller's output, after being limited, generates the current command i for the inner current loop control. ref ;

[0009] S5. Transfer the active power command P of the DC-AC inverter ref Divide by Get i dr , then i dr and i ref The summation yields the d-axis current command value i of the inner current loop. dref Reactive power command Q ref Divide by Get i qref Current inner loop d-axis reference command i dref Subtract i d The difference e is obtained id Current inner loop q-axis reference command i qref Subtract i q The difference e is obtained iq e id and eiq After passing through a PI controller and then undergoing a dq / abc conversion, the SPWM modulation signal D is obtained. a D b D c ; For SPWM modulated wave D a D b D c The duty cycle signal of the DC-AC inverter switching transistor is obtained by bipolar modulation with the triangular carrier wave, which controls the switching transistor to turn on and off.

[0010] This invention utilizes the DC-side capacitor of the inverter to release / absorb energy to simulate the inertia characteristics of a synchronous generator rotor. The islanded microgrid provides virtual inertia to achieve frequency regulation capabilities similar to those of a synchronous generator.

[0011] At the initial instant of a sudden load change, the system frequency shift is very small Δω. If the proportional coefficient k p If the value of k is relatively small, then the virtual inertia output power of the DC capacitor in the renewable energy inverter is extremely limited during periods of sudden load changes. When faced with large load fluctuations, the power output from the inertia-supported response is insufficient to improve the power imbalance in the microgrid. To effectively improve the power imbalance in the microgrid during sudden load changes, a larger k can be selected. p This amplifies the effect of Δω. Furthermore, at the moment of sudden load change, The absolute value of k reaches its maximum. If this significant characteristic can be utilized for inertia-supported power, then the inertia-supported power will be at its maximum at the instant of load change, which is beneficial for improving the frequency change rate performance of the microgrid after load disturbance. Therefore, a larger k can be selected. d enlarge Its effects.

[0012] As the system frequency drops to its lowest point or rises to its highest point, the absolute value of Δω gradually increases, while the absolute value of the rate of frequency change gradually decreases. To ensure reliable inertial power support, therefore, k... p and k d A larger value should be chosen. When the frequency reaches its lowest point, The value is approximately 0, at which point even if k d Taking a larger value results in limited power support capacity for this branch, while the absolute value of Δω is at its maximum. Therefore, a larger k... p It can effectively overcome the problem of k alone. d The branch controller exhibits extremely weak inertial support response at the lowest frequency point, which can improve the system's response at the lowest frequency point; therefore, k p Choose Zhengda.

[0013] When the system frequency approaches the extreme point, due to When the value is equal to 0, the virtual inertia response of the DC-side capacitor can be ignored, thus failing to effectively improve the frequency extrema. However, when the microgrid frequency is at its lowest point, i.e., when the absolute value of Δω is maximum, then k can be fully utilized. p Amplify the effect of Δω to improve power imbalance in microgrids.

[0014] During the process of frequency recovery from the extreme point to a new steady state, considering the recovery of the DC bus voltage, k should be removed at this time. p The response of the branch, and because at this time Different sign.

[0015] When the system frequency is recovering, compared to the system frequency offset, With opposite signs, the differential control term provides a reverse power response, which prolongs the system frequency recovery time and is detrimental to system frequency recovery. Therefore, k d The value should be reduced to mitigate the adverse effects of the reverse active power response on the rapid recovery of the system frequency as much as possible. d It can even take negative values ​​to provide a positive active power response, supporting rapid frequency recovery in the microgrid. Therefore, k d The value is set to a large negative value to achieve rapid recovery of the microgrid frequency.

[0016] By considering the different requirements of virtual inertia in different frequency response ranges of the system, the virtual inertia can be adaptively adjusted to achieve a reasonable and optimized allocation of the active power support energy of the DC-side capacitor during the frequency response process. This allows for the recovery of the DC-side capacitor voltage of the new energy inverter while taking into account the frequency offset and recovery characteristics of the system. In the initial stage of load change, it can effectively prevent the frequency from rising or falling rapidly, and at the same time, it can accelerate the recovery of the microgrid frequency during the frequency recovery stage, thereby improving the frequency transient response characteristics of the microgrid.

[0017] In step S3, the AC bus frequency deviation Δω and its derivative are calculated. Using these inputs as the first and second fuzzy controllers respectively, the output proportional coefficient k of the first fuzzy controller is obtained. p The output differential coefficient k of the second fuzzy controller d The specific implementation process includes:

[0018] Set the initial fuzzy subsets of the output variables of the first fuzzy controller and the second fuzzy controller;

[0019] Based on the AC microgrid frequency deviation Δω and its derivative The proportional coefficient k of the virtual inertia controller p and the differential coefficient k of the virtual inertia controller dBased on the correspondence, fuzzy control rules are designed; Δω∈{deltaωNL, deltaωNS, deltaωZO, deltaωPS, deltaωPL} is a fuzzy subset of the first input variable Δω, and deltaωNL, deltaωNS, deltaωZO, deltaωPS, and deltaωPL respectively represent the AC microgrid frequency deviation Δω as negative large, negative small, medium, positive small, and positive large; For the second input variable fuzzy subset, ndω bus NL, ndω bus NS, ndω bus ZO, ndω bus PS, ndω bus PL represent the derivatives of the frequency deviation of the AC microgrid, respectively. For negative large, negative small, medium, positive small, positive large

[0020] The proportional coefficient k of the output variable virtual inertia controller p Each fuzzy subset is set to k p NL,k p NS, k p ZO, k p PS, k p PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively.

[0021] The differential coefficient k of the output variable virtual inertia controller d Each fuzzy subset is set to k d NL,k d NS, k d ZO, k d PS, k d PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively.

[0022] The actual values ​​of the AC microgrid frequency deviation and its derivative are used as inputs to the fuzzy controller and fuzzified to obtain the first fuzzy subset corresponding to the actual value of the proportional term of the virtual inertia controller and the second fuzzy subset corresponding to the actual value of the differential term of the virtual inertia controller.

[0023] Based on the fuzzy control rules and the first and second fuzzy subsets, a fuzzy subset of the output variable is obtained. Based on the initial fuzzy subset, the fuzzy subset of the output variable is clarified to obtain the proportional and differential coefficients of the current virtual inertia controller.

[0024] The fuzzy controller input selected in this invention is the AC microgrid frequency deviation Δω and the derivative of the AC microgrid frequency deviation. The output is the proportional coefficient k of the virtual inertia controller. p and the differential coefficient k of the virtual inertia controller d By combining the system's frequency response range and using fuzzy adaptive control to tune the proportional and differential coefficients of the virtual inertia controller, this invention ensures that the inverter's DC-side capacitor voltage can recover while also considering system frequency offset and recovery characteristics. Therefore, this invention achieves DC-side voltage recovery for new energy inverters, effectively preventing rapid frequency rises or falls during the initial stage of load surges, and accelerating microgrid frequency recovery during the frequency recovery phase, thus improving the microgrid's frequency transient response characteristics.

[0025] The process of defining a fuzzy subset of the first input variable includes:

[0026] Define the range of the first membership function; the first membership function includes multiple triangular membership functions.

[0027] Define the triangular membership function in the first membership function;

[0028] The range, type, and number of the first membership function are set as attributes of the first membership function. The attributes of the first membership function are input into the first fuzzy controller to obtain a fuzzy subset of the first input variable.

[0029] The process of defining a fuzzy subset of the second input variable includes:

[0030] Define the range of the second membership function; the second membership function includes multiple triangular membership functions.

[0031] Define the number of triangular membership functions in the second membership function;

[0032] The range, type, and number of the second membership function are set as attributes of the second membership function. The attributes of the second membership function are then input into the second fuzzy controller to obtain a fuzzy subset of the second input variable.

[0033] The process of setting the fuzzy subset of the first output variable includes:

[0034] Define the range of the third membership function; the third membership function includes multiple triangular membership functions.

[0035] Define the number of triangular membership functions in the third membership function;

[0036] The range, type, and number of the third membership function are set as attributes of the third membership function. The attributes of the third membership function are then input into the first fuzzy controller to obtain a fuzzy subset of the first output variable.

[0037] The process of setting the fuzzy subset of the second output variable includes:

[0038] Define the range of the fourth membership function; the fourth membership function includes multiple triangular membership functions.

[0039] Define the number of triangular membership functions in the fourth membership function;

[0040] The range, type, and number of the fourth membership function are set as attributes of the fourth membership function. The attributes of the fourth membership function are then input into the second fuzzy controller to obtain a fuzzy subset of the second output variable.

[0041] As an inventive concept, the present invention also provides an active frequency support control system for islanded microgrids, comprising:

[0042] One or more processors;

[0043] A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement the steps of the method described above.

[0044] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention selects the frequency deviation of the AC microgrid and its derivative as the input variables for fuzzy control, and the proportional coefficient and differential coefficient of the virtual inertia controller as the output variables for fuzzy control. This method uses the DC-side capacitor of the renewable energy inverter as an additional source of virtual inertia, and can adaptively adjust the virtual inertia control parameters according to the frequency response range of the AC microgrid. In the initial stage of load abrupt changes in islanded microgrids, it can effectively prevent rapid frequency rises or falls, and at the same time, it can accelerate the recovery of the microgrid frequency during the frequency recovery stage, thereby improving the frequency transient response characteristics of the microgrid. It overcomes the problem that traditional virtual inertia control strategies based on the DC-side capacitor voltage control of renewable energy inverters cannot achieve the recovery of the DC-side capacitor voltage of renewable energy inverters, and also overcomes the problem that traditional virtual inertia control cannot simultaneously consider system frequency offset and recovery characteristics. Attached Figure Description

[0045] Figure 1 This is a block diagram of a frequency active support control system for an islanded microgrid based on DC-side capacitor control of a new energy inverter, as described in an embodiment of the present invention.

[0046] Figure 2 The waveforms show the system's dynamic frequency response under different control methods during a sudden load increase.

[0047] Figure 3 The waveforms of the DC-side capacitor voltage of the inverter under different control methods during a sudden load increase are shown.

[0048] Figure 4 The waveforms show the system's frequency dynamic response under different control methods during a sudden load reduction.

[0049] Figure 5 The waveforms of the inverter's DC-side capacitor voltage under different control methods during a sudden load reduction are shown. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] Embodiment 1 of the present invention provides a frequency active support control method for islanded microgrids, comprising:

[0053] 1) At the beginning of each sampling period, the controller starts the A / D converter to measure the DC-side capacitor voltage u of the inverter. dc Inverter output current i o Capacitor voltage u o and microgrid bus voltage u bus The samples are collected separately, and all sampled data are converted by an A / D converter and then sent to the controller DSP for processing through a parallel interface.

[0054] 2) The controller processes the three-phase voltage u of the microgrid bus acquired in step 1). busa u busb u busc The angular frequency ω of the bus voltage is obtained through a phase-locked loop. bus and phase angle θ g Then calculate the AC bus frequency deviation using the following formula:

[0055] Δω=ω n -ω bus

[0056] Where, ω n Indicates the rated angular frequency; ω busIndicates the microgrid bus frequency;

[0057] 3) The controller processes the inverter output current i obtained in step 1). o Inverter output capacitor voltage u o Perform coordinate transformations abc / dq respectively, with a phase angle θ used for the transformation. g Then obtain i respectively d i q and U gd U gq .

[0058] 4) Combine Δω and As inputs to fuzzy controller 1 and fuzzy controller 2, after fuzzy control, fuzzy controller 1 outputs the proportional coefficient k of the virtual inertia controller. p The fuzzy controller 2 outputs the differential coefficient k of the virtual inertia controller. d .

[0059] 5) Put k p Multiply by Δω, and k d The derivative of the AC bus frequency deviation The above results are then added together to obtain the compensation amount Δu for the DC-side capacitor voltage change of the new energy converter. dc .

[0060] 6) The controller processes the inverter DC-side capacitor voltage u obtained in step 1). dc Subtract the voltage command value u ref Then add Δu dc The difference e can then be obtained. u , will e u The signal is fed into a PI controller, and the controller's output is limited to obtain the current command i for the inner current loop control. ref The expression is:

[0061]

[0062] k vp It is the proportional coefficient of the PI controller, k vi This represents the integral coefficient of the PI controller.

[0063] The inverter's active power command P ref Divide by i can be obtained dr , then i dr and i ref The summation yields the d-axis current command value i of the inner current loop. dref Reactive power command Q ref Divide by i can be obtained qrefCurrent inner loop d-axis reference command i dref Subtract i d The difference e is obtained id Current inner loop q-axis reference command i qref Subtract i q The difference e is obtained iq e id and e iq After passing through a PI controller and then undergoing a dq / abc conversion, the SPWM modulated signal D can be obtained. a D b D c .

[0064] 7) For the SPWM modulated wave D a D b D c The duty cycle signal of the switching transistor is obtained by bipolar modulation with the triangular carrier wave. This signal is then passed through the inverter's drive protection circuit to control the switching transistor's on and off states.

[0065] The specific implementation process of step 4) includes:

[0066] The frequency deviation of the AC microgrid Δω=ω n -ω bus It can be used as an input variable for fuzzy control; where ω n Indicates the rated angular frequency; ω bus This indicates the frequency of the microgrid bus.

[0067] The derivative of the frequency deviation of the AC microgrid It serves as the second input variable for fuzzy control.

[0068] The proportional term k of the virtual inertia controller p As the first output variable;

[0069] The differential term k of the virtual inertia controller d As the second output variable;

[0070] Set the initial fuzzy subset of the output variables;

[0071] Based on the AC microgrid frequency deviation Δω and its derivative The proportional coefficient k of the virtual inertia controller p and the differential coefficient k of the virtual inertia controller dBased on the correspondence, fuzzy control rules are designed; Δω∈{deltaωNL, deltaωNS, deltaωZO, deltaωPS, deltaωPL} is a fuzzy subset of the first input variable, and deltaωNL, deltaωNS, deltaωZO, deltaωPS, and deltaωPL represent the AC microgrid frequency deviation Δω as negative large, negative small, medium, positive small, and positive large, respectively; ndω is a fuzzy subset of the second input variable. bus NL, ndω bus NS, ndω bus ZO, ndω bus PS, ndω bus PL represent the derivatives of the frequency deviation of the AC microgrid, respectively. The values ​​are negative large, negative small, medium, positive small, and positive large.

[0072] The proportional coefficient k of the output variable virtual inertia controller p Each fuzzy subset is set to k p NL,k p NS, k p ZO, k p PS, k p PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively.

[0073] The differential coefficient k of the output variable virtual inertia controller d Each fuzzy subset is set to k d NL,k d NS, k d ZO, k d PS, k d PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively.

[0074] The actual values ​​of the AC microgrid frequency deviation and its derivative are used as inputs to the fuzzy controller and fuzzified to obtain the first fuzzy subset corresponding to the actual value of the proportional term of the virtual inertia controller and the second fuzzy subset corresponding to the actual value of the differential term of the virtual inertia controller.

[0075] Based on the fuzzy control rules and the first and second fuzzy subsets, a fuzzy subset of the output variable is obtained. Based on the initial fuzzy subset, the fuzzy subset of the output variable is clarified to obtain the proportional and differential coefficients of the current virtual inertia controller.

[0076] The process for obtaining the fuzzy subsets of the first input variable, the second input variable, and the output variable is the same; wherein, the process for obtaining the fuzzy subset of the first input variable includes: setting the range of the first membership function; the first membership function includes multiple triangular membership functions; setting the triangular membership function names in the first membership function; using the range, type, and number of the first membership function as attributes of the first membership function, and inputting the attributes of the first membership function into the fuzzy controller to obtain the fuzzy subset of the first input variable.

[0077] The process of setting a fuzzy subset of the second input variable includes: setting the range of the second membership function; the second membership function includes multiple triangular membership functions; setting the number of triangular membership functions in the second membership function; using the above-mentioned setting of the range, type, and number of the second membership function as attributes of the second membership function; inputting the attributes of the second membership function into the fuzzy controller to obtain the fuzzy subset of the second input variable.

[0078] The process of setting the fuzzy subset of the first output variable includes: setting the range of the third membership function; the third membership function includes multiple triangular membership functions; setting the number of triangular membership functions in the third membership function; using the above-mentioned setting of the range, type, and number of the third membership function as attributes of the third membership function; inputting the attributes of the third membership function into the fuzzy controller to obtain the fuzzy subset of the first output variable.

[0079] The process of setting the fuzzy subset of the second output variable includes: setting the range of the fourth membership function; the fourth membership function includes multiple triangular membership functions; setting the number of triangular membership functions in the fourth membership function; using the above-mentioned setting of the range, type, and number of the fourth membership function as attributes of the fourth membership function; inputting the attributes of the fourth membership function into the fuzzy controller to obtain the fuzzy subset of the second output variable.

[0080] In this embodiment of the invention, the number of membership functions of the triangle is 5.

[0081] In this embodiment of the invention, the reference value of the AC bus frequency is set to 50Hz. According to GB / T 35727-2017 "Guidelines for Medium and Low Voltage DC Distribution Voltage", the allowable range of frequency fluctuation in low-voltage microgrids is ±0.5Hz. Therefore, the membership function of the AC bus frequency deviation in this embodiment of the invention is determined to be [-0.5, 0.5].

[0082] The membership function for the AC bus frequency deviation is controlled within the range of [-0.5, 0.5], and the membership function type is selected as a triangular membership function. This embodiment of the invention requires the generation of five triangular membership functions for the AC bus frequency deviation, each with equal height and base length. The corresponding fuzzy subsets of the input variable DC bus voltage are set as deltaωNL, deltaωNS, deltaωZO, deltaωPS, and deltaωPL, representing the AC microgrid frequency deviation as negative large, negative small, medium, positive small, and positive large, respectively. Specifically, deltaωNL ranges from [-0.7493 to -0.2493], deltaωNS ranges from [-0.50], deltaωZO ranges from [-0.2493 to 0.2507], deltaωPS ranges from [0 to 0.5], and deltaωPL ranges from [0.2507 to 0.7507].

[0083] The membership function range of the derivative of the AC bus frequency deviation is controlled within [-1.5, 1.5], and the membership function type is selected as a triangular membership function. This embodiment of the invention requires the generation of five triangular membership functions for the supercapacitor's state of charge, each with equal height and base length. The corresponding fuzzy subsets of the input variable, the supercapacitor's state of charge, are set as ndω. bus NL, ndω bus NS, ndω bus ZO, ndω bus PS, ndω bus PL represents the derivatives of the AC microgrid frequency deviation as negative large, negative small, medium, positive small, and positive large, respectively; where ndω bus The range of NL is [-2.248 -0.7479], and ndω bus The range of NS is [-1.5 0], and ndω bus The range of ZO is [-0.7479 0.7521], and ndω bus The range of PS is [0 1.5], and ndω bus The range of PL is [0.7521 2.252];

[0084] The membership function range of the proportional coefficients of the virtual inertia controller is controlled within [0, 30], and the membership function type is selected as a triangular membership function. This embodiment of the invention requires generating five triangular membership functions for the proportional coefficients of the virtual inertia controller with equal height and base length. The fuzzy subsets of the proportional coefficients of the corresponding output variable, the virtual inertia controller, are set to k. p NL,k p NS, k p ZO, k p PS, k pPL represents the proportional term coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively, where k p The range of NL is [-7.48 7.521], k p The range of NS is

[015] , k p The range of ZO is [7.521 22.52], k p The range of PS is [15 30], k p The range of PL is [22.52 37.52].

[0085] The membership function range of the differential coefficients of the virtual inertia controller is controlled within [0, 50], and the membership function type is selected as a triangular membership function. This embodiment of the invention requires generating five triangular membership functions for the differential coefficients of the virtual inertia controller with equal height and base length. The fuzzy subsets of the corresponding output variable, the differential coefficients of the virtual inertia controller, are set to k. d NL,k d NS, k d ZO, k d PS, k d PL represents the proportional term coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively, where k d The range of NL is [-12.47 12.53], k d The range of NS is [0 25], k d The range of ZO is [12.53 37.53], k d The range of PS is [25 50], k d The range of PL is [37.53 62.53]. Table 1 lists the AC bus frequency deviation Δω and its derivative. The proportional coefficient k of the virtual inertia controller p The table below shows the correspondence between the AC bus frequency deviation Δω and its derivative. With the differential coefficient k of the virtual inertia controller d The corresponding relationship table.

[0086] Table 1 k p Tuned fuzzy control rule table

[0087]

[0088] Table 2 k d Tuned fuzzy control rule table

[0089]

[0090]

[0091] Depend on Figure 2 It can be seen that under the same load disturbance during a sudden load increase, the lowest system frequencies for the four control scenarios—no control, traditional proportional control, traditional derivative control, and the control method proposed in this invention—are 49.36Hz, 49.4Hz, 49.38Hz, and 49.45Hz, respectively. Without control, the system frequency is lowest and changes the fastest, exhibiting significant oscillations during frequency recovery. Traditional proportional control is the least effective at improving the frequency drop rate. While traditional derivative control improves the frequency change rate relatively well, it is not effective at improving the lowest frequency and has the longest frequency recovery time. The control method proposed in this invention provides the best improvement in both the lowest system frequency and the frequency change rate, exhibiting the smallest frequency drop amplitude and the slowest deviation rate, meaning the shortest system frequency recovery time.

[0092] Depend on Figure 3 It is known that when the disturbance ends, traditional proportional control will cause a continuous shift in the DC side voltage of the inverter, while the control strategy proposed in this embodiment of the invention can restore the DC side voltage.

[0093] Depend on Figure 4 It can be seen that under the same load disturbance, the maximum system frequency values ​​under the following conditions are 50.71Hz, 50.65Hz, 50.69Hz, and 50.6Hz, respectively, with no control applied, conventional proportional control, conventional derivative control, and the control proposed in this embodiment. Without control applied, the maximum system frequency is the highest, and the frequency change rate is the fastest, with significant oscillations observed during frequency recovery. Conventional proportional control is the least effective at improving the frequency descent rate.

[0094] When conventional differential control is applied, it can effectively improve and mitigate the rate of frequency deviation, but its effect on improving frequency extreme points is poor, and it is not conducive to rapid frequency recovery; conventional differential control has the longest frequency recovery time. When the control method proposed in this embodiment of the invention is used, the system has the best frequency transient response, the amplitude of frequency extreme points is significantly reduced, and the frequency deviation rate is significantly slowed down.

[0095] Depend on Figure 5 It is known that when the disturbance ends, traditional proportional control will cause a continuous shift in the DC side voltage of the inverter, while the control strategy proposed in this embodiment of the invention can restore the DC side voltage.

[0096] Example 2

[0097] Embodiment 2 of the present invention provides a control system corresponding to Embodiment 1 above, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method of Embodiment 1 above.

[0098] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0099] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0100] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0101] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A frequency active support control method for an islanded microgrid, the islanded microgrid comprising a new energy generation unit, a DC-DC converter, a DC-side capacitor, and a DC-AC inverter connected in sequence; characterized in that, Includes the following steps: S1. Collect the output current of the DC-AC inverter. DC-AC inverter output capacitor voltage Three-phase voltage of microgrid bus , , The three-phase voltage of the microgrid bus , , The angular frequency of the bus voltage is obtained through a phase-locked loop. and phase angle Calculate the AC bus frequency deviation. : ;in, Indicates the rated angular frequency; S2, the output current of the DC-AC inverter DC-AC inverter output capacitor voltage Perform abc / dq coordinate transformations respectively to obtain the output current. d-axis and q-axis currents , and output capacitor voltage d-axis and q-axis voltages , ; S3, AC bus frequency deviation and derivative The coefficients of the output proportional term of the first fuzzy controller are obtained by using them as inputs to the first fuzzy controller and the second fuzzy controller, respectively. The output differential coefficients of the second fuzzy controller ;Will Multiply To obtain the first product, Multiply The second product is obtained, and the first and second products are added together to obtain the compensation amount for the DC-side capacitor voltage change. ; S4. Convert the DC-AC inverter DC-side capacitor voltage Subtract voltage command value The difference plus The difference is obtained. , will The signal is fed into the PI controller, and the PI controller's output, after being limited, generates the current command for the inner current loop control. ; S5. Send the active power command of the DC-AC inverter. Divide by get Then and The summation yields the d-axis current command value of the inner current loop. Reactive power command Divide by get Current inner loop d-axis reference command minus Get the difference Current inner loop q-axis reference command minus Get the difference , and After passing through a PI controller, and then through a dq / abc converter, the SPWM modulation signal is obtained. , , ; for SPWM modulated waves , , The duty cycle signal of the DC-AC inverter switching transistor is obtained by bipolar modulation with the triangular carrier wave, which controls the switching transistor to turn on and off.

2. The active frequency support control method for islanded microgrids according to claim 1, characterized in that, In step S3, the AC bus frequency deviation is... and derivative The coefficients of the output proportional term of the first fuzzy controller are obtained by using them as inputs to the first fuzzy controller and the second fuzzy controller, respectively. The output differential coefficients of the second fuzzy controller The specific implementation process includes: Set the initial fuzzy subsets of the first output variable and the second output variable; Based on AC microgrid frequency deviation The derivative of the frequency deviation of AC microgrid Proportional coefficient of virtual inertia controller and the differential coefficients of the virtual inertia controller Based on the correspondence, design fuzzy control rules; Δ ∈{delta NL, delta NS, delta ZO, delta PS, delta PL} is the first input variable fuzzy subset, delta NL, delta NS, delta ZO, delta PS, delta PL represent the frequency deviation of the AC microgrid. The values ​​are negative large, negative small, medium, positive small, and positive large. ∈{nd bus NL,nd bus NS, nd bus ZO,nd bus PS, nd bus PL} is the second input variable fuzzy subset, nd bus NL,nd bus NS, nd bus ZO,nd bus PS, nd bus PL represent the derivatives of the frequency deviation of the AC microgrid, respectively. The values ​​are negative large, negative small, medium, positive small, and positive large. The first output variable is the proportional term coefficient of the virtual inertia controller. Each fuzzy subset is set as k p NL, k p NS, k p ZO, k p PS, k p PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively. The differential coefficient of the second output variable, the virtual inertia controller. Each fuzzy subset is set as k d NL, k d NS, k d ZO, k d PS, k d PL represents the proportional coefficient of the virtual inertia controller as negative large, negative small, medium, positive small, and positive large, respectively. The actual values ​​of the AC microgrid frequency deviation and its derivative are used as inputs to the fuzzy controller and fuzzified to obtain the first fuzzy subset corresponding to the actual value of the proportional term of the virtual inertia controller and the second fuzzy subset corresponding to the actual value of the differential term of the virtual inertia controller. Based on the fuzzy control rules and the first and second fuzzy subsets, the fuzzy subsets of the first and second output variables are obtained. Based on the initial fuzzy subsets, the fuzzy subsets of the first and second output variables are clarified to obtain the proportional and differential coefficients of the current virtual inertia controller.

3. The active frequency support control method for islanded microgrids according to claim 2, characterized in that, The process of defining a fuzzy subset of the first input variable includes: Define the range of the first membership function; The first membership function includes multiple triangular membership functions; Define the triangular membership function in the first membership function; The range, type, and number of the first membership function are set as attributes of the first membership function. The attributes of the first membership function are input into the first fuzzy controller to obtain a fuzzy subset of the first input variable.

4. The active frequency support control method for islanded microgrids according to claim 2, characterized in that, The process of defining a fuzzy subset of the second input variable includes: Define the range of the second membership function; The second membership function includes multiple triangular membership functions; Define the number of triangular membership functions in the second membership function; The range, type, and number of the second membership function are set as attributes of the second membership function. The attributes of the second membership function are then input into the second fuzzy controller to obtain a fuzzy subset of the second input variable.

5. The active frequency support control method for islanded microgrids according to claim 2, characterized in that, The process of setting the fuzzy subset of the first output variable includes: Define the range of the third membership function; The third membership function includes multiple triangular membership functions; Define the number of triangular membership functions in the third membership function; The range, type, and number of the third membership function are set as attributes of the third membership function. The attributes of the third membership function are then input into the first fuzzy controller to obtain a fuzzy subset of the first output variable.

6. The active frequency support control method for islanded microgrids according to claim 2, characterized in that, The process of setting the fuzzy subset of the second output variable includes: Define the range of the fourth membership function; The fourth membership function includes multiple triangular membership functions; Define the number of triangular membership functions in the fourth membership function; The range, type, and number of the fourth membership function are set as attributes of the fourth membership function. The attributes of the fourth membership function are then input into the second fuzzy controller to obtain a fuzzy subset of the second output variable.

7. A frequency active support control system for an islanded microgrid, characterized in that, include: One or more processors; A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to perform the steps of the method according to any one of claims 1 to 6.

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