CN113541170B - Grid-connected inversion control method and system for emergency power supply of fuel cell - Google Patents
Grid-connected inversion control method and system for emergency power supply of fuel cell Download PDFInfo
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- H02M1/00—Details of apparatus for conversion
- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
- H02M1/088—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters for the simultaneous control of series or parallel connected semiconductor devices
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- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
- H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
- H02M7/48—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/53—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
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Abstract
The invention discloses a grid-connected inversion control method and a system of a fuel cell emergency power supply, wherein the method establishes a state space mathematical model according to the topological structure of a grid-connected inverter, discretizes the model, and adds a prediction step length and a control step length to obtain a prediction equation; then, the limiting condition of the control quantity duty ratio and the actual requirement of the output grid-connected current construct an objective function with the limiting condition; and finally, adopting a firefly intelligent optimizing algorithm with a faster optimizing characteristic, correlating the actual control quantity duty ratio of the inverter with the brightness of fireflies, solving the optimal duty ratio of PWM waves for controlling the switching tube, and optimally controlling the power circuit of the three-phase four-bridge arm grid-connected inverter. The invention can enable the grid-connected inverter to obtain better dynamic and static performance, the deviation between the current incorporated into the power grid and the set value is small enough, and when the set value of the output current changes, the grid-connected current of the inverter can also track the set value in a shorter time.
Description
Technical Field
The invention relates to the field of grid-connected inversion control of emergency power supplies of fuel cells, in particular to a grid-connected inversion control method and system of an emergency power supply of a fuel cell.
Technical Field
In recent years, with the rapid development of new energy power generation technology, fuel cells are becoming a research hotspot 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 of serious influence of output power, large variation of voltage range, low output voltage and the like, and the output of the fuel cell needs to be changed into stable direct current or inversion grid connection through a power regulating system to become power consumption conforming to industry or daily life. Grid-connected inverters for fuel cells are important devices for power regulation in fuel cell power plants, and are a fundamental premise that fuel cell power can be incorporated into the power grid. The grid-connected inverter for the fuel cell has the main functions of inverting high-quality, high-voltage and stable direct current provided by the front-stage equipment into alternating current and then integrating the alternating current into a power grid, wherein the output power quality of the grid-connected inverter directly relates 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 a key for determining the steady-state precision, dynamic performance and robustness of the inverter. The model predictive control is widely applied by virtue of the advantages of high control precision, strong robustness, quick 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 performance, the deviation between the current of a power grid and a set value is small enough, and when the set value of an output current is changed, the grid-connected current of the inverter can track the set value in a shorter time.
In order to achieve the above purpose, the invention provides a grid-connected inversion control method of a fuel cell emergency power supply, which is characterized in that the method acts on a fuel cell emergency power supply system to optimally control a power circuit of a three-phase four-bridge arm grid-connected inverter, and the method comprises the following steps:
1) According to a topological circuit of the three-phase four-bridge arm grid-connected inverter, a state space mathematical model is established by utilizing kirchhoff voltage and current law;
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) Two parameters of a required prediction step length and a control step length in prediction control are introduced to obtain a prediction equation of the grid-connected inverter, and the prediction equation is written into a vector equation form;
4) On the basis of the vector form prediction equation, combining the limiting condition of the control quantity duty ratio and the actual requirement of the output grid-connected current to construct an objective function with a constraint condition;
5) And (3) correlating the actual control quantity duty ratio of the inverter with the brightness of the firefly by adopting a firefly intelligent optimizing algorithm, obtaining the optimal duty ratio of PWM waves for controlling the switching tube by simulating the moving process of the firefly, and optimally controlling the power circuit of the three-phase four-bridge arm grid-connected inverter.
Preferably, the expression of the state space mathematical model in step 1) is:
in the inverter side inductance L 1 The current of (2) is i 1a ，i 1b ，i 1c Grid side inductance L 2 The current of (2) is i 2a ，i 2b ，i 2c Filter capacitor C f Is of the voltage u ca ，u cb ，u cc ，R c Is a filter capacitor C f Damping resistor v connected in series ga ，v gb ，v gc U is a three-phase AC network a ，u b ，u c ，u n Is the voltage of the central points of four bridge arms, t is time, R2 is the equivalent series resistance on the inductor L2, L n Is a neutral inductance connected in series on the middle line of the fourth bridge arm, R n Is the inductance L n Equivalent series resistance.
Preferably, the mathematical model of the grid-connected inverter in the discrete time domain in the step 2) is:
wherein, phi, Γ, Λ, D are all variable coefficients.
Preferably, the prediction equation of the vector form of the three-phase four-bridge arm grid-connected inverter in the step 3) is:
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 g (k) K is a time, and G, H, S, W is a control coefficient.
Preferably, the objective function in the step 4) is:
where θ=a, b, c, u=a, b, c, n, p is the prediction step, i 2θ * (k+i) is a reference value of output currents of three phases A, B, C at the time of k+i, i 2θ (k+i|k) is the predicted value of the three-phase output current of the controller at the time k for the time k+i, d u (k+i-1) represents the duty cycle of the switching tubes of A, B, C, N four bridge arms at the time k+i-1, q and r being the weight coefficients of the output current tracking error and the output current smoothness, respectively.
Preferably, the constraint of the objective function is d u (k+i-1)∈[0,1]。
Preferably, in the step 5), the brightness of the firefly and the objective function J (x) are set in an inverse relationship with each other:
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of the firefly at position x.
Preferably, the specific flow of the firefly algorithm adopted in the step 5) is as follows:
(1) Setting population quantity, maximum iteration times, initial attractiveness, light intensity absorption coefficient and step factor;
(2) Generating a corresponding number of fireflies according to the set population size, and randomly distributing positions for each firefly in a constraint condition;
(3) Calculating the brightness of each firefly generated randomly, substituting the brightness of the firefly into the reciprocal of the value of the cost function for the current position of the firefly, and then sequencing the brightness of each firefly to select the optimal individual in the current group;
(4) Updating the position of each firefly, for fireflies with lower brightness, moving the fireflies to fireflies with higher brightness, and for fireflies with highest brightness, randomly moving the fireflies within a set range;
(5) According to the updated firefly positions, calculating the brightness of each firefly again and reordering, and repeating the step (4) until the difference of the continuous duty ratio of two times is smaller than the set precision;
(6) And outputting the optimal individual value and the global minimum 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 at an output end 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 step length and a control step length are added to obtain a prediction equation; then, the limiting condition of the control quantity duty ratio and the actual requirement of the output grid-connected current construct an objective function with the limiting condition; and finally, a firefly intelligent optimizing algorithm with a faster optimizing characteristic is adopted, the actual control quantity duty ratio of the inverter is related to the brightness of fireflies, the optimal duty ratio of PWM waves for controlling the switching tube is obtained by simulating the moving process of the fireflies, and the power circuit of the three-phase four-bridge arm grid-connected inverter is optimally controlled. Compared with the prior art, the control method of the three-phase four-bridge arm grid-connected inverter 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 system.
Fig. 2 is a schematic topology diagram of a power conversion circuit according to the present invention.
Fig. 3 is a schematic diagram of an implementation of the firefly algorithm.
Fig. 4 is a simulated waveform diagram under the action of the PI controller.
Fig. 5 is a schematic diagram of FFT analysis under PI controller.
FIG. 6 is a diagram of simulated waveforms under the action of the FA-MPC controller.
FIG. 7 is a schematic diagram of FFT analysis under the action of the FA-MPC controller.
FIG. 8 is a schematic diagram of the results of the B-phase current dynamic response simulation under the action of two controllers.
Detailed Description
The invention is described in further detail below with reference to the drawings and specific examples.
As shown in FIG. 1, the grid-connected inversion control method of the fuel cell emergency power supply provided by the invention acts on a fuel cell emergency power supply system, and the structure of the fuel cell emergency power supply system is shown in FIG. 1 and comprises a fuel cell module, a DC/DC converter, a lithium cell module, a grid-connected inverter and a power grid.
The invention provides a grid-connected inversion control method of a fuel cell emergency power supply, which comprises the following steps:
1) According to a topological circuit of the three-phase four-bridge arm grid-connected inverter, a state space mathematical model is established by utilizing kirchhoff voltage and current law;
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) Two parameters of a required prediction step length and a control step length in prediction control are introduced to obtain a prediction equation of the grid-connected inverter, and the prediction equation is written into a vector equation form;
4) On the basis of the vector form prediction equation, combining the limiting condition of the control quantity duty ratio and the actual requirement of the output grid-connected current to construct an objective function with a constraint condition;
5) And (3) correlating the actual control quantity duty ratio of the inverter with the brightness of the firefly by adopting a firefly intelligent optimizing algorithm, obtaining the optimal duty ratio of PWM waves for controlling the switching tube by simulating the moving process of the firefly, and optimally controlling the power circuit of the three-phase four-bridge arm grid-connected inverter.
In the invention, the topological structure of the three-phase four-bridge arm grid-connected inverter is shown in fig. 2, wherein a direct current power supply U dc After passing through the three-phase four-bridge arm inverter, the three-phase four-bridge arm inverter is filtered by an LCL filter to be the mostFinally flows into three-phase AC network v ga ，v gb ，v gc . Inverter side inductance L 1 The current of (2) is i 1a ，i 1b ，i 1c Grid side inductance L 2 The current of (2) is i 2a ，i 2b ，i 2c Filter capacitor C f Is of the voltage u ca ，u cb ，u cc 。R 1 Is the inductance L 1 Equivalent series resistance of R 2 Is the inductance L 2 Equivalent series resistance of R c Then it is the filter capacitor C f The damping resistor is connected in series, so as to restrain resonance peak caused by the LCL filter. L (L) n Is a neutral inductance connected in series on the middle line of the fourth bridge arm and is used for improving the overall filtering effect of the filter, R n Is the inductance L n Equivalent series resistance. u (u) a ，u b ，u c ，u n Then the voltage at the center point of the four legs. According to kirchhoff's voltage law and current law, mathematical modeling is carried out on the three-phase four-leg grid-connected inverter, and the three-phase four-leg grid-connected inverter is described by a mathematical expression to obtain a state space equation of the three-phase four-leg grid-connected inverter. And then discretizing and iterating the state space equation under the continuous time domain to obtain a prediction equation of the three-phase four-bridge arm grid-connected inverter, and then introducing two parameters of a prediction step length and a control step length to finally obtain a vector matrix form of the prediction equation. According to the working condition requirements in the actual operation of the inverter, a corresponding objective function is designed, a prediction equation is substituted into the objective function for simplification and arrangement, a certain constraint condition is added according to the actual condition of the controlled quantity, a firefly algorithm is adopted to find the minimum value of the objective function with constraint, and after the optimization accuracy reaches the standard, the optimal duty ratio is output.
The grid-connected inverter plays a role in converting the front-stage direct current into alternating current and integrating the alternating current into a power grid, A, B, C three phases of the grid-connected inverter and N phases form loops, and the loops do not affect each other. One of the phases 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 and current equations of the three phases are summarized, so that the following steps are obtained:
where t is time, each variable can be represented as follows:
let da, db, dc, dn now be the duty cycle of the switching tubes on the four legs a, B, C and N, so that in one switching cycle the potential at point a with respect to the negative side of the dc voltage is u a ＝d a *U dc . Similarly, u b ＝d b *U dc ，u c ＝d c *U dc ，u n ＝d n *U dc The method can obtain:
the above mathematical expression is rewritten into a vector form, and the following can be obtained:
wherein the state variable is x= [ i ] 1 v c i 2 ] T The control variable is u= [ d ] a d b d c d n ] T The output variable is y=i 2 The method comprises the steps of carrying out a first treatment on the surface of the Alpha, beta, lambda, gamma and D in the above formula are constant coefficient matrixesAnd obtaining according to the actual topological parameters of the inverter. Definition E now i For the unit matrix of order i, O i×j For an all zero matrix of i rows and j columns, the five constant coefficient matrices α, β, λ, γ, D can be expressed as:
γ＝[O 3×6 E 3 ] T (10)
D＝γ T (11)
the expression is further simplified and arranged, and the following can be obtained:
wherein a=α -1 ×β,B＝α -1 ×λ,C＝α -1 X gamma. Then discretizing the above formula to obtain a mathematical expression of the three-phase four-bridge arm grid-connected inverter in a discrete time domain:
wherein, e is a natural constant. T (T) s For controlling the sampling period of the system.
And iterating the above formula for a plurality of times to obtain a prediction equation:
in the invention, let the prediction step length of the designed prediction controller be p and the control step length be m. For convenience, the prediction step size and the control step size are both chosen to be p, i.e. m=p. Thus, p sets of prediction equations can be obtained, which are then written in matrix form as follows:
order the
Therefore, a prediction equation of a vector form of the three-phase four-bridge arm grid-connected inverter can be obtained:
in the invention, the controlled object is the output current of the three-phase four-bridge arm grid-connected inverter, and two requirements are provided for the output current: (1) 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 requirement guarantees the basic shape of the output current as the main control target; the second point requires that the fluctuation of the output current can be suppressed as a secondary control target. The error can be expressed in terms of the square of the difference between the reference current and the output current, i.e., (i) ref -i o ) 2 The method comprises the steps of carrying out a first treatment on the surface of the While the smoothness of the output current is influenced by the control amount u, u can be used 2 To suppress fluctuations in current. In combination with the primary and secondary control objectives, a cost function is constructed as follows:
where θ=a, b, c, u=a, b, c, n, p is the prediction step, i 2θ * (k+i) is a reference value of output currents of three phases A, B, C at the time of k+i, i 2θ (k+i|k) is the predicted value of the three-phase output current of the controller at the time k for the time k+i, d u (k+i-1) represents the duty cycle of the switching tubes of A, B, C, N four bridge arms at the time k+i-1, q and r being the weight coefficients of the output current tracking error and the output current smoothness, respectively.
The cost function is mainly divided into two parts, namely, the square sum of the differences between the three-phase output current reference value and the output current predicted value at all times is used for representing the fundamental wave shape of the output current of the inverter, and the smaller the part is, the smaller the error between the output current of the inverter and the reference current is; and secondly, the square sum of the duty ratios of the switching tubes on the four bridge arms at each moment 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 a weight coefficient of an output current tracking error and an output current smoothness, which represent the bias degree of the controller on the two parts, and the output current tracking error is a main control object, and the current smoothness is an additional soft constraint, so the following principle should be followed in the selection of the weight coefficient: q > > r.
Further simplification of formula (17) yields:
wherein,y * (k+i) is the reference value of the current output by the system at time k+i,/>Q＝diag(q,…,q)，R＝diag(r,…,r)，U min ＝[0…0] T ，U max ＝[1…1] T 。
Combining the formulas to obtain:
wherein ψ=wg·x (k) +ws·v g (k)。
The firefly algorithm (Firefly algorithm, FA) is a novel heuristic group intelligent optimization algorithm, and because the algorithm has the characteristic of faster optimization, the method is adopted to solve the objective function.
In the firefly algorithm, the position of each firefly represents one feasible solution of the problem, the brightness of the firefly is used for representing the adaptability of the position of the firefly, and the higher the brightness of the firefly is, the better the adaptability of the position of the individual is, namely the better the position in the whole solution space is. In the solution space, each firefly flies towards the firefly with higher brightness than the firefly, so that the firefly is in a better position, and the higher the brightness, the higher the attraction degree of the firefly to other fireflies. In the present invention, the minimum value of the objective function is solved, so that the brightness of firefly and the objective function can be set in an inverse relationship with each other:
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of the firefly at position x.
And (3) sequencing the brightness of each firefly randomly given in the initialization, and then moving each firefly according to the rule that the low brightness is attracted by the high brightness. The formula of movement is as follows:
wherein beta is 0 Attraction degree when distance r=0 from firefly; gamma is the absorption coefficient of the light propagation medium (typically air); alpha is a step factor and is expressed as a random disturbance term; r is (r) ij For any two in space at position X i And X j The euclidean distance between fireflies i and j is expressed as follows:
wherein n represents the maximum dimension of the position of the firefly in space, and x i,k Indicating that the ith firefly is in space X i The kth dimensional coordinate value on the position.
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 factor are mainly set.
(2) And initializing a population. Generating corresponding number of fireflies according to the population size set in the previous step, and randomly distributing positions for each firefly in the constraint condition.
(3) And (3) carrying out brightness calculation on each firefly randomly generated in the previous step. For the minimum value optimization problem, the brightness of the fireflies can be enabled to be the current position of the fireflies, the inverse value of the value function is substituted into the current position of the fireflies, then the brightness of each firefly is ordered, and the optimal individual in the current group is selected.
(4) The position of each firefly is updated. For fireflies with lower brightness, the fireflies are allowed to move to fireflies with higher brightness by the formula (22), and for fireflies with highest brightness, the fireflies are allowed to randomly move within a certain range.
(5) And (3) calculating the brightness of each firefly again and reordering according to the updated firefly position in the previous step, and repeating the step (4) until the difference of the continuous duty ratio of two times is smaller than the set precision.
(6) And outputting the optimal individual value and the global minimum point in the solution space.
Comparative experiment for stabilizing grid-connected current waveform
And performing a comparison experiment when the grid-connected current waveform is stable, and obtaining two groups of grid-connected current waveforms and current error waveforms when the grid-connected current waveform is stable, wherein the two groups of grid-connected current waveforms and the current error waveforms are shown in figure 4. In the figure, the ordinate represents grid-connected current and current error, respectively, and the abscissa represents simulation time. FFT analysis under the action of PI controller As shown in FIG. 5, the amplitude of the current waveform at the fundamental frequency under the action of PI controller is 20.21A, THD=0.78%. The simulation waveform under the action of the FA-MPC controller provided by the invention is shown in figure 6, the FFT analysis under the action of the FA-MPC controller is shown in figure 7, the amplitude of the current waveform under the action of the FA-MPC controller at the fundamental frequency is 20.86A, and THD=0.65%.
From the above experiments, it can be seen that the amplitudes at the fundamental frequencies of the two controllers are 20.21A and 20.86A, respectively, which are not quite different from the set 21.21A, but the total harmonic distortion of the two controllers can be found by observing the total harmonic distortion of the two controllers, wherein the thd=0.78% of the PI controller and the thd=0.65% of the FA-MPC controller are all lower values, which indicates that the current harmonics are less and mainly lower harmonics.
The PI controller and the FA-MPC controller are better in control effect from the aspects of current sine degree and smoothness and steady-state error and total harmonic distortion THD value.
Fig. 8 is a schematic diagram of a simulation result of the B-phase current dynamic response under the action of two controllers, when t=0.1 s, the reference effective value of the grid-connected current is reduced from 15A to 10A, and then the dynamic response of the B-phase current is observed under the action of two different controllers. Amplifying the current waveform near 0.1s, the overshoot of the B-phase current waveform under the action of the PI controller can be seen to reach 4.8A, and the adjusting time is 0.4ms; the overshoot of the B-phase current waveform under the action of the FA-MPC controller was only 4.5A, with a tuning time of 0.55ms. In the aspect of controlling the overshoot, the FA-MPC controller is more excellent in performance; whereas the PI controller is faster than the FA-MPC controller by 0.15ms in the tuning time, accounting for 0.75% of a control period of 20ms, it can be considered that the phase difference is not large, and the influence is negligible, mainly because the calculated amount of the FA-MPC controller is larger relative to the PI controller, and the required time is longer. Overall, the control effect of both in terms of dynamic response is quite good, but the FA-MPC controller works better than the PI controller.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present patent and not for limiting the same, and although the present patent has been 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 present patent may be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present patent, and all such embodiments are included in the scope of the claims of the present patent.
Claims (10)
1. The grid-connected inversion control method for the emergency power supply of the fuel cell is characterized by comprising the following steps of: the method acts on a fuel cell emergency power system to optimally control a power circuit of a three-phase four-bridge arm grid-connected inverter, and comprises the following steps:
1) According to a topological circuit of the three-phase four-bridge arm grid-connected inverter, a state space mathematical model is established by utilizing kirchhoff voltage and current law;
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) Two parameters of a required prediction step length and a control step length in prediction control are introduced to obtain a prediction equation of the grid-connected inverter, and the prediction equation is written into a vector equation form;
4) On the basis of the vector form prediction equation, combining the limiting condition of the control quantity duty ratio and the actual requirement of the output grid-connected current to construct an objective function with a constraint condition;
5) And (3) correlating the actual control quantity duty ratio of the inverter with the brightness of the firefly by adopting a firefly intelligent optimizing algorithm, obtaining the optimal duty ratio of PWM waves for controlling the switching tube by simulating the moving process of the firefly, and optimally controlling the power circuit of the three-phase four-bridge arm grid-connected inverter.
2. The fuel cell emergency power 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:
in the inverter side inductance L 1 The current of (2) is i 1a ，i 1b ，i 1c Grid side inductance L 2 The current of (2) is i 2a ，i 2b ，i 2c Filter capacitor C f Is of the voltage u ca ，u cb ，u cc ，R c For filteringCapacitor C f Damping resistor v connected in series ga ，v gb ，v gc U is a three-phase AC network a ，u b ，u c ，u n Is the voltage of the central points of four bridge arms, t is time, R2 is the equivalent series resistance on the inductor L2, L n Is a neutral inductance connected in series on the middle line of the fourth bridge arm, R n Is the inductance L n Equivalent series resistance.
3. The fuel cell emergency power 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:
wherein, phi, Γ, Λ, D are all variable coefficients.
4. The fuel cell emergency power grid-connected inversion control method according to claim 1, characterized in that: the prediction equation of the vector form of the three-phase four-bridge arm grid-connected inverter in the step 3) is as follows:
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 g (k) K is a time, and G, H, S, W is a control coefficient.
5. The fuel cell emergency power grid-connected inversion control method according to claim 1, characterized in that: the objective function in the step 4) is as follows:
in the formula, θ=a, b, c, u=a, b, c, n, p is the prediction step, i 2θ * (k+i) is a reference value of output currents of three phases A, B, C at the time of k+i, i 2θ (k+i|k) is the predicted value of the three-phase output current of the controller at the time k for the time k+i, d u (k+i-1) represents the duty cycle of the switching tubes of A, B, C, N four bridge arms at the time k+i-1, q and r being the weight coefficients of the output current tracking error and the output current smoothness, respectively.
6. The fuel cell emergency power grid-connected inversion control method according to claim 5, wherein: the constraint condition of the objective function is d u (k+i-1)∈[0,1]。
7. The fuel cell emergency power grid-connected inversion control method according to claim 1, characterized in that: in the step 5), the brightness of firefly and the objective function J (x) are set in inverse relation to each other:
I(x)＝1/J(x) (21)
wherein I (x) is the brightness of the firefly at position x.
8. The fuel cell emergency power grid-connected inversion control method according to claim 1, characterized in that: the specific flow of the firefly algorithm adopted in the step 5) is as follows:
(1) Setting population quantity, maximum iteration times, initial attractiveness, light intensity absorption coefficient and step factor;
(2) Generating a corresponding number of fireflies according to the set population size, and randomly distributing positions for each firefly in a constraint condition;
(3) Calculating the brightness of each firefly generated randomly, substituting the brightness of the firefly into the reciprocal of the value of the cost function for the current position of the firefly, and then sequencing the brightness of each firefly to select the optimal individual in the current group;
(4) Updating the position of each firefly, for fireflies with lower brightness, moving the fireflies to fireflies with higher brightness, and for fireflies with highest brightness, randomly moving the fireflies within a set range;
(5) According to the updated firefly positions, calculating the brightness of each firefly again and reordering, and repeating the step (4) until the difference of the continuous duty ratio of two times is smaller than the set precision;
(6) And outputting the optimal individual value and the global minimum point in the solution space.
9. A grid-connected inversion control system of a fuel cell emergency power supply is characterized in that: the system includes a power conversion circuit and a controller capable of performing the fuel cell emergency power grid-tie inverter control method as claimed in any one of claims 1 to 8.
10. The fuel cell emergency power grid-tie inverter control system of claim 9, wherein: the power conversion circuit comprises a voltage stabilizing capacitor at an input end, four bridge arms and an LCL filter at an output end for filtering harmonic waves.
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