Alternating-current micro-grid frequency adjusting device adopting fuzzy logic

Abstract

The invention discloses an alternating-current micro-grid frequency adjusting device adopting fuzzy logic, and relates to the technical field of micro-grid control, comprising an initialization module for initializing parameters; the data acquisition module acquires operation parameters in real time; the hierarchical control module comprises a primary control unit and a secondary control unit, the primary control unit outputs frequency and output voltage based on a droop control strategy, and the secondary control unit performs fine adjustment on frequency deviation and voltage deviation of adjustment of the primary control unit according to fuzzy logic; and the simulation optimization module constructs a micro-grid simulation model, performs simulation verification, and obtains optimal PI controller parameters by optimizing an objective function. By adopting fuzzy logic control and an objective function optimization algorithm, PI controller parameters are dynamically optimized, the stability of the micro-grid is effectively improved, the electric energy quality is optimized, frequency fluctuation and harmonic distortion are reduced, meanwhile, multi-objective optimization is achieved, and efficient and stable operation of the micro-grid under complex working conditions is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid control, and in particular to an AC microgrid frequency regulation device using fuzzy logic. Background Art

[0002] With the widespread application of distributed generation and renewable energy, microgrids, as an important part of the future power system, have gradually become a key technology to solve energy supply security, improve energy efficiency, and reduce environmental pollution. However, microgrids often face the following challenges during operation:

[0003] Frequency and voltage fluctuations: A common problem in microgrids is frequency and voltage fluctuations. Since microgrids usually rely on renewable energy sources such as solar energy and wind energy, and the output of these energy sources has strong randomness and volatility, the system frequency and voltage are unstable. Especially when the load changes or the output of renewable energy fluctuates greatly, traditional frequency control methods (such as proportional integral control, PI control) are often difficult to maintain stability in a dynamically changing environment.

[0004] Limitations of traditional control methods: Although traditional PI controllers can maintain system stability to a certain extent, they are difficult to respond quickly and optimize efficiently when faced with large-scale load fluctuations, frequent renewable energy fluctuations, and external disturbances. In addition, fixed PI control parameters cannot be automatically adjusted under complex system conditions, resulting in reduced control accuracy and even system instability.

[0005] Multi-objective optimization requirements: The control system of the microgrid needs to take into account multiple objectives: on the one hand, the fluctuation of frequency and voltage needs to be reduced as much as possible; on the other hand, the power quality needs to be ensured, especially the reduction of harmonic distortion. In addition, the system response speed is also an important performance indicator. How to improve the system response speed while ensuring frequency stability and power quality has become a research focus in the field of microgrid control. Summary of the invention

[0006] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide an AC microgrid frequency regulation device using fuzzy logic to solve the above-mentioned technical problems.

[0007] To achieve the above object, the present invention provides the following technical solutions: an AC microgrid frequency regulation device using fuzzy logic, comprising: an initialization module, a data acquisition module, a hierarchical control module and a simulation optimization module;

[0008] The initialization module is used to initialize the reference frequency, reference voltage, reference active power, reference reactive power, droop control coefficient and PI controller parameters;

[0009] The data acquisition module is used to collect the operating parameters of the AC microgrid in real time and eliminate measurement noise through a filtering algorithm;

[0010] The hierarchical control module includes a primary control unit and a secondary control unit, wherein the primary control unit adjusts the output frequency and output voltage according to the active power and reactive power requirements based on a preset droop control strategy, and the secondary control unit finely adjusts the frequency deviation and voltage deviation adjusted by the primary control unit according to a preset fuzzy logic;

[0011] The simulation optimization module is used to construct a microgrid simulation model, perform simulation verification according to the operating parameters of the AC microgrid, and obtain the optimal PI controller parameters by optimizing the preset objective function.

[0012] The present invention is further configured such that the droop control coefficient includes an active power droop control coefficient and a reactive power droop control coefficient, and the PI controller parameters include a frequency proportional gain, a frequency integral gain, a voltage proportional gain and a voltage integral gain.

[0013] The present invention is further configured to adjust the output frequency and output voltage based on a preset droop control strategy according to the active power and reactive power requirements, and the adjustment logic of the output frequency and output voltage is: f-f0=-m(P-P0), U-U0=-n(Q-Q0), wherein f is the output frequency of the inverter, f0 is the reference frequency, m is the active power droop control coefficient, P is the active power, P0 is the reference active power, U is the output voltage of the inverter, U0 is the reference voltage, n is the reactive power droop control coefficient, Q is the reactive power, and Q0 is the reference reactive power.

[0014] The present invention is further configured such that the dynamic adjustment logic of the active power droop control coefficient and the reactive power droop control coefficient includes:

[0015] Calculate the deviation and deviation increment of active power and reactive power at the current moment;

[0016] According to the deviation and deviation increment of the active power and reactive power at the current moment, the active power droop control coefficient and the reactive power droop control coefficient are dynamically adjusted according to the preset coefficient dynamic adjustment logic;

[0017] The dynamically adjusted active power droop control coefficient and reactive power droop control coefficient are transmitted to the primary control unit for updating.

[0018] The present invention is further configured such that the calculation logic of the deviation between active power and reactive power at the current moment is: ΔP = P mg -P0, ΔQ = Q mg -Q0, ΔP is the deviation of active power, P mgis the active power at the current moment, ΔQ is the deviation of reactive power, Q mg is the reactive power at the current moment. The calculation logic of the deviation increment between the active power and reactive power at the current moment is: Δ′P is the deviation increment of active power, Δ′Q is the deviation increment of reactive power;

[0019] The preset coefficient dynamic adjustment logic is: m=m0+α1tanh(β1ΔP)+γ1exp(-κ1|Δ′P|), n=n0+α2tanh(β2ΔQ)+γ2exp(-κ2|Δ′Q|), among which, m0 and n0 are the initial droop control coefficients, α1, β1, γ1 and κ1 are the active power dynamic adjustment parameters, and α2, β2, γ2 and κ2 are the reactive power dynamic adjustment parameters.

[0020] The present invention is further configured to finely adjust the frequency deviation and voltage deviation of the primary control unit according to a preset fuzzy logic, and the adjustment logic is: Δω=k pf (ff mg )+k if ∫(ff mg )dt, ΔE=k pU (UU mg )+k iU ∫(UU mg )dt, where Δω and ΔE are the adjustment values ​​of frequency and voltage, f mg and U mg is the frequency and voltage at the current moment, k pf is the frequency proportional gain, k if is the frequency integral gain, k pU is the voltage proportional gain, k iU is the voltage integral gain.

[0021] The present invention is further configured such that the dynamic adjustment logic of the frequency proportional gain, the frequency integral gain, the voltage proportional gain and the voltage integral gain includes:

[0022] Calculate the deviation value and deviation increment of the frequency and voltage at the current moment;

[0023] According to the deviation value and deviation increment of the frequency and voltage at the current moment, the frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are dynamically adjusted according to the preset gain dynamic adjustment logic;

[0024] The dynamically adjusted frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are transmitted to the secondary control unit for updating.

[0025] The present invention is further configured such that the calculation logic of the deviation value of the frequency and voltage at the current moment is: Δf=ff mg , ΔU=UU mg , Δf is the frequency deviation, ΔU is the voltage deviation, and the calculation logic of the deviation increment of frequency and voltage at the current moment is: Δ′f is the deviation increment of frequency, Δ′U is the deviation increment of voltage;

[0026] The preset gain dynamic adjustment logic is: k pf =k pf0 ·[1+δ f ·tanh(ε f Δf)]·exp(-∈ f |Δ′f|), k if =k if0 ·[1+θ f ·tanh(ρ f Δf)]·[1-exp(-σ f |Δ′f|)],k pu =k pu0 ·[1+δ u ·tanh(ε u ΔU)]·exp(-∈ u |Δ′U|), k iu =k iu0 ·[1+θ u ·tanh(ρ u ΔU)]·[1-exp(-σ u |Δ′U|)], where k pf0 , k if0 , k pu0 and k iu0 is the initial PI controller parameter, δ f , ε f ,∈ f ,θ f , f and σ f is the frequency dynamic adjustment parameter, δ u , ε u ,∈ u ,θ u , u and σ u Dynamically adjust parameters for voltage.

[0027] The present invention is further configured such that the preset objective function is: Among them, k opt is the objective function, k is the combination of PI controller parameters, V nis the voltage amplitude of the nth harmonic in the simulation period T, V1 is the fundamental voltage amplitude, Δf(t) is the frequency deviation at time t, t stabilized is the time when the secondary control unit completes dynamic adjustment, t start is the moment of dynamic adjustment, ω1, ω2 and ω3 are the weighting coefficients.

[0028] The present invention is further configured such that the solution logic of the preset objective function is:

[0029] The format of defining the solution is k i =(k pf ,k if ,k pU ,k iU ), randomly generate an initial solution, and optimize the initial solution iteratively. The iterative logic is: k i+1 =k i +v i , where k i+1 is the i+1th solution, v i is the update speed of the ith solution, v i The update logic is v i+1 is the update speed of the i+1th solution, is the inertia weight, c1 and c2 are learning factors, which are used to control the degree of approach to the individual optimal solution and the global optimal solution, r1 and r2 are random numbers between [0,1], which are used to increase the randomness of the search and avoid premature convergence, pbest ij k i The individual optimal solution position of the jth parameter in , gbest is the global optimal solution position;

[0030] Iterate to the maximum iteration round, and set the PI controller parameters corresponding to the minimum objective function as the PI controller parameters of the secondary control unit.

[0031] The present invention provides an AC microgrid frequency regulation device using fuzzy logic, comprising: an initialization module, a data acquisition module, a hierarchical control module and a simulation optimization module; the initialization module is used to initialize a reference frequency, a reference voltage, a reference active power, a reference reactive power, a droop control coefficient and a PI controller parameter; the data acquisition module is used to collect the operating parameters of the AC microgrid in real time and eliminate measurement noise through a filtering algorithm; the hierarchical control module comprises a primary control unit and a secondary control unit, the primary control unit adjusts the output frequency and output voltage based on a preset droop control strategy and according to active power and reactive power requirements, and the secondary control unit finely adjusts the frequency deviation and voltage deviation adjusted by the primary control unit according to the preset fuzzy logic; the simulation optimization module is used to construct a microgrid simulation model, perform simulation verification according to the operating parameters of the AC microgrid, and obtain the optimal PI controller parameters by optimizing the preset objective function, and the beneficial effects produced include:

[0032] 1. Improve the stability of the microgrid: By adopting fuzzy logic control and objective function optimization algorithm, the parameters of the PI controller can be dynamically adjusted according to the real-time frequency and voltage deviation of the microgrid, so as to effectively balance the active power and reactive power in the system and reduce the frequency and voltage fluctuations caused by load fluctuations and fluctuations in renewable energy output. Especially in the case of sudden load changes or unstable power generation, the system parameters can be quickly adjusted to maintain the stability of frequency and voltage;

[0033] 2. Optimize power quality: By optimizing controller parameters, the total harmonic distortion rate can be effectively reduced and the power quality can be improved. In microgrids, due to the nonlinear characteristics of devices such as inverters, harmonics are often generated, affecting the voltage waveform and the operation of the equipment. The present invention can reduce the harmonic content through precise frequency and voltage control, thereby improving power quality, reducing equipment damage and improving system efficiency;

[0034] 3. Realize multi-objective optimization: Optimize frequency fluctuation, total harmonic distortion and response time simultaneously through the objective function optimization algorithm. In the operation of the microgrid, frequency fluctuation, power quality and response speed are interrelated optimization targets. Traditional control methods can usually only focus on one of the targets, while the present invention uses a multi-objective optimization algorithm to balance the relationship between multiple targets while taking into account multiple targets, ensuring that the system can operate more stably and efficiently under complex working conditions.

[0035] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0037] Figure 1 The present invention is a schematic structural diagram of an AC microgrid frequency regulation device using fuzzy logic according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0039] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0040] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0041] An AC microgrid frequency regulation device using fuzzy logic, such as Figure 1 As shown, including:

[0042] Initialization module, data acquisition module, hierarchical control module and simulation optimization module;

[0043] The initialization module is used to initialize the reference frequency, reference voltage, reference active power, reference reactive power, droop control coefficient and PI controller parameters;

[0044] The data acquisition module is used to collect the operating parameters of the AC microgrid in real time and eliminate measurement noise through a filtering algorithm;

[0045] The hierarchical control module includes a primary control unit and a secondary control unit, wherein the primary control unit adjusts the output frequency and output voltage according to the active power and reactive power requirements based on a preset droop control strategy, and the secondary control unit finely adjusts the frequency deviation and voltage deviation adjusted by the primary control unit according to a preset fuzzy logic;

[0046] The simulation optimization module is used to construct a microgrid simulation model, perform simulation verification according to the operating parameters of the AC microgrid, and obtain the optimal PI controller parameters by optimizing the preset objective function.

[0047] Specifically, the initialization module provides basic initial parameters for the frequency regulation and voltage control process of the entire system, and is responsible for setting all necessary initial conditions for the system, including reference frequency, reference voltage, reference active power, reference reactive power, droop control coefficient and PI controller parameters. The initialization process ensures that the system has a reasonable starting point at the beginning of operation, so that it can be dynamically adjusted according to different operating environments and requirements in actual operation. The reference frequency is the target frequency of the system when it is running stably, usually taking the standard frequency when the system is connected to the grid; the reference voltage is the target voltage value of the microgrid when it is running stably, usually taking the grid voltage standard when the system is connected to the grid; the reference active power refers to the target power output in the stable state of the system, which is usually determined by the load demand and renewable energy generation capacity, and is preset according to the load and generation capacity of the microgrid; the reference reactive power refers to the amount of reactive power required for voltage regulation in the microgrid system, which is usually determined by the reactive load demand and power factor requirements. It is set according to the voltage level and load conditions of the power grid; the droop control coefficient is a parameter used to adjust the relationship between active power and reactive power; the proportional and integral gain parameters of the PI controller are the core parameters of the control system to adjust the frequency and voltage, and fine control is achieved by adjusting the system's response to frequency and voltage deviations; the present invention is further configured such that the droop control coefficient includes an active power droop control coefficient and a reactive power droop control coefficient, and the PI controller parameters include a frequency proportional gain, a frequency integral gain, a voltage proportional gain, and a voltage integral gain. The active power droop control coefficient describes the effect of active power deviation on frequency in a microgrid. That is, how the frequency is adjusted with changes in active power when the load fluctuates or the power changes. This coefficient enables the system to automatically adjust the frequency according to power changes by defining a linear relationship between frequency and active power, and the reactive power droop control coefficient describes the effect of reactive power deviation on voltage in a microgrid. Changes in reactive power directly affect the voltage level of the system, so the voltage response is adjusted by adjusting the reactive power droop control coefficient.

[0048] The present invention is further configured to adjust the output frequency and output voltage based on the preset droop control strategy according to the active power and reactive power requirements, and the adjustment logic of the output frequency and output voltage is: f-f0=-m(P-P0), U-U0=-n(Q-Q0), wherein f is the output frequency of the inverter, f0 is the reference frequency, m is the active power droop control coefficient, P is the active power, P0 is the reference active power, U is the output voltage of the inverter, U0 is the reference voltage, n is the reactive power droop control coefficient, Q is the reactive power, and Q0 is the reference reactive power. Specifically, the above logic is based on the droop control strategy, and the output frequency and voltage of the inverter are adjusted by dynamically monitoring the active power and reactive power requirements. This adjustment mechanism ensures that the microgrid can maintain the stability of the system when the load changes or the output of renewable energy fluctuates by setting appropriate droop control coefficients and reference power values. The basic logic of the droop control strategy is: according to the deviation between the real-time active power P and reactive power Q and the reference power P0, Q0, the frequency and voltage are automatically adjusted. Active power droop control: the output frequency is adjusted by the active power deviation P-P0 to ensure the balance between frequency and power demand; reactive power droop control: the output voltage is adjusted by the reactive power deviation Q-Q0 to ensure the balance between voltage and reactive power demand. Through the droop control of active power and reactive power, the microgrid can automatically adjust the frequency and voltage to maintain the stability of the system when the load fluctuates and the power generation is unstable.

[0049] The present invention is further configured such that the dynamic adjustment logic of the active power droop control coefficient and the reactive power droop control coefficient includes:

[0050] Calculate the deviation and deviation increment of active power and reactive power at the current moment; the present invention is further configured that the calculation logic of the deviation of active power and reactive power at the current moment is: ΔP = P mg -P0, ΔQ = Q mg -Q0, ΔP is the deviation of active power, P mg is the active power at the current moment, ΔQ is the deviation of reactive power, Q mg is the reactive power at the current moment. The calculation logic of the deviation increment between the active power and reactive power at the current moment is: Δ′P is the deviation increment of active power, and Δ′Q is the deviation increment of reactive power; specifically, the active power deviation ΔP is the difference between the actual active power at the current moment and the reference active power; the reactive power deviation ΔQ is the difference between the actual reactive power at the current moment and the reference reactive power; the active power deviation increment Δ′P is the change between the active power deviation at the current moment and the active power deviation at the previous moment; the reactive power deviation increment Δ′Q is the change between the reactive power deviation at the current moment and the reactive power deviation at the previous moment;

[0051] According to the deviation and deviation increment of active power and reactive power at the current moment, the active power droop control coefficient and the reactive power droop control coefficient are dynamically adjusted according to the preset coefficient dynamic adjustment logic; the preset coefficient dynamic adjustment logic is: m=m0+α1tanh(β1ΔP)+γ1exp(-κ1|Δ′P|), n=n0+α2tanh(β2ΔQ)+γ2exp(-κ2|Δ′Q|), wherein m0 and n0 are initial droop control coefficients, α1, β1, γ1 and κ1 are active power dynamic adjustment parameters, and α2, β2, γ2 and κ2 are reactive power dynamic adjustment parameters; specifically, according to the deviation and deviation increment of active and reactive power, the preset dynamic adjustment logic is used to adjust the active power droop control coefficient and the reactive power droop control coefficient, and the system demand is dynamically responded to by increasing or decreasing the control coefficient to optimize the stability of frequency and voltage; the active power dynamic adjustment parameter and the reactive power dynamic adjustment parameter are set according to the stability requirement and regulation accuracy of the system. Larger parameter values ​​can make the system more sensitive to power changes, but may cause system oscillations; smaller values ​​are conducive to smooth regulation, and the value range is [0.01, 2.0]. By dynamically adjusting the droop control coefficient and PI controller parameters, the present invention can quickly respond to changes in active and reactive power and ensure the stability of the frequency and voltage of the microgrid, especially in the case of load changes and fluctuations in renewable energy.

[0052] The dynamically adjusted active power droop control coefficient and reactive power droop control coefficient are transmitted to the primary control unit for updating.

[0053] The present invention is further configured to finely adjust the frequency deviation and voltage deviation of the primary control unit according to a preset fuzzy logic, and the adjustment logic is: Δω=k pf (ff mg )+k if ∫(ff mg )dt, ΔE=k pU (UU mg )+k iU ∫(UU mg )dt, where Δω and ΔE are the adjustment values ​​of frequency and voltage, f mg and U mg is the frequency and voltage at the current moment, k pf is the frequency proportional gain, k if is the frequency integral gain, k pU is the voltage proportional gain, k iUis the voltage integral gain. Specifically, by further introducing the preset fuzzy logic, the frequency deviation and voltage deviation adjusted by the primary control unit are finely adjusted. Through the application of fuzzy logic, the frequency and voltage can be adjusted more accurately to adapt to various changes in the operation of the microgrid;

[0054] The present invention is further configured such that the dynamic adjustment logic of the frequency proportional gain, the frequency integral gain, the voltage proportional gain and the voltage integral gain includes:

[0055] Calculate the deviation value and deviation increment of the frequency and voltage at the current moment; the present invention is further configured such that the calculation logic of the deviation value of the frequency and voltage at the current moment is: Δf=ff mg , ΔU=UU mg , Δf is the frequency deviation, ΔU is the voltage deviation, and the calculation logic of the deviation increment of frequency and voltage at the current moment is: Δ′f is the frequency deviation increment, and Δ′U is the voltage deviation increment; specifically, the frequency deviation refers to the difference between the actual frequency and the reference frequency at the current moment. The frequency deviation reflects the deviation between the microgrid operating state and the target frequency; the voltage deviation refers to the difference between the actual voltage and the reference voltage at the current moment. The voltage deviation reflects the deviation between the microgrid voltage level and the target voltage. The deviation increment refers to the change between the frequency or voltage deviation at the current moment and the deviation at the previous moment. It indicates the rate of system frequency and voltage fluctuations;

[0056] According to the deviation value and deviation increment of the frequency and voltage at the current moment, the frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are dynamically adjusted according to the preset gain dynamic adjustment logic; the preset gain dynamic adjustment logic is: k pf =k pf0 ·[1+δ f ·tanh(ε f Δf)]·exp(-∈ f |Δ′f|), k if =k if0 ·[1+θ f ·tanh(ρ f Δf)]·[1-exp(-σ f |Δ′f|)],k pu =k pu0 ·[1+δ u ·tanh(ε u ΔU)]·exp(-∈ u |Δ′U|), k iu =k iu0 ·[1+θ u ·tanh(ρ u ΔU)]·[1-exp(-σu |Δ′U|)], where k pf0 , k if0 , k pu0 and k iu0 is the initial PI controller parameter, δ f , ε f ,∈ f ,θ f , f and σ f is the frequency dynamic adjustment parameter, δ u , ε u ,∈ u ,θ u , u and σ u Dynamically adjust the voltage parameters; based on the current frequency and voltage deviation values ​​and their increments, use the preset dynamic gain adjustment logic to adjust the frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain of the PI controller. According to the current frequency and voltage deviations and their change increments, dynamically adjust the controller gain so that the system can respond quickly to changes in frequency and voltage, ensuring that the microgrid can operate stably in dynamic environments such as load fluctuations and changes in renewable energy.

[0057] The dynamically adjusted frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are transmitted to the secondary control unit for updating.

[0058] The present invention is further configured such that the preset objective function is: Among them, k opt is the objective function, k is the combination of PI controller parameters, V n is the voltage amplitude of the nth harmonic in the simulation period T, V1 is the fundamental voltage amplitude, Δf(t) is the frequency deviation at time t, t stabilized is the time when the secondary control unit completes dynamic adjustment, t start is the moment of dynamic adjustment, and ω1, ω2 and ω3 are trade-off coefficients. Specifically, the objective function is a comprehensive performance indicator used to measure the operation quality of the microgrid system. The objective function is based on a weighted combination of multiple factors, including frequency deviation, voltage amplitude, and harmonic distortion; the objective function contains a combination of PI controller parameters, which means that the stability and response speed of the system are improved by optimizing the proportional gain and integral gain of the PI controller. Multiple trade-off coefficients ω1, ω2 and ω3 are introduced in the objective function to balance the relative importance of each control objective. By adjusting these coefficients, certain indicators can be optimized based on the actual needs of the system.

[0059] The present invention is further configured such that the solution logic of the preset objective function is: the format of defining the solution is k i =(k pf ,k if ,k pU ,k iU ), randomly generate an initial solution, and optimize the initial solution iteratively. The iterative logic is: k i+1 =k i +v i , where k i+1 is the i+1th solution, v i is the update speed of the ith solution, v i The update logic is v i+1 is the update speed of the i+1th solution, is the inertia weight, c1 and c2 are learning factors, which are used to control the degree of approach to the individual optimal solution and the global optimal solution, r1 and r2 are random numbers between [0,1], which are used to increase the randomness of the search and avoid premature convergence, pbest ij k i The individual optimal solution position of the jth parameter in , gbest is the global optimal solution position;

[0060] Iterate to the maximum iteration round, and set the PI controller parameters corresponding to the minimum objective function as the PI controller parameters of the secondary control unit. Specifically, the PI controller parameters of the microgrid are precisely adjusted by optimizing the objective function. The objective function optimization algorithm is used to optimize and iterate the parameters of the PI controller, and finally obtain the parameter configuration that minimizes the objective function, so as to achieve precise regulation of the frequency and voltage of the microgrid. The solution format is a combination of PI controller parameters (such as frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain). The initial solution is a randomly generated PI controller parameter, which is used as the starting point for optimization; each solution is iteratively updated by the objective function optimization algorithm. Each solution has an update speed, and the position of the solution is updated according to the speed to optimize the objective function. Each solution updates its position according to the inertia weight, the position of the individual optimal solution and the position of the global optimal solution. The learning factor is used to control the degree of closeness of the solution to the individual optimal solution and the global optimal solution, avoiding premature convergence and enhancing the diversity of the search; the inertia weight determines the degree of dependence on the current update speed. Usually, a larger weight at the beginning is conducive to global search, and gradually decreases as the iteration proceeds, with a value range of [0.4, 0.9]. The learning factor determines the degree of dependence of the particle on the individual optimal solution and the global optimal solution. A larger learning factor helps the particle converge to the optimal solution, with a value range of 1.5≤c1, c2≤2.5. Random numbers are used to increase the diversity of the search space and prevent premature convergence. Usually, random numbers are regenerated at each iteration, with a value range of 0≤r1, r2≤1. Through the particle swarm optimization algorithm, the system can accurately optimize the gain parameters of the PI controller, thereby finely adjusting the frequency and voltage of the microgrid to ensure efficient and stable operation of the system.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0062] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0063] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0064] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0067] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0068] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0070] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0071] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An AC microgrid frequency regulation device using fuzzy logic, characterized in that: include: Initialization module, data acquisition module, hierarchical control module and simulation optimization module; The initialization module is used to initialize the reference frequency, reference voltage, reference active power, reference reactive power, droop control coefficient and PI controller parameters; The data acquisition module is used to collect the operating parameters of the AC microgrid in real time and eliminate measurement noise through a filtering algorithm; The hierarchical control module includes a primary control unit and a secondary control unit, wherein the primary control unit adjusts the output frequency and output voltage according to the active power and reactive power requirements based on a preset droop control strategy, and the secondary control unit finely adjusts the frequency deviation and voltage deviation adjusted by the primary control unit according to a preset fuzzy logic; The simulation optimization module is used to construct a microgrid simulation model, perform simulation verification according to the operating parameters of the AC microgrid, and obtain the optimal PI controller parameters by optimizing the preset objective function.

2. The AC microgrid frequency regulation device using fuzzy logic according to claim 1, characterized in that: The droop control coefficient includes an active power droop control coefficient and a reactive power droop control coefficient, and the PI controller parameters include a frequency proportional gain, a frequency integral gain, a voltage proportional gain and a voltage integral gain.

3. The AC microgrid frequency regulation device using fuzzy logic according to claim 2, characterized in that: Based on the preset droop control strategy, the output frequency and output voltage are adjusted according to the active power and reactive power requirements. The adjustment logic of the output frequency and output voltage is: f-f0=-m(P-P0), U-U0=-n(Q-Q0), wherein f is the output frequency of the inverter, f0 is the reference frequency, m is the active power droop control coefficient, P is the active power, P0 is the reference active power, U is the output voltage of the inverter, U0 is the reference voltage, n is the reactive power droop control coefficient, Q is the reactive power, and Q0 is the reference reactive power.

4. The AC microgrid frequency regulation device using fuzzy logic according to claim 3, characterized in that: The dynamic adjustment logic of the active power droop control coefficient and the reactive power droop control coefficient includes: Calculate the deviation and deviation increment of active power and reactive power at the current moment; According to the deviation and deviation increment of the active power and reactive power at the current moment, the active power droop control coefficient and the reactive power droop control coefficient are dynamically adjusted according to the preset coefficient dynamic adjustment logic; The dynamically adjusted active power droop control coefficient and reactive power droop control coefficient are transmitted to the primary control unit for updating.

5. The AC microgrid frequency regulation device using fuzzy logic according to claim 4, characterized in that: The calculation logic of the deviation between active power and reactive power at the current moment is: ΔP = P mg -P0, ΔQ = Q mg -Q0, ΔP is the deviation of active power, P mg is the active power at the current moment, ΔQ is the deviation of reactive power, Q mg is the reactive power at the current moment. The calculation logic of the deviation increment between the active power and reactive power at the current moment is: Δ′P is the deviation increment of active power, Δ′Q is the deviation increment of reactive power; The preset coefficient dynamic adjustment logic is: m=m0+α1tanh(β1ΔP)+γ1exp(-κ1|Δ′P|), n=n0+α2tanh(β2ΔQ)+γ2exp(-κ2|Δ′Q|), among which, m0 and n0 are the initial droop control coefficients, α1, β1, γ1 and κ1 are the active power dynamic adjustment parameters, and α2, β2, γ2 and κ2 are the reactive power dynamic adjustment parameters.

6. The AC microgrid frequency regulation device using fuzzy logic according to claim 3, characterized in that: The frequency deviation and voltage deviation of the primary control unit are finely adjusted according to the preset fuzzy logic, and the adjustment logic is: Δω=k pf (ff mg )+k if ∫(ff mg )dt, ΔE=k pU (UU mg )+k iU ∫(UU mg )dt, where Δω and ΔE are the adjustment values ​​of frequency and voltage, f mg and U mg is the frequency and voltage at the current moment, k pf is the frequency proportional gain, k if is the frequency integral gain, k pU is the voltage proportional gain, k iU is the voltage integral gain.

7. The AC microgrid frequency regulation device using fuzzy logic according to claim 6, characterized in that: The dynamic adjustment logic of frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain includes: Calculate the deviation value and deviation increment of the frequency and voltage at the current moment; According to the deviation value and deviation increment of the frequency and voltage at the current moment, the frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are dynamically adjusted according to the preset gain dynamic adjustment logic; The dynamically adjusted frequency proportional gain, frequency integral gain, voltage proportional gain and voltage integral gain are transmitted to the secondary control unit for updating.

8. The AC microgrid frequency regulation device using fuzzy logic according to claim 7, characterized in that: The calculation logic of the current frequency and voltage deviation is: Δf = ff mg , ΔU=UU mg , Δf is the frequency deviation, ΔU is the voltage deviation, and the calculation logic of the deviation increment of frequency and voltage at the current moment is: Δ′f is the deviation increment of frequency, Δ′U is the deviation increment of voltage; The preset gain dynamic adjustment logic is: k pf =k pf0 ·[1+δ f ·tanh(ε f Δf)]·exp(-∈ f |Δ′f|), k if =k if0 ·[1+θ f ·tanh(ρ f Δf)]·[1-exp(-σ f |Δ′f|)],k pu =k pu0 ·[1+δ u ·tanh(ε u ΔU)]·exp(-∈ u |Δ′U|), k iu =k iu0 ·[1+θ u ·tanh(ρ u ΔU)]·[1-exp(-σ u |Δ′U|)], where k pf0 , k if0 , k pu0 and k iu0 is the initial PI controller parameter, δ f , ε f ,∈ f ,θ f , f and σ f is the frequency dynamic adjustment parameter, δ u , ε u ,∈ u ,θ u , u and σ u Dynamically adjust parameters for voltage.

9. The AC microgrid frequency regulation device using fuzzy logic according to claim 2, characterized in that: The preset objective function is: Among them, k opt is the objective function, k is the combination of PI controller parameters, V n is the voltage amplitude of the nth harmonic in the simulation period T, V1 is the fundamental voltage amplitude, Δf(t) is the frequency deviation at time t, t stabilized is the time when the secondary control unit completes dynamic adjustment, t start is the moment of dynamic adjustment, ω1, ω2 and ω3 are the weighting coefficients.

10. The AC microgrid frequency regulation device using fuzzy logic according to claim 9, characterized in that: The solution logic of the preset objective function is: The format of defining the solution is k i =(k pf ,k if ,k pU ,k iU ), randomly generate an initial solution, and optimize the initial solution iteratively. The iterative logic is: k i+1 =k i +v i , where k i+1 is the i+1th solution, v i is the update speed of the ith solution, v i The update logic is v i+1 is the update speed of the i+1th solution, is the inertia weight, c1 and c2 are learning factors, which are used to control the degree of approach to the individual optimal solution and the global optimal solution, r1 and r2 are random numbers between [0,1], which are used to increase the randomness of the search and avoid premature convergence, pbest ij k i The individual optimal solution position of the jth parameter in , gbest is the global optimal solution position; Iterate to the maximum iteration round, and set the PI controller parameters corresponding to the minimum objective function as the PI controller parameters of the secondary control unit.

Images