CN108429475B - Grid-connected inverter control method for wave power generation system - Google Patents

Grid-connected inverter control method for wave power generation system Download PDF

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CN108429475B
CN108429475B CN201810144406.5A CN201810144406A CN108429475B CN 108429475 B CN108429475 B CN 108429475B CN 201810144406 A CN201810144406 A CN 201810144406A CN 108429475 B CN108429475 B CN 108429475B
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余海涛
王�琦
董坤
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M1/00—Details of apparatus for conversion
    • H02M1/0003—Details of control, feedback or regulation circuits
    • H02M1/0025—Arrangements for modifying reference values, feedback values or error values in the control loop of a converter

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Abstract

The invention discloses a grid-connected inverter control method for a wave power generation system. In order to realize rapid non-difference tracking, the invention adopts a global rapid terminal sliding mode control strategy based on a reinforcement learning algorithm, and compensates the rapid terminal sliding mode control strategy in real time through interaction of the reinforcement learning algorithm and the environment, so that the inverter has self-learning capability to external interference, and the robustness and the stability of the system are enhanced.

Description

Grid-connected inverter control method for wave power generation system
Technical Field
The invention relates to the technical field of inverter control of a wave power generation system, in particular to a reinforcement learning algorithm and a terminal sliding mode control algorithm.
Background
Wave energy is an important component of ocean energy, and the reserve of the wave energy is very large, so that the wave energy has a wide development and utilization prospect. China is a big ocean country, available wave energy resources are extremely rich, and the development and utilization of the wave energy resources are one of the important contents for the development and utilization of ocean renewable energy resources in China. The wave energy development mainly adopts wave power generation, the wave power generation device is built on the sea, and electric energy is transmitted to the shore through a seabed composite cable. Because the wavelength, the wave height and the wave period are all changed, the wave energy is unstable and discontinuous, and the synchronization of the output voltage of the inverter, the amplitude of the voltage of the power grid and the phase is especially important by controlling. Most of traditional wave power generation grid-connected inverters adopt PI control algorithms, the PI control algorithms are simple to implement, but PI parameters need to be adjusted off line, the PI control algorithms are poor in robustness, and the application effect in small-scale wave power generation single-phase grid-connected inverters is poor.
The sliding mode variable structure control method is a nonlinear control method, the sliding mode of the sliding mode variable structure control method can be designed and is irrelevant to object parameters and disturbance, and therefore the sliding mode variable structure control method has the advantages of being fast in response, insensitive to corresponding parameter change and disturbance, free of system online identification, simple in physical implementation and the like. The conventional sliding mode surface is a linear function, and after the conventional sliding mode surface is reached, the system error cannot be converged in a limited time. According to the terminal sliding mode control strategy, a nonlinear function is introduced into a sliding mode surface, so that the sliding mode controller has overall rapidity. However, after the sliding mode surface is reached, the magnitude of the switch gain epsilon is fixed and unchanged, and the phenomenon of 'buffeting' cannot be avoided.
Disclosure of Invention
The technical problem is as follows: the invention provides a grid-connected inverter control method for a wave power generation system, wherein a global terminal sliding mode controller is used for controlling the duty ratio of a switching tube of an inverter, and a reinforcement learning algorithm is adopted to dynamically adjust the switching gain epsilon of the sliding mode controller on line, so that the phenomenon of buffeting is greatly weakened, and the robustness and the stability of the system are enhanced.
The technical scheme is as follows: the invention discloses a grid-connected inverter control method for a wave power generation system, which comprises the following steps:
step 1: initializing an evaluation network learning rate and a discount factor of a reinforcement learning algorithm, and designing a reinforcement learning signal according to a system error;
step 2: calculating an evaluation network error, and carrying out online adjustment on the weight of the evaluation network through the calculated evaluation network error;
and step 3: solving an optimal performance index function to obtain a global fast terminal sliding mode self-adaptive control rate based on a reinforcement learning principle;
and 4, step 4: and generating a global fast terminal sliding mode control algorithm control signal based on a reinforcement learning principle according to the self-adaptive control rate, and controlling the conduction time of the inverter switching tube.
Further, in the method of the present invention, in the step 1, the reinforcement learning signal r (t) is designed according to the following formula:
Figure GDA0001645196330000021
wherein, Uac(t) inverter AC output voltage at time t;is the grid voltage at time t; u (t) is the duty ratio of the switching tube at the time t, and is the system control quantity; r (t) is a reinforcement learning signal at time t.
Further, in the method of the present invention, in the step 2, the network error is calculated and evaluated according to the following formula:
ec(t)＝γJ(t)-[J(t-1)-r(t)]
wherein e isc(t) the evaluation network error at time t; j (t) evaluating the network output at time t; j (t-1) is the evaluation network output at the time of t-1; gamma is a discount factor; r (t) is a reinforcement learning signal.
Further, in the method of the present invention, in the step 2, the weight of the evaluation network is adjusted online according to the following rules:
Figure GDA0001645196330000023
wherein, Wc(t) evaluating a network weight matrix at the moment t; wc(t +1) evaluating a network weight matrix at the moment of t + 1; Δ Wc(t) evaluating the variable quantity of the network weight matrix at the moment t; lcTo evaluate the network learning rate; ecAnd (t) is the square of the evaluation network error.
Further, in the method of the present invention, in the step 3, a global fast terminal sliding mode adaptive control rate based on a reinforcement learning principle is obtained according to the following formula:
Figure GDA0001645196330000024
wherein laIs an adaptive learning rate; u shapeac(t) inverter AC output voltage at time t; e.g. of the typec(t) evaluating network errors; ec(t) is the square of an evaluation network error, and delta epsilon (t) is the global fast terminal sliding mode self-adaptive control rate at the moment t; epsilon (t) is the sliding mode switch gain at time t.
Further, in the method of the present invention, in the step 4, the control signal is generated according to the following formula:
Figure GDA0001645196330000031
wherein, UdcIs the DC side voltage of the inverter; l is an inverter alternating current inductor; c is an inverter alternating current capacitor; r is an inverter alternating current resistor; u (t +1) is the duty ratio of the switching tube at the moment of t + 1;
Figure GDA0001645196330000032
the difference value between the output voltage of the inverter at the moment t and the actual power grid voltage is obtained; e (t +1) is the difference value between the inverter output voltage at the moment of t +1 and the actual power grid voltage;
Figure GDA0001645196330000033
is the grid voltage at time t;
Figure GDA0001645196330000034
the grid voltage is at the moment t + 1;
Figure GDA0001645196330000035
the grid voltage is the t-1 moment; u shapeac(t) inverter AC output voltage at time t; u shapeac(t-1) the inverter alternating current output voltage at the time of t-1; u shapeac(t +1) is the inverter alternating current output voltage at the moment of t + 1; s is a terminal sliding mode surface; t issThe method comprises the steps of sampling time, α, β, q, p and k are sliding mode adjusting parameters, α, β and k are positive numbers, q and p are positive integers, epsilon (t) is sliding mode switch gain at the moment t, and delta epsilon (t) is global fast terminal sliding mode self-adaptive control rate at the moment t.
Has the advantages that: compared with the prior art, the invention has the following advantages:
most of traditional wave power generation grid-connected inverters adopt PI control algorithms, the PI control algorithms are simple to implement and wide in application, but PI control algorithm parameters need to be adjusted offline, inherent defects of a classical control theory exist, system parameter changes and external disturbance are too sensitive, robustness is poor, and the application effect in small-scale wave power generation single-phase grid-connected inverters is poor.
The sliding mode variable structure control method is a nonlinear control method, the sliding mode of the sliding mode variable structure control method can be designed and is irrelevant to object parameters and disturbance, and therefore the sliding mode variable structure control method has the advantages of being fast in response, insensitive to corresponding parameter change and disturbance, free of system online identification, simple in physical implementation and the like. The conventional sliding mode surface is a linear function, and after the conventional sliding mode surface is reached, the system error cannot be converged in a limited time. According to the terminal sliding mode control strategy, a nonlinear function is introduced into a sliding mode surface, so that the sliding mode controller has overall rapidity. However, after the sliding mode surface is reached, the magnitude of the switch gain epsilon is fixed and unchanged, and the phenomenon of 'buffeting' cannot be avoided.
The invention takes the global terminal sliding mode control strategy as a main controller, and adopts the reinforcement learning algorithm to dynamically adjust the switching gain epsilon of the sliding mode controller on line, so that the control strategy of the inverter can be continuously self-adjusted according to the environmental characteristics, the phenomenon of buffeting is greatly weakened, and the robustness and the stability of the system are enhanced.
Drawings
Fig. 1 is a schematic structural diagram of a global fast terminal sliding mode control algorithm based on a reinforcement learning principle.
Fig. 2 is a schematic diagram of a hardware implementation main circuit structure.
Detailed Description
For a more detailed understanding of the present invention, reference is now made to the detailed description of the specific embodiments of the present invention, taken in conjunction with the accompanying drawings.
A control method of a grid-connected inverter for a wave power generation system is shown in figure 1, and the specific implementation comprises the following steps:
step 1: initializing an evaluation network learning rate and a discount factor of a reinforcement learning algorithm, and designing a reinforcement learning signal according to a system error;
the reinforcement learning signal r (t) is designed according to the following formula:
Figure GDA0001645196330000041
wherein, Uac(t) inverter AC output voltage at time t;
Figure GDA0001645196330000042
is the grid voltage at time t; u (t) is the duty ratio of the switching tube at the moment f, and is the system control quantity; r (t) is a reinforcement learning signal at time t; the evaluation web learning rate is set to 0.3 and the discount factor is set to 0.95.
Step 2: calculating an evaluation network error, and carrying out online adjustment on the weight of the evaluation network through the calculated evaluation network error;
the evaluation network error is: e.g. of the typec(t)＝γJ(t)-[J(t-1)-r(t)]
Wherein e isc(t) the evaluation network error at time t; j (t) evaluating the network output at time t; j (t-1) is the evaluation network output at the time of t-1; gamma is a discount factor; r (t) is a reinforcement learning signal.
And (3) carrying out online adjustment on the weight of the evaluation network according to the following rules: wc(t+1)＝Wc(t)+ΔWc(t)，
Figure GDA0001645196330000043
Wherein, Wc(t) evaluating a network weight matrix at the moment t; wc(t +1) evaluating a network weight matrix at the moment of t + 1; Δ Wc(t) evaluating the variable quantity of the network weight matrix at the moment t; lcTo evaluate the network learning rate; ecAnd (t) is the square of the evaluation network error.
And step 3: solving an optimal performance index function to obtain a global fast terminal sliding mode self-adaptive control rate based on a reinforcement learning principle;
the optimal performance index function solving process is as follows:
Figure GDA0001645196330000051
wherein laIs an adaptive learning rate; u shapeac(t) inverter AC output voltage at time t; e.g. of the typec(t) evaluating network errors; ec(t) is the square of an evaluation network error, and delta epsilon (t) is the global fast terminal sliding mode self-adaptive control rate at the moment t; epsilon (t) is the sliding mode switch gain at time t.
And 4, step 4: and generating a global fast terminal sliding mode control algorithm control signal based on a reinforcement learning principle according to the self-adaptive control rate, and controlling the conduction time of the inverter switching tube.
The mathematical model of the grid-connected inverter of the wave power generation system is as follows:
Figure GDA0001645196330000052
wherein, UdcIs the DC side voltage of the inverter; l is an inverter alternating current inductor; c is an inverter alternating current capacitor; r is an inverter alternating current resistor; u (t) is the duty ratio of the switching tube at the time t, and is the system control quantity; u shapeac(t) inverter AC output voltage at time t; u shapeac(t-1) the inverter alternating current output voltage at the time of t-1; t issIs the sampling time; u shapeacAnd (t-2) is the alternating current output voltage of the inverter at the time t-2.
The terminal sliding mode control surface is as follows:
wherein the content of the first and second substances,
Figure GDA0001645196330000054
the difference value between the output voltage of the inverter at the moment t and the actual power grid voltage is obtained; u shapeac(t) inverter AC output voltage at time t;
Figure GDA0001645196330000055
is the grid voltage at time t; e (t-1) is the difference value between the output voltage of the inverter at the moment t-1 and the actual power grid voltage; t issThe sampling time is s is a terminal sliding mode surface, α, β, q and p are sliding mode adjusting parameters, α and β are positive numbers, and q and p are positive integers.
The Lyapunov function is:
Figure GDA0001645196330000056
wherein s is a terminal sliding mode surface; v1Is the Lyapunov function;
obtaining a derivative of the Lyapunov function
Wherein the content of the first and second substances,
Figure GDA0001645196330000061
is the derivative of the terminal sliding mode surface;is the derivative of the lyapunov function;
in order to make the Lyapunov function satisfy the stability condition
Figure GDA0001645196330000063
And obtaining the inverter control rate at the t +1 moment as follows:
Figure GDA0001645196330000064
wherein, UdcIs the DC side voltage of the inverter; l is an inverter alternating current inductor; c is an inverter alternating current capacitor; r is an inverter alternating current resistor; u (t +1) is the duty ratio of the switching tube at the moment of t + 1;
Figure GDA0001645196330000065
the difference value between the output voltage of the inverter at the moment t and the actual power grid voltage is obtained; e (t +1) is the difference value between the inverter output voltage at the moment of t +1 and the actual power grid voltage;is the grid voltage at time t;
Figure GDA0001645196330000067
the grid voltage is at the moment t + 1;
Figure GDA0001645196330000068
the grid voltage is the t-1 moment; u shapeac(t) inverter AC output voltage at time t; u shapeac(t-1) the inverter alternating current output voltage at the time of t-1; u shapeac(t +1) is the inverter alternating current output voltage at the moment of t + 1; s is a terminal sliding mode surface; t issThe sampling time is α, β, q, p and k are sliding mode adjusting parameters, α, β and k are positive numbers, q and p are positive integers, and at the moment, the adjustment k is large, so that the moving point can quickly approach to the sampling timeAnd after the sliding mode switching surface is reached, the convergence rate is determined by α, β, q and p parameters, the convergence rate is adjusted according to multiple experiments, and delta epsilon (t) is the global fast terminal sliding mode self-adaptive control rate at the time t.
The algorithm is implemented by hardware, as shown in figure 2, the alternating current generated by the wave-activated generator is converted into direct current voltage U by the rectifying circuitdcThe global fast terminal sliding mode control algorithm based on the reinforcement learning principle is realized by programming on the DSP28335, and the voltage of the power grid is collected through an AD sampling circuit
Figure GDA0001645196330000069
And a DC voltage UdcAnd the control quantity is PWM output duty ratio, and four switching tubes of the inverter are controlled, so that the output voltage of the inverter is synchronous with the amplitude and the phase of the grid voltage.
The above examples are only preferred embodiments of the present invention, it should be noted that: it will be apparent to those skilled in the art that various modifications and equivalents can be made without departing from the spirit of the invention, and it is intended that all such modifications and equivalents fall within the scope of the invention as defined in the claims.

Claims (5)

1. A grid-connected inverter control method for a wave power system is characterized by comprising the following steps:
step 1: initializing an evaluation network learning rate and a discount factor of a reinforcement learning algorithm, and designing a reinforcement learning signal r (t) according to the following formula according to a system error:
Figure FDA0002259005960000011
wherein, Uac(t) inverter AC output voltage at time t;
Figure FDA0002259005960000012
is the grid voltage at time t; u (t) is the duty ratio of the switching tube at the time t, and is the system control quantity;r (t) is a reinforcement learning signal at time t;
step 2: calculating an evaluation network error, and carrying out online adjustment on the weight of the evaluation network through the calculated evaluation network error;
and step 3: solving an optimal performance index function to obtain a global fast terminal sliding mode self-adaptive control rate based on a reinforcement learning principle;
and 4, step 4: and generating a global fast terminal sliding mode control algorithm control signal based on a reinforcement learning principle according to the self-adaptive control rate, and controlling the conduction time of the inverter switching tube.
2. The grid-connected inverter control method for a wave power system according to claim 1, wherein the step 2 is to calculate and evaluate the network error according to the following formula:
ec(t)＝γJ(t)-[J(t-1)-r(t)]
wherein e isc(t) the evaluation network error at time t; j (t) evaluating the network output at time t; j (t-1) is the evaluation network output at the time of t-1; gamma is a discount factor; r (t) is a reinforcement learning signal.
3. The grid-connected inverter control method for the wave power generation system according to claim 1 or 2, characterized in that in step 2, the weight of the evaluation network is adjusted on line according to the following rules:
Figure FDA0002259005960000013
wherein, Wc(t) evaluating a network weight matrix at the moment t; wc(t +1) is the evaluation network weight matrix at the moment of t +1, △ Wc(t) evaluating the variable quantity of the network weight matrix at the moment t; lcTo evaluate the network learning rate; ecAnd (t) is the square of the evaluation network error.
4. The grid-connected inverter control method for the wave power generation system according to claim 1 or 2, characterized in that, in the step 3, the global fast terminal sliding mode adaptive control rate based on the reinforcement learning principle is obtained according to the following formula:
Figure FDA0002259005960000021
wherein laIs an adaptive learning rate; u shapeac(t) inverter AC output voltage at time t; e.g. of the typec(t) evaluating network errors; ecAnd (t) is the square of an evaluation network error, △ epsilon (t) is the global fast terminal sliding mode adaptive control rate at the time t, and epsilon (t) is the sliding mode switch gain at the time t.
5. The grid-connected inverter control method for a wave power system according to claim 4, characterized in that in step 4, the control signal is generated according to the following formula:
wherein, UdcIs the DC side voltage of the inverter; l is an inverter alternating current inductor; c is an inverter alternating current capacitor; r is an inverter alternating current resistor; u (t +1) is the duty ratio of the switching tube at the moment of t + 1;
Figure FDA0002259005960000023
the difference value between the output voltage of the inverter at the moment t and the actual power grid voltage is obtained; e (t +1) is the difference value between the inverter output voltage at the moment of t +1 and the actual power grid voltage;
Figure FDA0002259005960000024
is the grid voltage at time t;
Figure FDA0002259005960000025
the grid voltage is at the moment t + 1;
Figure FDA0002259005960000026
for the grid voltage at time t-1；Uac(t) inverter AC output voltage at time t; u shapeac(t-1) the inverter alternating current output voltage at the time of t-1; u shapeac(t +1) is the inverter alternating current output voltage at the moment of t + 1; s is a terminal sliding mode surface; t issThe adaptive control method comprises the steps of sampling time, α, β, q, p and k are sliding mode adjustment parameters, α, β and k are positive numbers, q and p are positive integers, epsilon (t) is sliding mode switch gain at the time t, and △ epsilon (t) is global fast terminal sliding mode adaptive control rate at the time t.
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