CN113541170A - Fuel cell emergency power supply grid-connected inversion control method and system - Google Patents

Fuel cell emergency power supply grid-connected inversion control method and system Download PDF

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CN113541170A
CN113541170A CN202110666631.7A CN202110666631A CN113541170A CN 113541170 A CN113541170 A CN 113541170A CN 202110666631 A CN202110666631 A CN 202110666631A CN 113541170 A CN113541170 A CN 113541170A
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firefly
fuel cell
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陈启宏
覃国安
胡宇航
张立炎
周克亮
贺远华
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Wuhan University of Technology WUT
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a grid-connected inversion control method and a grid-connected inversion control system for a fuel cell emergency power supply, wherein the method comprises the steps of establishing a state space mathematical model according to a topological structure of a grid-connected inverter, carrying out discretization treatment, and adding a prediction step length and a control step length to obtain a prediction equation; then, constructing a target function with constraint conditions by using the limiting conditions of the control quantity duty ratio and the actual requirements of the output grid-connected current; and finally, associating the actual control quantity duty ratio of the inverter with the brightness of the firefly by adopting a firefly intelligent optimization algorithm with the characteristic of quick optimization, obtaining the optimal duty ratio of PWM (pulse-width modulation) waves for controlling a switching tube, and optimally controlling a power circuit of the three-phase four-leg grid-connected inverter. The grid-connected inverter can obtain better dynamic and static performances, the deviation between the current merged into the power grid and the set value is small enough, and when the set value of the output current is changed, the grid-connected current of the inverter can track the set value in a shorter time.

Description

Fuel cell emergency power supply grid-connected inversion control method and system
Technical Field
The invention relates to the field of grid-connected inversion control of fuel cell emergency power supplies, in particular to a grid-connected inversion control method and a grid-connected inversion control system of a fuel cell emergency power supply.
Technical Field
In recent years, with the rapid development of new energy power generation technology, fuel cells are becoming hot research spots in the new energy field. However, in a system using a fuel cell as an electric power source, the fuel cell cannot meet the industrial power demand due to the disadvantages that the output voltage of the fuel cell is seriously affected by the output power, the voltage range is greatly changed, the output voltage is low, and the like, and it is necessary to change the output of the fuel cell into a stable direct current or invert and grid the direct current into a power consumption meeting the industrial or daily life through a power regulating system. The grid-connected inverter for the fuel cell is an important device for power regulation in the fuel cell power generation equipment, and is a basic premise that the electric energy of the fuel cell can be incorporated into a power grid. The grid-connected inverter for the fuel cell has the main function that high-quality, high-voltage and stable direct current provided by the front-stage equipment is inverted into alternating current and then is incorporated into a power grid, and the output power quality of the grid-connected inverter is directly related to the stability of the power grid and the safety and service life of the whole system equipment. In order to ensure the safety of the fuel cell and the power grid, a grid-connected inverter with low steady-state error, strong robustness and fast dynamic characteristics is indispensable, and the research of a control strategy is the key for determining the steady-state precision, the dynamic performance and the robustness of the inverter. The model prediction control is widely applied by the advantages of high control precision, strong robustness, fast dynamic response and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a grid-connected inversion control method and a grid-connected inversion control system for a fuel cell emergency power supply, which can enable a grid-connected inverter to obtain better dynamic and static performances, have enough small deviation between the current merged into a power grid and a set value, and can enable the grid-connected current of the inverter to track the set value in a shorter time when the set value of the output current is changed.
In order to achieve the purpose, the grid-connected inversion control method for the fuel cell emergency power supply is characterized by acting on a fuel cell emergency power supply system to carry out optimized control on a power circuit of a three-phase four-bridge-arm grid-connected inverter, and comprises the following steps:
1) establishing a state space mathematical model by utilizing kirchhoff voltage and current law according to a three-phase four-bridge-arm grid-connected inverter topological circuit;
2) discretizing the grid-connected inverter mathematical model in the continuous time domain state to obtain the grid-connected inverter mathematical model in the discrete time domain;
3) introducing two parameters of a prediction step length and a control step length which are needed in prediction control to obtain a prediction equation of the grid-connected inverter, and writing the prediction equation into a vector equation form;
4) on the basis of the vector form prediction equation, an objective function with constraint conditions is constructed by combining the limiting conditions of the duty ratio of the controlled variable and the actual requirements of output grid-connected current;
5) the method comprises the steps of adopting a firefly intelligent optimization algorithm, associating the actual control quantity duty ratio of an inverter with the brightness of the firefly, obtaining the optimal duty ratio of PWM (pulse-width modulation) waves of a control switch tube by simulating the movement process of the firefly, and carrying out optimization control on a power circuit of the three-phase four-leg grid-connected inverter.
Preferably, the expression of the state space mathematical model in step 1) is:
Figure BDA0003117686140000021
Figure BDA0003117686140000022
Figure BDA0003117686140000031
Figure BDA0003117686140000032
in the formula, an inverter side inductor L1Has a current of i1a，i1b，i1cGrid side inductance L2Has a current of i2a，i2b，i2cFilter capacitor CfHas a voltage of uca，ucb，ucc，RcIs a filter capacitor CfDamping resistor connected in series, vga，vgb，vgcIs a three-phase AC network ua，ub，uc，unThe voltage of the center points of four bridge arms, t is time, R2 is equivalent series resistance on an inductor L2, and L isnIs a neutral inductor, R, connected in series on the neutral line of the fourth bridge armnIs an inductance LnThe equivalent series resistance of (c).
Preferably, the mathematical model of the grid-connected inverter in the discrete time domain in step 2) is as follows:
Figure BDA0003117686140000033
in the formula, phi, gamma, lambda and D are all variable coefficients.
Preferably, the vector-form prediction equation of the three-phase four-leg grid-connected inverter in the step 3) is as follows:
Figure BDA0003117686140000034
wherein X (k) is a state vector, Y (k) is an output vector, x (k) is a state variable, U (k) is a control vector, V (k) is a control vectorg(k) For grid variables, k is time, and G, H, S, W are control coefficients, respectively.
Preferably, the objective function in step 4) is:
Figure BDA0003117686140000035
where θ is a, b, c, u is a, b, c, n, p is the prediction step, i is2θ *(k + i) is a reference amount of the output current of the three phases at the time point of k + i A, B, C, i2θ(k + i | k) is the predicted value of the three-phase output current of the controller at time k to time k + i, du(k + i-1) represents the duty ratio of the switching tube of A, B, C, N four bridge arms at the moment of k + i-1, and q and r are weight coefficients of the tracking error of the output current and the smoothness of the output current respectively.
Preferably, the constraint condition of the objective function is du(k+i-1)∈[0,1]。
Preferably, the luminance of the firefly is set in inverse proportion to the objective function j (x) in the step 5):
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of firefly at position x.
Preferably, the specific process of the firefly algorithm adopted in the step 5) is as follows:
(1) setting population quantity, maximum iteration times, initial attraction, light intensity absorption coefficient and step size factor;
(2) generating a corresponding number of fireflies according to the set population size, and randomly allocating a position to each firefly within the constraint conditions;
(3) calculating the brightness of each firefly generated randomly, enabling the brightness of the firefly to be the reciprocal of the value function substituted by the current position of the firefly, then sequencing the brightness of each firefly, and selecting the optimal individual in the current population;
(4) updating the position of each firefly, and enabling the firefly with lower brightness to move towards the firefly with higher brightness, and enabling the firefly with highest brightness to randomly move within a set range;
(5) calculating the brightness of each firefly again according to the updated firefly position, reordering, and repeating the step (4) until the difference of duty ratios of two consecutive times is smaller than the set precision;
(6) and outputting the optimal individual value and the global minimum value point in the solution space.
The invention also provides a grid-connected inversion control system of the fuel cell emergency power supply, which is characterized by comprising a power conversion circuit and a controller, wherein the controller can execute the grid-connected inversion control method of the fuel cell emergency power supply.
Further, the power conversion circuit comprises a voltage stabilizing capacitor at an input end, four bridge arms and an LCL filter of which an output end is used for filtering harmonic waves.
According to the topological structure of the three-phase four-bridge-arm grid-connected inverter, a state space mathematical model is established, discretization is carried out, and a prediction equation is obtained by adding a prediction step length and a control step length; then, constructing a target function with constraint conditions by using the limiting conditions of the control quantity duty ratio and the actual requirements of the output grid-connected current; and finally, a firefly intelligent optimization algorithm with the characteristic of fast optimization is adopted, the actual control quantity duty ratio of the inverter is associated with the brightness of the firefly, the optimal duty ratio of PWM (pulse-width modulation) waves for controlling a switching tube is obtained by simulating the movement process of the firefly, and the power circuit of the three-phase four-leg grid-connected inverter is optimally controlled. Compared with the prior art, the control method of the three-phase four-bridge-arm grid-connected inverter provided by the invention can enable the grid-connected inverter to have better dynamic and steady-state performances.
Drawings
Fig. 1 is a block diagram of a fuel cell emergency power supply system.
Fig. 2 is a schematic diagram of a power conversion circuit according to the present invention.
Fig. 3 is a schematic diagram of the implementation process of the firefly algorithm.
Fig. 4 is a simulation waveform diagram under the action of the PI controller.
Fig. 5 is a schematic diagram of FFT analysis under the action of a PI controller.
FIG. 6 is a graph of simulated waveforms under the action of a FA-MPC controller.
FIG. 7 is a diagram of FFT analysis performed by the FA-MPC controller.
FIG. 8 is a diagram showing the simulation results of the dynamic response of the phase B current under the action of two controllers.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the fuel cell emergency power grid-connected inverter control method provided by the present invention acts on a fuel cell emergency power system, and the fuel cell emergency power system has a structure as shown in fig. 1, and includes a fuel cell module, a DC/DC converter, a lithium battery module, a grid-connected inverter, and a power grid.
The invention provides a grid-connected inversion control method for an emergency power supply of a fuel cell, which comprises the following steps:
1) establishing a state space mathematical model by utilizing kirchhoff voltage and current law according to a three-phase four-bridge-arm grid-connected inverter topological circuit;
2) discretizing the grid-connected inverter mathematical model in the continuous time domain state to obtain the grid-connected inverter mathematical model in the discrete time domain;
3) introducing two parameters of a prediction step length and a control step length which are needed in prediction control to obtain a prediction equation of the grid-connected inverter, and writing the prediction equation into a vector equation form;
4) on the basis of the vector form prediction equation, an objective function with constraint conditions is constructed by combining the limiting conditions of the duty ratio of the controlled variable and the actual requirements of output grid-connected current;
5) the method comprises the steps of adopting a firefly intelligent optimization algorithm, associating the actual control quantity duty ratio of an inverter with the brightness of the firefly, obtaining the optimal duty ratio of PWM (pulse-width modulation) waves of a control switch tube by simulating the movement process of the firefly, and carrying out optimization control on a power circuit of the three-phase four-leg grid-connected inverter.
In the invention, the topological structure of the three-phase four-leg grid-connected inverter is shown in figure 2, wherein a direct-current power supply UdcAfter passing through a three-phase four-bridge arm inverter, the three-phase four-bridge arm inverter is filtered by an LCL filter and finally flows into a three-phase alternating current power grid vga，vgb，vgc. Inverter side inductor L1Has a current of i1a，i1b，i1cGrid side inductance L2Has a current of i2a，i2b，i2cFilter capacitor CfHas a voltage of uca，ucb，ucc。R1Is an inductance L1Equivalent series resistance of R2Is an inductance L2Equivalent series resistance of RcThen is the filter capacitor CfThe purpose of the damping resistor connected in series is to suppress the resonance peak brought by the LCL filter. L isnIs a neutral inductor connected in series on the neutral line of the fourth bridge arm and is used for improving the integral filtering effect of the filter, RnIs an inductance LnThe equivalent series resistance of (c). u. ofa，ub，uc，unThe voltage at the center point of the four bridge arms. And performing mathematical modeling on the three-phase four-bridge-arm grid-connected inverter according to the kirchhoff voltage law and the current law, and describing the mathematical modeling by using a mathematical expression to obtain a state space equation of the three-phase four-bridge-arm grid-connected inverter. Then discretizing and iterating the state space equation in the continuous time domain to obtain a prediction equation of the three-phase four-bridge-arm grid-connected inverter, then introducing two parameters of a prediction step length and a control step length, and finally obtaining a vector matrix of the prediction equationFormula (II) is shown. Designing a corresponding objective function by combining working condition requirements in actual operation of the inverter, substituting a prediction equation into the objective function for simplification and arrangement, adding a certain constraint condition according to the actual condition of the controlled quantity, then solving the minimum value of the objective function with the constraint by adopting a firefly algorithm, and outputting the optimal duty ratio after the optimization precision reaches the standard.
The grid-connected inverter has the function of converting the front-stage direct current into the alternating current and merging the alternating current into a power grid, A, B, C three phases of the grid-connected inverter form loops with N phases respectively, and the loops are not influenced with each other. One phase can be listed separately, analyzed using kirchhoff's voltage law and current law, then the other two phases can be analogized, and then the voltage-current equations for the three phases are summed up to give:
Figure BDA0003117686140000071
where t is time, the variables may be expressed as follows:
Figure BDA0003117686140000072
Figure BDA0003117686140000073
Figure BDA0003117686140000074
now let da, db, dc and dn be the duty cycles of the switching tubes on the four arms A, B, C and N, so that the potential of point a relative to the negative terminal of the DC voltage is u in one switching perioda＝da*Udc. In the same way, ub＝db*Udc，uc＝dc*Udc，un＝dn*UdcThe following can be obtained:
Figure BDA0003117686140000075
rewriting the above mathematical expression into a vector form yields:
Figure BDA0003117686140000081
wherein the state variable is x ═ i1 vc i2]TThe controlled variable is u ═ da db dc dn]TThe output variable is y ═ i2(ii) a Alpha, beta, lambda, gamma and D in the formula are all constant coefficient matrixes and can be obtained according to actual topological parameters of the inverter. Now define EiIs an i-order identity matrix, Oi×jIs an all-zero matrix with i rows and j columns, therefore, the five constant coefficient matrices α, β, λ, γ, and D can be expressed as follows:
Figure BDA0003117686140000082
Figure BDA0003117686140000083
Figure BDA0003117686140000084
γ＝[O3×6 E3]T (10)
D＝γT (11)
the expression is further simplified and arranged to obtain:
Figure BDA0003117686140000085
wherein A ═ α-1×β,B＝α-1×λ,C＝α-1X γ. Then discretizing the formula to obtain a mathematical expression of the three-phase four-bridge-arm grid-connected inverter in a discrete time domain:
Figure BDA0003117686140000086
wherein,
Figure BDA0003117686140000087
Figure BDA0003117686140000088
Figure BDA0003117686140000089
e is a natural constant. T issIs the sampling period of the control system.
The above formula is iterated for many times to obtain a prediction equation:
Figure BDA0003117686140000091
in the invention, the prediction step length of the designed prediction controller is p, and the control step length is m. For convenience, both the prediction step size and the control step size are selected to be p, i.e., m ═ p. Thus, p sets of prediction equations are available, which are then written in matrix form as follows:
Figure BDA0003117686140000092
order to
Figure BDA0003117686140000093
Figure BDA0003117686140000094
Figure BDA0003117686140000095
Figure BDA0003117686140000096
Figure BDA0003117686140000097
Figure BDA0003117686140000098
Figure BDA0003117686140000099
Figure BDA00031176861400000910
Therefore, a vector-form prediction equation of the three-phase four-leg grid-connected inverter can be obtained:
Figure BDA00031176861400000911
in the invention, the controlled object is the output current of the three-phase four-bridge-arm grid-connected inverter, and the controlled object has two requirements on the output current: the error between the output current and the reference current is as small as possible; ② the waveform of the output current is as smooth as possible. The first point requires that the basic shape of the output current is guaranteed as a main control target; the second point requires that the fluctuation of the output current be able to be suppressed as a secondary control target. The error can be expressed as the square of the difference between the reference current and the output current, i.e. (i)ref-io)2(ii) a The smoothness of the output current is influenced by a controlled quantity u, which can be used2To suppress fluctuations in current. And (3) combining the primary and secondary control targets to construct a cost function as shown in the following formula:
Figure BDA0003117686140000101
where θ is a, b, c, u is a, b, c, n, p is the prediction step, i is2θ *(k + i) is k + i at time A, B,Reference quantity of output current of C three phases, i2θ(k + i | k) is the predicted value of the three-phase output current of the controller at time k to time k + i, du(k + i-1) represents the duty ratio of the switching tube of A, B, C, N four bridge arms at the moment of k + i-1, and q and r are weight coefficients of the tracking error of the output current and the smoothness of the output current respectively.
The method mainly comprises the following steps that a cost function is divided into two parts, wherein the cost function is the sum of squares of differences between three-phase output current reference values and output current predicted values at all moments and is used for representing the fundamental wave shape of the output current of an inverter, and the smaller the part is, the smaller the error between the output current of the inverter and the reference current is; and the second is the sum of squares of duty ratios of the switching tubes on the four bridge arms at each moment, which is used for representing the control action change of the controller, and the smaller the part is, the smoother the waveform of the output current of the inverter is. q and r are two newly introduced parameters, namely weight coefficients of an output current tracking error and an output current smoothness, which represent the control bias degree of the controller for the two parts, and since the output current tracking error is a main control object and the current smoothness is an additional soft constraint, the following principle should be followed in the selection of the weight coefficients: q > > r.
By further simplifying the formula (17), it is possible to obtain:
Figure BDA0003117686140000102
wherein,
Figure BDA0003117686140000103
y*(k + i) is a reference value of the output current of the system at the moment k + i,
Figure BDA0003117686140000104
Q＝diag(q,…,q)，R＝diag(r,…,r)，Umin＝[0…0]T，Umax＝[1…1]T。
combining the formulas to obtain:
Figure BDA0003117686140000111
where Ψ ═ WG · x (k) + WS · Vg(k)。
The Firefly Algorithm (FA) is a novel heuristic group intelligent optimization algorithm, and because the Firefly algorithm has the characteristic of fast optimization, the method is adopted to solve the objective function.
In the firefly algorithm, the position of each firefly represents a feasible solution of the problem, the brightness of the firefly represents the fitness of the position of the firefly, and the higher the brightness of the firefly is, the better the fitness of the position of the individual is, namely, the better the position in the whole solution space is. In solving the space, every firefly can fly towards luminance than oneself high firefly, makes oneself be in more excellent position, and the bigger firefly of luminance is just also bigger to other firefly's attraction degree. In the present invention, the minimum value of the objective function is solved, so the luminance of the firefly and the objective function can be set in an inverse relationship:
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of firefly at position x.
The method includes the steps of conducting brightness sequencing on each firefly randomly given in initialization, and then moving each firefly according to the rule that low brightness is attracted by high brightness. The move formula is as follows:
Figure BDA0003117686140000112
wherein, beta0The attraction degree when the distance r to the firefly is 0; γ is the absorption coefficient of the light-transmitting medium (typically air); alpha is a step factor and is expressed as a random disturbance term; r isijIs that any two in space are respectively at the position XiAnd XjExpressed as follows, the Euclidean distance between fireflies i and j:
Figure BDA0003117686140000113
wherein n represents the maximum dimension of the location of firefly in space, and xi,kIndicating that the ith firefly is in space XiThe value of the k-th dimension coordinate at the location.
As shown in fig. 3, the specific flow of the firefly algorithm is as follows:
(1) basic parameters of the algorithm are initialized. The population number, the maximum iteration number, the initial attraction degree, the light intensity absorption coefficient and the step size factor are mainly set.
(2) And (5) initializing a population. And generating a corresponding number of fireflies according to the population size set in the last step, and randomly allocating a position to each firefly within the constraint conditions.
(3) The luminance calculation was performed for each firefly randomly generated in the previous step. For the minimum optimization problem, the firefly brightness can be made to be the reciprocal of the firefly current position substituted into the value function, then the brightness of each firefly is sequenced, and the optimal individual in the current population is selected.
(4) The location of each firefly is updated. The firefly with low luminance is moved to the firefly with high luminance by the formula (22), and the firefly with the highest luminance is moved randomly within a certain range.
(5) And (4) calculating the brightness of each firefly again according to the updated firefly position in the previous step, reordering, and repeating the step (4) until the difference of the duty ratios of two consecutive times is smaller than the set precision.
(6) And outputting the optimal individual value and the global minimum value point in the solution space.
Contrast experiment for grid-connected current waveform stability
A comparative experiment is performed when the grid-connected current waveforms are stable, and two groups of stable grid-connected current waveforms and current error waveforms are obtained and are shown in fig. 4. In the figure, the ordinate represents the grid-connected current and the current error respectively, and the abscissa represents the simulation time. FFT analysis by PI controller as shown in fig. 5, the amplitude of the current waveform by PI controller at the fundamental frequency was 20.21A, and THD was 0.78%. The simulation waveform under the action of the FA-MPC controller provided by the invention is shown in fig. 6, the FFT analysis under the action of the FA-MPC controller is shown in fig. 7, the amplitude of the current waveform under the action of the FA-MPC controller at the fundamental frequency is 20.86A, and the THD is 0.65%.
From the above experiments, it can be seen that the amplitudes at the fundamental frequency of the two controllers are 20.21A and 20.86A, respectively, which are not much different from the 21.21A set by us, but it can be found from the observation of their total harmonic distortion rates that the THD of the PI controller is 0.78%, and the THD of the FA-MPC controller is 0.65%, which are all low values, indicating that they contain fewer current harmonics, mainly low order harmonics.
For the PI controller and the FA-MPC controller, the FA-MPC controller is superior and has better control effect in view of current sine degree and smoothness, steady-state error and total harmonic distortion THD value.
Fig. 8 is a schematic diagram of a simulation result of dynamic response of the phase B current under the action of two controllers, when t is 0.1s, the reference effective value of the grid-connected current is reduced from 15A to 10A, and then the dynamic response of the phase B current is observed under the action of two different controllers. Amplifying the current waveform near 0.1s, and showing that the overshoot of the B-phase current waveform under the action of the PI controller reaches 4.8A, and the adjusting time is 0.4 ms; the overshoot of the B-phase current waveform under the action of the FA-MPC controller is only 4.5A, and the adjusting time is 0.55 ms. In the aspect of controlling the overshoot, the FA-MPC controller performs more excellently; the PI controller is 0.15ms faster than the FA-MPC controller in the adjustment time, and occupies 0.75% of 20ms of a control period, which is considered to be slightly different, and the influence is ignored, mainly because the calculation amount of the FA-MPC controller is larger than that of the PI controller, and the required time is longer. Overall, the control effect of both in terms of dynamic response is quite good, but FA-MPC controllers are much better than PI controllers.
Finally, it should be noted that the above detailed description is only for illustrating the technical solution of the patent and not for limiting, although the patent is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the patent can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the patent, which should be covered by the claims of the patent.

Claims (10)

1. The fuel cell emergency power supply grid-connected inversion control method is characterized in that: the method acts on a fuel cell emergency power supply system and performs optimal control on a power circuit of a three-phase four-bridge-arm grid-connected inverter, and comprises the following steps:
1) establishing a state space mathematical model by utilizing kirchhoff voltage and current law according to a three-phase four-bridge-arm grid-connected inverter topological circuit;
2) discretizing the grid-connected inverter mathematical model in the continuous time domain state to obtain the grid-connected inverter mathematical model in the discrete time domain;
3) introducing two parameters of a prediction step length and a control step length which are needed in prediction control to obtain a prediction equation of the grid-connected inverter, and writing the prediction equation into a vector equation form;
4) on the basis of the vector form prediction equation, an objective function with constraint conditions is constructed by combining the limiting conditions of the duty ratio of the controlled variable and the actual requirements of output grid-connected current;
5) the method comprises the steps of adopting a firefly intelligent optimization algorithm, associating the actual control quantity duty ratio of an inverter with the brightness of the firefly, obtaining the optimal duty ratio of PWM (pulse-width modulation) waves of a control switch tube by simulating the movement process of the firefly, and carrying out optimization control on a power circuit of the three-phase four-leg grid-connected inverter.
2. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: the expression of the state space mathematical model in the step 1) is as follows:
Figure FDA0003117686130000011
Figure FDA0003117686130000021
Figure FDA0003117686130000022
Figure FDA0003117686130000023
in the formula, an inverter side inductor L1Has a current of i1a，i1b，i1cGrid side inductance L2Has a current of i2a，i2b，i2cFilter capacitor CfHas a voltage of uca，ucb，ucc，RcIs a filter capacitor CfDamping resistor connected in series, vga，vgb，vgcIs a three-phase AC network ua，ub，uc，unThe voltage of the center points of four bridge arms, t is time, R2 is equivalent series resistance on an inductor L2, and L isnIs a neutral inductor, R, connected in series on the neutral line of the fourth bridge armnIs an inductance LnThe equivalent series resistance of (c).
3. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: the mathematical model of the grid-connected inverter in the discrete time domain in the step 2) is as follows:
Figure FDA0003117686130000024
in the formula, phi, gamma, lambda and D are all variable coefficients.
4. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: the vector form prediction equation of the three-phase four-leg grid-connected inverter in the step 3) is as follows:
Figure FDA0003117686130000025
wherein X (k) is a state vector, Y (k) is an output vector, x (k) is a state variable, U (k) is a control vector, V (k) is a control vectorg(k) For grid variables, k is time, and G, H, S, W are control coefficients, respectively.
5. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: the objective function in the step 4) is as follows:
Figure FDA0003117686130000031
where θ is a, b, c, u is a, b, c, n, p is the prediction step, i is2θ *(k + i) is a reference amount of the output current of the three phases at the time point of k + i A, B, C, i2θ(k + i | k) is the predicted value of the three-phase output current of the controller at time k to time k + i, du(k + i-1) represents the duty ratio of the switching tube of A, B, C, N four bridge arms at the moment of k + i-1, and q and r are weight coefficients of the tracking error of the output current and the smoothness of the output current respectively.
6. The grid-connected inversion control method for the emergency power supply of the fuel cell as claimed in claim 5, wherein: the constraint condition of the objective function is du(k+i-1)∈[0,1]。
7. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: in the step 5), the brightness of the firefly and the objective function j (x) are set to be in inverse proportion to each other:
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of firefly at position x.
8. The fuel cell emergency power supply grid-connected inversion control method according to claim 1, characterized in that: the specific process of the firefly algorithm adopted in the step 5) is as follows:
(1) setting population quantity, maximum iteration times, initial attraction, light intensity absorption coefficient and step size factor;
(2) generating a corresponding number of fireflies according to the set population size, and randomly allocating a position to each firefly within the constraint conditions;
(3) calculating the brightness of each firefly generated randomly, enabling the brightness of the firefly to be the reciprocal of the value function substituted by the current position of the firefly, then sequencing the brightness of each firefly, and selecting the optimal individual in the current population;
(4) updating the position of each firefly, and enabling the firefly with lower brightness to move towards the firefly with higher brightness, and enabling the firefly with highest brightness to randomly move within a set range;
(5) calculating the brightness of each firefly again according to the updated firefly position, reordering, and repeating the step (4) until the difference of duty ratios of two consecutive times is smaller than the set precision;
(6) and outputting the optimal individual value and the global minimum value point in the solution space.
9. The utility model provides a fuel cell emergency power supply inverter control system that is incorporated into power networks which characterized in that: the system comprises a power conversion circuit and a controller, wherein the controller can execute the fuel cell emergency power grid-connected inversion control method according to any one of claims 1 to 8.
10. The fuel cell emergency power grid-connected inverter control system according to claim 9, characterized in that: the power conversion circuit comprises a voltage stabilizing capacitor at an input end, four bridge arms and an LCL filter of which an output end is used for filtering harmonic waves.
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