Method for designing network construction-network following type inverter based on artificial bee colony optimization fractional order controller

Abstract

The invention relates to a network construction-network following type inverter design method based on an artificial bee colony optimization fractional order controller. The method comprises the following steps: constructing a basic model of an inverter; based on the basic model, utilizing a Monte Carlo simulation method to obtain an optimal boundary of inverter parameters; and based on an artificial bee colony optimization algorithm, optimizing the optimal boundary of the inverter parameters to obtain optimal inverter parameters. According to the invention, Monte Carlo simulation and an artificial bee colony heuristic algorithm are simultaneously utilized to optimize controller parameters, so that the purpose of improving the performance of the inverter to the greatest extent is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter design, and in particular to a design method of a grid-building and grid-following inverter based on an artificial bee colony optimized fractional-order controller. Background Art

[0002] Energy is the basic driving force of human development. In order to achieve "low-carbon, clean and sustainable" development, humans are currently undergoing energy transformation. Under the "dual carbon" goal, the low-carbon transformation of energy will be significantly accelerated. The energy transformation mainly relies on expanding the proportion of clean energy, low-carbon energy and zero-carbon energy, and promoting the realization of energy production and consumption centered on electricity.

[0003] In the power system with high penetration of new energy, the traditional grid dominated by synchronous generators is changing. The proportion of power electronic inverters in the power system is increasing. Large power systems are gradually separated into multiple microgrid systems, and inverters are gradually replacing synchronous generators. Inverters have fast response speed, are easy to control, and can propose multiple strategies to solve grid operation problems.

[0004] In the context of large-scale grid connection of new energy, inverter control technology is the key to grid-connected energy storage. There are two main control technologies for inverters, namely grid-following control technology and grid-connected control technology. At present, grid-connected energy storage inverters usually adopt grid-following control technology. With the access of more and more new energy and power electronic equipment, the inertia of the power system is reduced, the system strength is weakened, and the stability problem is becoming more and more serious. Grid-connected inverters are gradually favored.

[0005] In most cases, grid-connected and grid-following inverters are designed and operated separately. However, in modern times, it is increasingly necessary for a single inverter to be both grid-connected and grid-following, depending on conditions or system requirements. For example, an isolated factory has only one inverter. When the load demand is large, the grid-connected operation mode is generally adopted. In the event of a grid failure or disconnection, the factory will change the operation mode to grid-connected to provide basic load. Therefore, a single inverter needs to change its operation mode according to the situation.

[0006] At present, the use of both grid-building and grid-following control technologies in a single inverter is rare, and the design of its inverter is mostly based on traditional PI controllers. However, when using traditional PI controllers, there are problems such as small control freedom, insufficient control precision, and low robustness, which limits the performance of the designed grid-building and grid-following inverters.

[0007] At the same time, in the existing grid-building and grid-following inverter design process, the controller parameter setting method is not discussed. The performance of the controller depends on the parameter setting to a large extent. Ignoring this issue will greatly limit the performance of the designed inverter. Summary of the invention

[0008] The purpose of the present invention is to propose a design method for a grid-building and grid-following inverter based on an artificial bee colony optimized fractional-order controller to solve the problems existing in the above-mentioned prior art, and at the same time use Monte Carlo simulation and artificial bee colony heuristic algorithm to optimize controller parameters to achieve the purpose of maximizing the inverter performance.

[0009] To achieve the above object, the present invention provides the following solutions:

[0010] A design method for a grid-building and grid-following inverter based on an artificial bee colony optimized fractional-order controller, comprising:

[0011] Construct a basic model of the inverter;

[0012] Based on the basic model, using the Monte Carlo simulation method, the optimal boundaries of the inverter parameters are obtained;

[0013] Based on an artificial bee colony optimization algorithm, the optimal boundaries of the inverter parameters are optimized to obtain optimal inverter parameters.

[0014] Optionally, constructing a basic model of the inverter includes:

[0015] S1. Determine the system composition and establish the equation; the system composition equation is:

[0016]

[0017] Where R and L are the series resistance and inductance between the inverter terminal and the grid connection point or the common axis point. L is the inductor current. V g , V inv are the grid voltage and inverter voltage respectively, di L Indicates the inductor current i L dl is the small change of time;

[0018] S2. Voltage and inductor current unit conversion:

[0019]

[0020] Among them, V g (pu),i L (pu) is the normalized value of the measured voltage and current, V g phase to ground is the grid phase grounding voltage value, V g base is the grid reference voltage value, i L base is the inductor reference current value, iL abc is the current value of the specific phase of the inductor;

[0021] S3. Convert the normalized values ​​of the measured voltage and current into dq quantities:

[0022]

[0023] Among them, x d , x q Represents a vector in dq mode, x a , x b , x c is a vector in the abc coordinate system, and θ is the phase angle, which can be measured using a standard phase-locked loop (PLL) circuit;

[0024] S4. Select first-order and second-order low-pass filters:

[0025]

[0026] Where H(S) is the transfer function, s is the complex frequency domain variable, T is the system time constant, which indicates the response speed of the system to input changes, ω n is the natural frequency of the system, ζ is the damping ratio of the system;

[0027] S5. Establish the decoupling equation:

[0028]

[0029] Among them, V di is the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, K p is the proportional gain, K i is the integral gain, S is the synchronous speed, l is the inductance in the system, λ l is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, i dg is the actual d-axis voltage, i qg is the actual q-axis voltage, ω g is the grid angular frequency, L g is the generator inductance, V dg is the d-axis voltage, V qg is the q-axis voltage, R g To control the circuit resistance;

[0030] S6. In the network-following mode, the relevant quantity ω in the decoupling equation g Convert to unit quantity:

[0031]

[0032] Among them, ω g is the grid angular frequency, V rate is the effective value of the grid voltage, V grid is the amplitude of the grid voltage, I rate is the effective value of the current in the power grid, P rateESS is the power of the energy storage system, ω LESS is the grid angular frequency after conversion to unit quantity, f g is the frequency of the electric energy storage system;

[0033] S7. Establish the equation in the network mode:

[0034]

[0035] Among them, V di is the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, V is the motor line voltage, K p is the proportional gain, used to adjust the proportional control, K i is the integral gain, which is used to adjust the integral control, S is the synchronous speed, λ is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, V dESS is the d-axis voltage of the energy storage system, V qESS is the q-axis voltage of the electric energy storage system;

[0036] S8. Use a virtual phase-locked loop to generate the desired frequency and convert it into abc quantities to obtain the desired voltage of the inverter, forming a basic model of the inverter.

[0037] Optionally, obtaining the optimal boundary of the inverter parameter includes:

[0038] The objective function is preset and the Monte Carlo simulation method is used to simulate the search space of the initial feasible solution of the controller design parameters;

[0039] The objective function is:

[0040]

[0041] Among them, Error(ITAE) represents the objective function, t represents the time variable, P error Represents the active power error, Q error Represents reactive power error, V derror Represents the d-axis error of voltage, V qerror Represents the q-axis error of the voltage.

[0042] Optionally, optimizing the optimal boundary of the inverter parameter based on an artificial bee colony optimization algorithm includes:

[0043] Step 1: Based on the optimal boundary of the inverter parameters, the algorithm parameters are initialized, and an initial honey source is generated using a first preset formula; wherein the initial honey source refers to an initial feasible solution, that is, the initial value of the inverter parameters;

[0044] Step 2: Use the second preset formula to update the initial nectar source, obtain a new nectar source, and use a greedy algorithm to compare the fitness values ​​of the new and old nectar sources to select the optimal value;

[0045] Step 3: For the nectar source with the best value, a roulette strategy is used to select it, and the second preset formula is used again to find a new nectar source, and the best value is saved;

[0046] Step 4: If a scout bee appears, regenerate the initial position and perform update selection, otherwise execute step 5;

[0047] Step 5: If the number of iterations is less than the preset number of iterations, repeat steps 2 to 4; otherwise, output the optimal solution.

[0048] Optionally, the first preset formula is:

[0049] x ij =x minj +rand[0,1](x maxj -x minj )

[0050] Among them, X ij represents the i-th nectar source X i The j-th dimension value, X minj and X maxj Respectively represent the minimum and maximum values ​​of the j-th dimension;

[0051] The second preset formula is:

[0052]

[0053] Among them, v ij represents the j-th latitude value of the i-th individual of the updated new nectar source, Represents a random number between [-1,1], used to control the step size and direction of the update, X k Indicates the neighborhood nectar source.

[0054] The beneficial effects of the present invention are:

[0055] The present invention simultaneously utilizes Monte Carlo simulation and artificial bee colony heuristic algorithm to optimize controller parameters, so as to maximize the performance of the inverter.

[0056] The present invention uses a fractional-order PI controller to enable a single inverter to adapt to both the grid-building and grid-following control modes. This has great advantages for some special scenarios, such as an isolated factory with only one inverter. The inverter can flexibly switch the control mode according to the actual load demand and grid status (grid-connected or islanded), provide basic load and ensure the normal operation of the system, which improves the applicability and flexibility of the inverter.

[0057] At the same time, due to the use of advanced algorithms to optimize controller parameters, the inverter can better adapt to different system configurations, including different grid voltages, frequencies, load characteristics, and ESS (electric energy storage system) capacities, etc. For example, in different simulation scenarios, facing different load level changes and grid condition changes, the controller can show satisfactory performance.

[0058] The Monte Carlo simulation method used in the present invention helps to find a more comprehensive feasible solution search space, avoiding the problem of falling into a local optimal solution that may occur in traditional methods. By selecting a large number of evenly distributed samples, the optimal boundaries of the controller parameters can be determined more accurately, providing better initial conditions for subsequent optimization algorithms.

[0059] The artificial bee colony heuristic algorithm used in the present invention has efficient optimization capabilities. It can quickly find the optimal solution in the search space through the collaborative mechanism of employed bees, follower bees and explorer bees. Compared with some other algorithms, this algorithm can converge to a better solution in a shorter time when dealing with complex inverter parameter optimization problems, thereby improving the optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative labor.

[0061] Figure 1 A schematic flow chart of a design method for a grid-building and grid-following inverter based on an artificial bee colony optimized fractional-order controller according to an embodiment of the present invention;

[0062] Figure 2 is a schematic diagram of a basic control structure of an inverter according to an embodiment of the present invention;

[0063] Figure 3 is a schematic diagram of an inverter model according to an embodiment of the present invention;

[0064] Figure 4 is a schematic diagram of a controller circuit according to an embodiment of the present invention;

[0065] Figure 5 It is the SIMULINK main model structure and parameter diagram based on the OPAL-RT system of the embodiment of the present invention;

[0066] Figure 6 Detailed information diagram of modules related to power grid connection and mode switching based on the OPAL-RT system according to an embodiment of the present invention;

[0067] Figure 7 This is a key indicator monitoring diagram based on the OPAL-RT system test process according to an embodiment of the present invention;

[0068] Figure 8 This is a frequency performance diagram of the ABC-based FOPI controller in scenario 1 according to an embodiment of the present invention;

[0069] Fig. 9 is a d-axis voltage performance diagram of the ABC-based FOPI controller in scenario 1 according to an embodiment of the present invention;

[0070] Fig.10 The q-axis voltage performance diagram of the FOPI controller based on ABC in scenario 1 according to an embodiment of the present invention is shown in FIG.

[0071] Fig.11 This is an actual power performance diagram of the ABC-based FOPI controller in scenario 1 according to an embodiment of the present invention;

[0072] Fig.12 The reactive power performance diagram of the FOPI controller based on ABC in scenario 1 according to an embodiment of the present invention is shown in FIG.

[0073] Fig.13 This is a frequency performance diagram of the ABC-based FOPI controller in scenario 2 according to an embodiment of the present invention;

[0074] Fig.14 is a d-axis voltage performance diagram of the ABC-based FOPI controller in scenario 2 according to an embodiment of the present invention;

[0075] Fig.15 The q-axis voltage performance diagram of the FOPI controller based on ABC in scenario 2 according to an embodiment of the present invention is shown in FIG.

[0076] Fig.16 The actual power performance diagram of the ABC-based FOPI controller in scenario 2 according to an embodiment of the present invention;

[0077] Fig.17 It is a reactive power performance diagram of the FOPI controller based on ABC in scenario 1 according to an embodiment of the present invention;

[0078] Fig.18ABC-based FOPI controller frequency performance diagram in scenario 3 and scenario 4 according to an embodiment of the present invention;

[0079] Fig.19 d-axis voltage performance diagram of the ABC-based FOPI controller in scenarios 3 and 4 according to an embodiment of the present invention;

[0080] Fig. 20 q-axis voltage performance diagram of the ABC-based FOPI controller in scenarios 3 and 4 according to an embodiment of the present invention;

[0081] Fig.21 The actual power performance diagram of the ABC-based FOPI controller in scenario 3 and scenario 4 according to an embodiment of the present invention;

[0082] Fig. 22 1 is a reactive power performance diagram of the ABC-based FOPI controller in scenarios 3 and 4 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0085] This embodiment proposes a design method for a grid-building and grid-following inverter based on an artificial bee colony optimized fractional-order controller, including:

[0086] Construct a basic model of the inverter;

[0087] Based on the basic model, using the Monte Carlo simulation method, the optimal boundaries of the inverter parameters are obtained;

[0088] Based on an artificial bee colony optimization algorithm, the optimal boundaries of the inverter parameters are optimized to obtain optimal inverter parameters.

[0089] Specifically, in this embodiment, Monte Carlo simulation and artificial bee colony heuristic algorithm are used simultaneously to optimize controller parameters to maximize the performance of the inverter, such as Figure 1As shown in the figure, the proposed method can be mainly divided into the modeling stage, the Monte Carlo simulation stage, the heuristic algorithm optimization stage and the real-time verification stage, which respectively include: using the fractional-order PI (FOPI) controller to design the inverter, and establishing the basic model according to the control circuit and constraint equations. Using Monte Carlo simulation, the initial feasible solution search space is determined for the heuristic algorithm. The basic parameters of the controller are optimized using the artificial bee colony optimization algorithm to improve the controller performance. The simulation model is developed in the SIMULINK environment, and the simulation scenario is designed to verify the feasibility of the proposed method and examine its performance.

[0090] Furthermore, the basic model of the inverter is constructed including:

[0091] Step 1: Determine the system composition and establish the basic equation. Specifically, in this embodiment, in order to develop the optimal grid-building and grid-following inverter, the basic model is established using the following steps. The basic control structure of the inverter is as follows: Figure 2 As shown, the design model diagram is as follows Figure 3 As shown. The system considered consists of a balanced series RL system of inverter and grid-connected terminal. It can be described by the following equation:

[0092]

[0093] Where R and L are the series resistance and inductance between the inverter terminal and the grid connection point or the common axis point. L is the inductor current. V g , V inv They are grid voltage and inverter voltage respectively. The measured voltage and current are three-phase quantities. The voltage measurement method is selected for ground measurement.

[0094] Step 2: Voltage and inductor current unit conversion. To improve the robustness of the inverter and enable the inverter to work effectively regardless of the capacity of the distributed generator / ESS, the voltage and inductor current in step 1 should be normalized. The unit conversion of voltage and current can be completed using the following formula:

[0095]

[0096] Where V g (pu),i L (pu) is the normalized value of the measured voltage and current, V g phase to ground is the grid phase grounding voltage value, V g base is the grid reference voltage value, i L base is the inductor reference current value, i L abc It is the current value of a specific phase (abc phase) of the inductor.

[0097] and,

[0098]

[0099] Among them, V gnom is the rated value of the grid voltage, V grms is the effective value of the actual voltage of the power grid, P baseESS is the system base power value.

[0100] Step 3: Convert the normalized values ​​of measured voltage and current into dq quantities. The proposed inverter works in dq mode. Therefore, it is necessary to convert the measured normalized values ​​of voltage and current into dq quantities:

[0101]

[0102] In the above formula, x d , x q Represents a vector in dq mode, x a , x b , x c is a vector in the abc coordinate system. θ can be measured using a standard phase-locked loop (PLL) circuit. In addition, before converting the three-phase quantity to dq quantity, the measured variable should pass through a low-pass filter circuit because the measured quantity may have high-frequency components and may cause a difficult task in controller development.

[0103] Step 4: Select a low-pass filter. In this embodiment, a second-order low-pass filter is selected before the abc dq0 conversion, and a first-order low-pass filter is selected after the conversion. The first-order and second-order low-pass filter circuits can be expressed by the following formula:

[0104]

[0105] Where H(S) is the transfer function, s is the complex frequency domain variable, T is the system time constant, which indicates the response speed of the system to input changes, ω n is the natural frequency of the system, indicating the frequency of the system oscillation; ζ is the damping ratio of the system, indicating the degree of damping of the system oscillation.

[0106] Step 5: Establish the decoupling equation. Once the dq0 conversion is completed, the measured dq0 quantity is sent to the decoupling controller circuit part ( Figure 4 ). Decoupling means that in the case of grid-connected operation, the real power (P) and reactive power (Q) are controlled by two different control circuits. The input is the filtered signal in step 4, and the decoupling equation is as follows:

[0107]

[0108] Among them, V diis the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, K p is the proportional gain, used to adjust the proportional control, K i is the integral gain, which is used to adjust the integral control, S is the synchronous speed, l is the inductance in the system, λ l is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, i dg is the actual d-axis voltage, i qg is the actual q-axis voltage, ω g is the system generator speed, L g is the generator inductance, V dg is the d-axis voltage, V qg is the q-axis voltage, R g is the control circuit resistance.

[0109] In network following mode, refer to and It can be calculated using the following formula:

[0110]

[0111] Where P measured is the active power actually generated by the system, V dESS is the d-axis voltage of the energy storage system, is the expected reactive power.

[0112] Step 6: Convert the relevant quantities in the above equation to unit quantities in the network tracking mode. Use the following formula to convert ωg in the above equation to unit quantities:

[0113]

[0114]

[0115] Among them, ω g is the grid angular frequency, V rate is the effective value of the grid voltage, V grid is the amplitude of the grid voltage, I rate is the effective value of the current in the power grid, P rateESS is the power of the energy storage system, ω LESS is the speed of the energy storage system, f g is the frequency of the energy storage system.

[0116] Step 7: Establish the equation in the network mode. In the network mode, the reference current and Will be generated from the other two FOPI inverters. These inverters will be used to control the grid forming voltage. The equation is as follows:

[0117]

[0118] Among them, V di is the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, V is the motor line voltage, K p is the proportional gain, used to adjust the proportional control, K i is the integral gain, which is used to adjust the integral control, S is the synchronous speed, λ is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, V dESS is the d-axis voltage of the energy storage system (ESS), V qESS is the q-axis voltage of the electric energy storage system.

[0119] Step 8: Use a virtual phase-locked loop to generate the desired frequency and convert it into abc quantities. In order to control the frequency according to the desired value, instead of using the frequency as the inverter parameter, a virtual phase-locked loop is used to generate the desired frequency and convert the dq quantity into abc quantities to generate the desired voltage of the inverter. A virtual phase-locked loop should be used to generate the desired frequency and convert it into abc quantities. The equations of the virtual phase-locked loop and the reference voltage generator are as follows:

[0120]

[0121] Where ωt is a periodic variable, f is the frequency of the periodic variable, t is time, indicating the current moment, φ is the phase angle, and the mod function accepts the reminder after dividing the first parameter by the second parameter.

[0122]

[0123] ωt is θ, x d , x q Represents a vector in dq mode, x a , x b , x c is a vector in the abc coordinate system. The inverter reference voltage using dq0 and θ can be developed using SVM / SVPWM technology.

[0124] The basic inverter model constructed involves multiple equations, starting from the system composition equation, through the steps of voltage and current unit conversion, dq quantity conversion, filtering, decoupling, reference current calculation, unit quantity conversion, and equation establishment in different modes, and finally the expected voltage of the inverter is obtained through the virtual phase-locked loop and conversion to abc quantities. The equations of each step are interrelated and together constitute the basic model of the inverter.

[0125] Furthermore, obtaining the optimal boundaries of the inverter parameters includes:

[0126] The objective function is preset and the Monte Carlo simulation method is used to simulate the initial feasible solution search space of the controller design parameters.

[0127] Specifically, in this embodiment, when most of the controllers are designed using metaheuristic algorithms, the initial solution search space is randomly selected, but this may cause the problem of falling into a local optimal solution. In traditional metaheuristic methods, the selection of the optimal solution depends on the boundary of the solution, considering that one boundary may provide a local optimum, while selecting other boundaries may provide a global solution. The boundary search problem of finding a completely feasible solution can be solved by Monte Carlo simulation. In this work, Monte Carlo simulation is applied to select 1000 uniformly distributed samples. The reason for choosing a uniform distribution is that the initially generated solutions will be uniformly distributed within a given range, so the final optimal solution range will not be biased by any specific solution range.

[0128] The Monte Carlo solution can be used to find the optimal boundary, and it is easier and faster to apply the global or near-global solution search algorithm on this basis. Therefore, this study initially conducted a larger search space without knowing the range of feasible solutions, and found the feasible solution boundary after a user-defined number of simulations (Monte Carlo). The objective (error) function used for this problem is as follows:

[0129]

[0130] Among them, P error , Q error , V d error , V q error Vq error is P, Q, V d , V q Controller error.

[0131] Further, based on the artificial bee colony optimization algorithm, optimizing the optimal boundary of the inverter parameters includes:

[0132] Step 1: Based on the optimal boundary of the inverter parameters, the algorithm parameters are initialized, and an initial honey source is generated using a first preset formula; the initial honey source refers to an initial feasible solution, that is, the initial value of the inverter parameters;

[0133] Step 2: Use the second preset formula to update the initial nectar source, obtain a new nectar source, and use a greedy algorithm to compare the fitness values ​​of the new and old nectar sources to select the optimal value;

[0134] The fitness value is calculated by substituting the solution represented by the nectar source (i.e., the feasible solution) into the fitness function. The fitness function is the above-mentioned target variable function Error (ITAE);

[0135] Step 3: For the nectar source with the best value, a roulette strategy is used to select it, and the second preset formula is used again to find a new nectar source, and the best value is saved;

[0136] Step 4: If a scout bee appears, regenerate the initial position and perform update selection, otherwise execute step 5; scout bee refers to the occurrence of a situation where a solution has not been improved after a certain number of iterations. When this situation occurs, a scout bee appears and randomly generates a new solution to increase the diversity of the population and prevent the algorithm from falling into a local optimum.

[0137] Step 5: If the number of iterations is less than the preset number of iterations, go to step 2; otherwise, output the optimal solution.

[0138] The iterative process is reflected in step 2. The initial nectar source is updated using the second preset formula to obtain a new nectar source, and the greedy algorithm is used to compare the fitness values ​​of the new and old nectar sources to select the optimal value. This is the first time that the nectar source is updated and compared, and it is the beginning of the iterative process. If the number of iterations is less than the preset number of iterations, this step and subsequent steps (relevant operations will also be involved when the conditions are met in steps 3 and 4) will be repeated to continuously optimize the nectar source until the preset number of iterations is reached.

[0139] Specifically, in this embodiment, before applying the optimization algorithm, it is necessary to generate the first generation of initial solutions of the optimization algorithm, and generate a unified solution set from the solution boundary created by Monte Carlo simulation. This initial solution generation method is discussed in Algorithm 1 (initial solution generation).

[0140]

[0141] Initialize the model parameters such as filter parameters, system voltage, frequency, etc. according to Algorithm 1. After the parameters are initialized, a random solution is generated by uniform distribution within the solution boundary obtained by Monte Carlo simulation. Then the artificial bee colony algorithm is used for continuous iterative optimization.

[0142] The artificial bee colony optimization algorithm was proposed by D.Karaboga et al. in 2005. In this algorithm, the location of the nectar source is used to represent the solution, and the number of pollen in the nectar source is used to represent the fitness value of the solution. All bees are divided into three groups: employed bees, follower bees, and explorer bees. Employed bees and follower bees each account for half of the total number of bee colonies. Employed bees are responsible for the initial search for nectar sources and collecting nectar to share information, follower bees are responsible for staying in the hive to collect nectar according to the information provided by employed bees, and explorer bees are responsible for randomly finding new nectar sources to replace the original nectar sources after the original nectar sources are abandoned. Like other swarm intelligence algorithms, the ABC algorithm is iterative. After the initialization of the bee colony and nectar source, three processes, namely the employed bee, follower bee, and explorer bee stages, are repeatedly executed to find the optimal solution to the problem. Each stage is described as follows:

[0143] Initialization of the swarm:

[0144] Initialize the parameters of the ABC algorithm, which include the number of nectar sources SN, the number of times a nectar source is determined to be abandoned limit, and the number of iterations to terminate. In the standard ABC algorithm, the number of nectar sources SN is equal to the number of employed bees and the number of follower bees. The formula for generating a nectar source is:

[0145] x ij =x minj +rand[0,1](x maxj -x minj ) (twenty one)

[0146] Where X ij Represents the i-th nectar source X i The j-th dimension value of X, i is in {1, 2, ..., SN}, j is in {1, 2, ..., D}; minj and X maxj Represent the minimum and maximum values ​​of the jth dimension respectively. Initializing the honey source is to assign a random value within the value range to all dimensions of each honey source through the above formula, thereby randomly generating SN initial honey sources.

[0147] Hired bee stage:

[0148] In the hired bee stage, hired bees use the following formula to find new nectar sources:

[0149]

[0150] Where: X k represents the neighborhood honey source, K is in the range of {1, 2, …, SN}, and K is not equal to i; is a random number between -1, 1. After obtaining the new nectar source through formula (22), the greedy algorithm is used to compare the fitness values ​​of the new and old nectar sources and select the best one.

[0151] Follow the bee stages:

[0152] The hired bee stage ends and the follower bee stage begins. In this stage, hired bees share nectar source information in the dance area. Follower bees analyze this information and use a roulette strategy to select nectar sources for tracking and mining, so as to ensure that the probability of mining nectar sources with higher fitness values ​​is greater. The mining process of follower bees is the same as that of hired bees. They use formula (22) to find new nectar sources and keep those with better fitness.

[0153] Honey source has a parameter trial. When the honey source update is retained, trial is 0; otherwise, trial increases by 1. Therefore, trial can count the number of times a honey source has not been updated.

[0154] Exploration Bee Stages:

[0155] If a nectar source has not been updated after multiple mining, that is, the trial value is too high and exceeds the preset threshold limit, then this nectar source needs to be abandoned and the exploration bee stage is started. This reflects the negative feedback and fluctuation properties of self-organization in ABC. In this stage, the exploration bee uses formula (23) to randomly find a new nectar source to replace the abandoned nectar source.

[0156] x ij =x minj +rand[0,1](x maxj -x minj ) (twenty three)

[0157] The algorithm flow is as follows:

[0158]

[0159] Step 4: SIMULINK model simulation test;

[0160] The simulation model is developed in the RTLAB compatible SIMULINK environment. Figure 5-Figure 7 As shown. The whole system is divided into two different modules, namely SM calculation and SC user interface. SM calculation is used for the main model and control part, while SC user interface is used for input generation and data visualization, or in other words, for monitoring. The SM calculation module consists of ESS, inverter, RL circuit, load, grid connection, measured variable conversion and control system. The selected ESS model is a lithium-ion battery selected from the SIMULINK library. The basic parameters of the simulation are shown in Table 1. The selected inverter is an average model.

[0161] Table 1 System parameters

[0162]

[0163] Simulation scenario 1 setup;

[0164] Assume that in the simulation experiment, the model initially runs in the grid-connected mode, then an island occurs at 2 seconds, and the controller switches to the island mode. At 7 seconds, the system returns to normal and runs in grid-connected mode. Figure 8-Figure 12 As shown in the figure, by observing the changes in the parameters of the model, it is found that the peak overshoots of the frequency, d-axis and real power controllers are 0.3844% (when reconnected to the grid), 7.9772% (when isolated) and 227.9162% (when isolated). The settling times of these controllers are 7.4562 seconds (when reconnected to the grid), 5.0098 seconds (when isolated) and 7.0229 seconds (when reconnected to the grid). Since the grid was reconnected after 7s, the d-axis voltage was not affected by the change in the controller working mode switching from the perspective of the stabilization time of the voltage d-axis controller. The peak times are 7.0444 seconds (when reconnected to the grid), 2.6800 seconds (when isolated) and 2.0076 seconds (when isolated), indicating that when the grid was reconnected at the 7th second, the system was not affected by the excessive overshoot / overshoot of the voltage d-axis controller or the real power controller. For the q-axis voltage and reactive power, the performance of the ABC-based FOPI controller is also satisfactory. In both cases, the controller successfully reaches the steady-state condition within 3 seconds and 7.4 seconds, respectively.

[0165] Simulation scenario 2 settings;

[0166] Assume that in the simulation experiment, the load level changes every 3 seconds in both island mode and grid-connected mode. The load level changes to 8 MW, 4 MW, 5 MW, and 6 MW at 0 seconds, 3 seconds, 6 seconds, and 9 seconds, respectively. Figure 13-Figure 17 In the example, all transient changes due to changes in load levels and grid conditions (islanding and grid reconnection) are shown using appropriate scales to demonstrate the robustness of the controller. Figure 13-Figure 17 It can be seen that for each load level, the controller operates satisfactorily and reaches a steady state. Figure 13-Figure 17 The performance of the ABC-based FOPI controller in scenario 2 is shown.

[0167] Simulate the settings of scenarios 3 and 4;

[0168] It is assumed that in the simulation experiment, all line parameters (R and L) vary by ±5%. The results show that the performance of the proposed method is satisfactory. For these deviations, the +5% and -5% changes that occur during the grid reconnection process, such as Fig.18 Picture to Fig. 22The figure shows the performance of the ABC-based FOPI controller in scenarios 3 and 4, with peak overshoots of 0.0877% and 0.0225% for the frequency, respectively. The peak overshoots of the d-axis voltage controller during islanding operation were 8.0018% and 7.3432%, respectively. For the real power controller, changes of +5% and -5% also occurred during islanding operation, and these figures were 195.5130% and 3.6629%, respectively. The settling times of the d-axis voltage and real power controllers to these changes were 5.0475s and 4.8669s, respectively. In both cases, the grid reconnection was performed at the 8th second, and the grid reconnection was very effective. In summary, the performance is satisfactory as can be seen from the response graphs of the controllers.

[0169] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A design method for a grid-building and grid-following inverter based on artificial bee colony optimization fractional-order controller, characterized in that: include: Construct a basic model of the inverter; Based on the basic model, using the Monte Carlo simulation method, the optimal boundaries of the inverter parameters are obtained; Based on an artificial bee colony optimization algorithm, the optimal boundaries of the inverter parameters are optimized to obtain optimal inverter parameters.

2. The design method of grid-building and grid-following inverter based on artificial bee colony optimization fractional-order controller according to claim 1 is characterized in that: The basic model for building an inverter includes: S1. Determine the system composition and establish the equation; the system composition equation is: Where R and L are the series resistance and inductance between the inverter terminal and the grid connection point or the common axis point, i L is the inductor current. V g , V inv are the grid voltage and inverter voltage respectively, di L Indicates the inductor current i L dl is the small change of time; S2. Voltage and inductor current unit conversion: Among them, V g (pu),i L (pu) is the normalized value of the measured voltage and current, V g phase to ground is the grid phase grounding voltage value, V gbase is the grid reference voltage value, i Lbase is the inductor reference current value, i Labc is the current value of the specific phase of the inductor; S3. Convert the normalized values ​​of the measured voltage and current into dq quantities: Among them, x d , x q Represents a vector in dq mode, x a , x b , x c is a vector in the abc coordinate system, and θ is the phase angle, which can be measured using a standard phase-locked loop (PLL) circuit; S4. Select first-order and second-order low-pass filters: Where H(S) is the transfer function, s is the complex frequency domain variable, T is the system time constant, which indicates the response speed of the system to input changes, ω n is the natural frequency of the system, ζ is the damping ratio of the system; S5. Establish decoupling equations: Among them, V di is the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, K p is the proportional gain, K i is the integral gain, S is the synchronous speed, l is the inductance in the system, λ l is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, i dg is the actual d-axis voltage, i qg is the actual q-axis voltage, ω g is the grid angular frequency, L g is the generator inductance, V dg is the d-axis voltage, V qg is the q-axis voltage, R g To control the circuit resistance; S6. In the network-following mode, the relevant quantity ω in the decoupling equation g Convert to unit quantity: Among them, ω g is the grid angular frequency, V rate is the effective value of the grid voltage, V grid is the amplitude of the grid voltage, I rate is the effective value of the current in the power grid, P rateESS is the power of the energy storage system, ω LESS is the grid angular frequency after conversion to unit quantity, f g is the frequency of the electric energy storage system; S7. Establish the equation in the network mode: Among them, V di is the d-axis voltage of the direct-drive permanent magnet synchronous generator, V qi is the q-axis voltage of the direct-drive permanent magnet synchronous generator, V is the motor line voltage, K p is the proportional gain, used to adjust the proportional control, K i is the integral gain, which is used to adjust the integral control, S is the synchronous speed, λ is the system damping ratio, is the desired d-axis voltage, is the desired q-axis voltage, V dESS is the d-axis voltage of the energy storage system, V qESS is the q-axis voltage of the electric energy storage system; S8. Use a virtual phase-locked loop to generate the desired frequency and convert it into abc quantities to obtain the desired voltage of the inverter, forming a basic model of the inverter.

3. The design method of grid-building and grid-following inverter based on artificial bee colony optimization fractional-order controller according to claim 1 is characterized in that: Obtaining the optimal boundaries of inverter parameters includes: The objective function is preset and the Monte Carlo simulation method is used to simulate the search space of the initial feasible solution of the controller design parameters; The objective function is: Among them, Error(ITAE) represents the objective function, t represents the time variable, P error Represents the active power error, Q error Represents reactive power error, V derror Represents the d-axis error of voltage, V qerror Represents the q-axis error of the voltage.

4. The design method of grid-building and grid-following inverter based on artificial bee colony optimization fractional-order controller according to claim 1 is characterized in that: Based on the artificial bee colony optimization algorithm, optimizing the optimal boundary of the inverter parameters includes: Step 1: Based on the optimal boundary of the inverter parameters, the algorithm parameters are initialized, and an initial honey source is generated using a first preset formula; wherein the initial honey source refers to an initial feasible solution, that is, the initial value of the inverter parameters; Step 2: Use the second preset formula to update the initial nectar source, obtain a new nectar source, and use a greedy algorithm to compare the fitness values ​​of the new and old nectar sources to select the optimal value; Step 3: For the nectar source with the best value, a roulette strategy is used to select it, and the second preset formula is used again to find a new nectar source, and the best value is saved; Step 4: If a scout bee appears, regenerate the initial position and perform update selection, otherwise execute step 5; Step 5: If the number of iterations is less than the preset number of iterations, repeat steps 2 to 4; otherwise, output the optimal solution.

5. The design method of grid-building and grid-following inverter based on artificial bee colony optimization fractional-order controller according to claim 4 is characterized in that: The first preset formula is: x ij =x minj +rand[0,1](x maxj -x minj ) Among them, X ij represents the i-th nectar source X i The j-th dimension value, X minj and X maxj Respectively represent the minimum and maximum values ​​of the j-th dimension; The second preset formula is: Among them, v ij represents the j-th latitude value of the i-th individual of the updated new nectar source, Represents a random number between [-1,1], used to control the step size and direction of the update, X k Indicates the neighborhood nectar source.

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