CN104158418A - Photovoltaic grid-connected inverter device - Google Patents
Photovoltaic grid-connected inverter device Download PDFInfo
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- CN104158418A CN104158418A CN201410408718.4A CN201410408718A CN104158418A CN 104158418 A CN104158418 A CN 104158418A CN 201410408718 A CN201410408718 A CN 201410408718A CN 104158418 A CN104158418 A CN 104158418A
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Abstract
A photovoltaic grid-connected inverter device comprises a plurality of miniature inverters in one-to-one correspondence with photovoltaic cell panels of a photovoltaic power station. Each miniature inverter comprises an MPU controller, an energy-storage capacitor, a flyback converter, a power frequency inverter bridge and an EMI filter, wherein the energy-storage capacitor, the flyback converter, the power frequency inverter bridge and the EMI filter are sequentially connected between the photovoltaic cell panel and a power grid; and the MPU controller collects the voltage of the photovoltaic cell panel and the voltage of the power grid through a first voltage sensor and a second voltage sensor respectively, collects the grid-connected current output by the EMI filter through a current sensor, and controls the flyback converter and the power frequency inverter bridge through two drive modules respectively. The photovoltaic grid-connected inverter device helps to solve the problem of poor reliability of a centralized inverter at the same time the power generating efficiency is improved. The device enables a neural network inverse model to be combined with a PI controller; not only linearization of the system can be realized, but also the robustness of the PI controller can be played; and thus the power quality is effectively improved.
Description
Technical field
The present invention relates to a kind of grid-connected Miniature inverter system that adopts neural network contrary modeling control, belong to technical field of power generation.
Background technology
Along with energy crisis and problem of environmental pollution day by day serious, solar energy power generating is as clean green energy resource, its grid-connected control technology becomes that countries in the world are paid close attention to and the focus of research.
Utilization ratio and the quality of power supply requirement of modern society to the energy is more and more higher.Existing grid-connected photovoltaic inverter mostly is centralized inverter, by carrying out electric energy output by a high-power inverter after a large amount of photovoltaic cell connection in series-parallel.Owing to can not ensureing that each photovoltaic cell works in maximum power point, there is inefficient shortcoming in this mode.More seriously, the reliability of this mode is poor, if centralized inverter breaks down, will cause connected all photovoltaic cells to stop electric energy output.Therefore, the microminiaturization of photovoltaic DC-to-AC converter is by the development trend becoming from now on.In addition,, there is the shortcoming that grid-connected current harmonic content is higher in the simple PI controller of the many employings of existing grid-connected photovoltaic inverter.Can suppress to a certain extent harmonic wave by increasing output filtering unit, but but make the output impedance of system present even high-order LC characteristic of second order, easily cause system unstable.Visible, in order to improve the quality of power supply, must seek better control method.
Summary of the invention
The object of the invention is to the drawback for prior art, a kind of photovoltaic network inverter is provided, effectively to improve reliability and the quality of power supply of photovoltaic generation.
Problem of the present invention realizes with following technical proposals:
A kind of photovoltaic network inverter, formation comprises each photovoltaic battery panel multiple Miniature inverters one to one with photovoltaic plant, each Miniature inverter comprises MPU controller and is connected in successively the storage capacitor between photovoltaic battery panel and electrical network, anti exciting converter, power frequency inverter bridge and electromagnetic interface filter, described MPU controller gathers respectively photovoltaic battery panel voltage and line voltage by the first voltage sensor and second voltage transducer, gather the grid-connected current of electromagnetic interface filter output by current sensor, and control respectively anti exciting converter and power frequency inverter bridge by two driver modules, described MPU controller operates in the following manner:
1. gather photovoltaic battery panel voltage by the first voltage sensor
v pv , gather line voltage by second voltage transducer
v grid , gather grid-connected current by current sensor
i ac ; Using Miniature inverter as object, set up and train corresponding neural network contrary modeling, specific as follows:
A. choose the inversion model of three layers of BP neural network system, wherein, input layer nodes is 10, hidden layer neuron nodes is 18, output layer neuron node number is 1, hidden layer neuron transfer function uses hyperbolic tangent function, and output layer neuron transfer function uses S type function;
B. on photovoltaic miniature inverter, gather service data;
Utilize single loop PI controller to carry out grid-connected current control, make photovoltaic miniature inverter respectively under the operating mode in rated power, 80% rated power, 60% rated power, 40% rated power, 20% rated power, continuous acquisition under every kind of operating mode
ngroup grid-connected current
i ac , line voltage
v grid , anti exciting converter pwm signal duty ratio
d, photovoltaic battery panel voltage
v pv , form altogether 5
ngroup service data;
C. utilize the service data structure sample gathering;
For the k moment, the sample of constructing for
v grid (k),
v grid (k-1),
v grid (k-2),
i ac (k),
i ac (k-1),
i ac (k+1),
d(k-1),
d(k-2),
d(k-3),
v pv (k),
d(k) }, wherein,
v grid (k) be the line voltage in k moment,
v grid (k-1) be the line voltage in k-1 moment,
v grid (k-2) be the line voltage in k-2 moment,
i ac (k) be the grid-connected current in k moment,
i ac (k-1) be the grid-connected current in k-1 moment,
i ac (k+1) be the grid-connected current in k+1 moment,
d(k-1) be the anti exciting converter pwm signal duty ratio in k-1 moment,
d(k-2) be the anti exciting converter pwm signal duty ratio in k-2 moment,
d(k-3) be the anti exciting converter pwm signal duty ratio in k-3 moment,
v pv (k) be the voltage of the photovoltaic battery panel in k moment,
d(k) be the anti exciting converter pwm signal duty ratio in k moment, 5
ngroup service data forms 5 altogether
nindividual sample;
D. randomly draw 5
nin individual sample data 4
nindividual as training sample, all the other
nthree layers of individual as test samples, training is set up BP neural net; In training process, for the sample in k moment, first input of getting BP neural net is the line voltage in k moment
v grid (k), second line voltage that input is the k-1 moment
v grid (k-1), the 3rd input is the line voltage in k-2 moment
v grid (k-2), the 4th input is the grid-connected current in k moment
i ac (k), the 5th input is the grid-connected current in k-1 moment
i ac (k-1), the 6th input is the grid-connected current in k+1 moment
i ac (k+1), the 7th the anti exciting converter pwm signal duty ratio that input is the k-1 moment
d(k-1), the 8th the anti exciting converter pwm signal duty ratio that input is the k-2 moment
d(k-2), the 9th the anti exciting converter pwm signal duty ratio that input is the k-3 moment
d(k-3), the tenth input is the voltage of the photovoltaic battery panel in k moment
v pv (k), BP neural net is output as the anti exciting converter pwm signal duty ratio in k moment
d(k);
2. utilize the BP neural network contrary modeling training to control Miniature inverter, concrete steps are:
A. utilize the BP neural network contrary modeling training to carry out the calculating of anti exciting converter pwm signal duty ratio, for the current k moment, get the line voltage that first input is the k moment
v grid (k), second line voltage that input is the k-1 moment
v grid (k-1), the 3rd input is the line voltage in k-2 moment
v grid (k-2), the 4th input is the grid-connected current in k moment
i ac (k), the 5th input is the grid-connected current in k-1 moment
i ac (k-1), the 6th input is the grid-connected current set point in k+1 moment
i * ac (k+1), the 7th the anti exciting converter pwm signal duty ratio that input is the k-1 moment
d(k-1), the 8th the anti exciting converter pwm signal duty ratio that input is the k-2 moment
d(k-2), the 9th the anti exciting converter pwm signal duty ratio that input is the k-3 moment
d(k-3), the tenth input is the voltage of the photovoltaic battery panel in k moment
v pv (k), show that BP neural net is output as
d(k);
B. by next moment grid-connected current set point
i * ac (k+1) with current k moment grid-connected current
i ac (k) deviation is sent into PI controller, obtains closed-loop control amount
d c(k),
Wherein: e (k)=
i * ac (k+1)-
i ac (k),
k p represent proportionality coefficient,
k i represent integral coefficient;
C. by the output of neural network contrary modeling
dand the output of PI controller (k)
d c(k) stack after as final anti exciting converter pwm signal duty ratio
d(k), that is:
D(k)=
d(k)+
d C(k)。
Above-mentioned photovoltaic network inverter, the training of BP neural net is adopted to the error back propagation algorithm that becomes learning rate, be specially: first weight coefficient and threshold value in each layer of neuron are initialized as to (1, + 1) random quantity between, again using 10 neuron output values of input layer as the each neuronic input of hidden layer, 18 neuron output values of hidden layer are as the single neuronic input of output layer, and the single neuronic output valve of output layer is network and finally exports, for
kindividual training sample, calculates the
tthe actual output of network after inferior training
with desired output
error:
And 4
nthe error of sum square of individual training sample is:
Complete one time of every calculating, relatively
e t with anticipation error
e o if,
e t <
e o , train end condition to meet, training finishes; Otherwise, will
e t carry out backpropagation along access path, and adjust one by one weight coefficient and the threshold value of each layer, until
e t <
e o till; Then, utilize
nindividual test samples is tested model, calculates sample mean square error
mSE, if having
mSE <
t r (
t r for a certain fixed threshold), think that trained BP neural network model meets required precision, now records each neuronic weight coefficient and threshold value; Otherwise, again BP neural network model is trained, until it meets required precision.
Above-mentioned photovoltaic network inverter, described anti exciting converter comprises transformer, the first power tube, diode and the second electric capacity, the primary coil of described transformer connects photovoltaic battery panel voltage through the first power tube, its secondary coil is powered to power frequency inverter bridge through diode, the second electric capacity is connected to the direct-flow input end of power frequency inverter bridge, and the grid of the first power tube connects the output of the first driver module.
Above-mentioned photovoltaic network inverter, described electromagnetic interface filter comprises three inductance and six electric capacity, the head end of two coils of the first inductance connects respectively two ac output ends of power frequency inverter bridge, the two ends that their tail end gets access to grid through the second inductance and the 3rd inductance respectively, the 3rd electric capacity is attempted by the ac output end of power frequency inverter bridge, one end ground connection of the 4th electric capacity and the 5th electric capacity, the other end connects respectively two ac output ends of power frequency inverter bridge, the two ends of the 6th electric capacity connect respectively the tail end of two coils of the first inductance, one end ground connection of the 7th electric capacity and the 8th electric capacity, the two ends that the other end gets access to grid respectively.
Above-mentioned photovoltaic network inverter, described power frequency inverter bridge is the fully controlled bridge being connected into by four power tubes, the grid of four power tubes connects the output of the second driver module.
The present invention utilizes multiple Miniature inverters to replace the centralized inverter in conventional photovoltaic power station, makes each photovoltaic battery panel work in maximum power point, in improving generating efficiency, has also solved the problem of centralized inverter poor reliability.This device combines neural network contrary modeling and PI controller, both can realize system linearity, can bring into play again the robustness of PI controller, thereby has effectively improved the quality of power supply.
Brief description of the drawings
Below in conjunction with accompanying drawing, the invention will be further described.
Fig. 1 is the structured flowchart of grid-connected Miniature inverter device;
Fig. 2 is neural network contrary modeling schematic diagram;
Fig. 3 is single neuronic structural representation;
Fig. 4 is the test result of BP neural network contrary modeling;
Fig. 5 is the program structure diagram of nerve network reverse controller;
Fig. 6 is the grid-connected current curve of grid-connected Miniature inverter output;
Fig. 7 is the grid-connected current error curve of grid-connected Miniature inverter output.
In figure, each list of reference numerals is: UT1, the first voltage sensor, UT2, second voltage transducer, CT, current sensor, D1, diode, Q1～Q5, the first power tube～five power tube, C1～C8, the first electric capacity～eight electric capacity, L1～L3, the first inductance～three inductance, TX1, transformer.
In literary composition, each symbol inventory is:
v pv for photovoltaic battery panel voltage,
v grid for line voltage,
i ac for grid-connected current,
v grid (k) be the line voltage in k moment,
dfor anti exciting converter pwm signal duty ratio,
v grid (k-1) be the line voltage in k-1 moment,
v grid (k-2) be the line voltage in k-2 moment,
i ac (k) be the grid-connected current in k moment,
i ac (k-1) be the grid-connected current in k-1 moment,
i ac (k+1) be the grid-connected current in k+1 moment,
d(k-1) be the anti exciting converter pwm signal duty ratio in k-1 moment,
d(k-2) be the anti exciting converter pwm signal duty ratio in k-2 moment,
d(k-3) be the anti exciting converter pwm signal duty ratio in k-3 moment,
v pv (k) be the voltage of the photovoltaic battery panel in k moment,
d(k) be the anti exciting converter pwm signal duty ratio in k moment,
i * ac (k+1) be the grid-connected current set point in k+1 moment,
for
tthe actual output of network after inferior training,
for
tdesired output after inferior training,
e o for anticipation error,
e t for
tafter inferior training 4
nthe error of sum square of individual training sample.
Embodiment
The present invention is made up of multiple Miniature inverters, each Miniature inverter comprises: storage capacitor (being the first capacitor C 1 in Fig. 1), anti exciting converter is (by transformer TX1, the first power tube Q1, diode D1 and the second capacitor C 2 form), power frequency inverter bridge (being formed by the second power tube Q2～five power tube Q5), electromagnetic interface filter is (by the 3rd capacitor C 3～eight capacitor C 8, the first inductance L 1～three inductance L 3 forms), the first driver module (being the driver module 1 in Fig. 1), the second driver module (being the driver module 2 in Fig. 1), MPU controller, the first voltage sensor UT1, second voltage transducer UT2, current sensor CT, the positive pole of storage capacitor is connected with photovoltaic battery panel is anodal, the minus earth of storage capacitor, the minus earth of the first voltage sensor, the positive pole of the first voltage sensor are connected with photovoltaic battery panel positive pole, the measuring-signal output of the first voltage sensor is connected with MPU controller, the input anode of anti exciting converter is connected with photovoltaic battery panel is anodal, the input negativing ending grounding of anti exciting converter, the output plus terminal of anti exciting converter is connected with power frequency inverter bridge one input, and the output negative terminal of anti exciting converter is connected with another input of power frequency inverter bridge, one output of power frequency inverter bridge is connected with electromagnetic interface filter one input, and another input of another output and electromagnetic interface filter of power frequency inverter bridge is connected, one output of electromagnetic interface filter is connected with the positive pole of current sensor, and another output of electromagnetic interface filter is connected to electrical network, the positive pole of current sensor is connected with an output of electromagnetic interface filter, and the negative pole of current sensor is connected to electrical network, and the measuring-signal output of current sensor is connected with MPU controller, the positive pole of second voltage transducer is connected to the negative pole of current sensor, and the negative pole of second voltage transducer is connected to electrical network, and the measuring-signal output of second voltage transducer is connected with MPU controller, the input of the first driver module is connected with MPU controller, and the output of the first driver module is connected with the first power tube grid in anti exciting converter, the input of the second driver module is connected with MPU controller, one output of the second driver module is connected with the second power tube in power frequency inverter bridge, the grid of the 5th power tube, and another output of the second driver module is connected with the 3rd power tube in power frequency inverter bridge, the grid of the 4th power tube, MPU controller is connected with the measuring-signal output of the first voltage sensor, second voltage transducer, current sensor, and MPU controller is also connected with the input of the first driver module, the second driver module,
Further illustrate the present invention below in conjunction with the drawings and specific embodiments.
In the present embodiment, choose the 235W photovoltaic battery panel that Ying Li company produces, its parameter at standard test condition is: short circuit current
i sc=8.54A, open circuit voltage
v oc=37V, maximum power point electric current
i max=7.97A, maximum power point voltage
v max=29.5V.
The Miniature inverter structured flowchart designing for this photovoltaic battery panel as shown in Figure 1.This device mainly comprises: storage capacitor (the first electric capacity) C1, anti exciting converter, power frequency inverter bridge, electromagnetic interface filter, the first driver module, the second driver module, MPU controller, the first voltage sensor UT1, second voltage transducer UT2, current sensor CT; Wherein MPU controller is selected the high performance float-point digital signal processor TMS320F28335 of TI company, the functional modules such as I/O module, ADC module, PWM module, CAN module, UART and SPI module that this digital signal processor is integrated; Storage capacitor C1 selects 2200uF electrochemical capacitor; In anti exciting converter, transformer TX1 adopts NA5814-AL, and the first power tube Q1 selects TK50X15J1, and diode D1 adopts C2D05120E, and the second capacitor C 2 is selected 0.47uF polarity free capacitor; The second power tube Q2, the 3rd power tube Q3, the 4th power tube Q4, the 5th power tube Q5 select IPB60R190C6, and the first driver module and the second driver module are MCP14E4; In electromagnetic interface filter, the 3rd capacitor C 3, the 6th capacitor C 6 are selected 0.015uf polarity free capacitor, the 4th capacitor C 4, the 5th capacitor C 5, the 7th capacitor C 7, the 8th capacitor C 8 are selected 4700pF polarity free capacitor, the first inductance L 1 is selected FE2X03-4-3NL, and the second inductance L 2, the 3rd inductance L 3 are selected 2100LL-151-H-RC; Voltage sensor adopts MCP6022; Current sensor CT adopts Hall current sensor ACS712ELCTR-058-1;
Fig. 2 is three layers of set up BP neural network contrary modeling.Input layer nodes is 10, and hidden layer neuron nodes is 18, and output layer neuron node number is 1.
Fig. 3 is single neuronic structural representation,
for neuronic input signal,
for neuronic weight coefficient,
for neuronic threshold value, neuronic being output as:
Hidden layer neuron transfer function uses hyperbolic tangent function,
Wherein:
for transfer function input;
for transfer function output.
Output layer neuron transfer function uses S type function,
(3)
Wherein:
for transfer function input;
for transfer function output.
In embodiment, gathering service data process is specially: utilize single loop PI controller to carry out grid-connected current control, make photovoltaic miniature inverter respectively under the operating mode in rated power, 80% rated power, 60% rated power, 40% rated power, 20% rated power, under every kind of operating mode with the sample frequency continuous acquisition of 114KHz
n=10000 groups of grid-connected current
i ac , line voltage
v grid , anti exciting converter pwm signal duty ratio
d, photovoltaic battery panel voltage
v pv , obtain altogether 50000 groups of service datas, thereby form 50000 samples.Randomly draw wherein 40000 as training sample, all the other 10000 as test samples.
In embodiment, arrange
e o =1.5, threshold value
t r =0.01, iteration is trained after 9876 times,
e t =1.47, meet end condition
e t <
e o , training finishes.Now, recycling test samples is tested model, and partial test result as shown in Figure 4, calculates sample mean square error
mSE=0.006, has
mSE<
t r , therefore think and meet inversion model required precision, now record each neuronic weight coefficient and threshold value.
In embodiment, the parameter tuning of PI controller is
k p =0.01,
k i =800; Grid-connected current set point
i * ac change given by digital signal processor according to discrete sine rule.
Write software program and implant in dsp chip according to above-mentioned steps, can realize the control of the nerve network reverse controller of photovoltaic miniature inverter.Software program comprises system main program and Neural network inverse control interrupt service subroutine, and block diagram is as Fig. 5.
Fig. 6 is the grid-connected current curve of photovoltaic miniature inverter output after use control method of the present invention, and Fig. 7 is grid-connected current aberration curve.Visible, grid-connected current can be good at following the tracks of its set point, has less deviation.
In order further to analyze the effect of control method of the present invention, under 20%, 40%, 60%, 80%, 100% rated power output operating mode, itself and conventional PI control method (are got respectively
k p =0.01,
k i =800) contrast, table 1 is the grid-connected current harmonic content comparing result of two kinds of methods, known the inventive method has good robustness for the variation of operating mode, grid-connected current harmonic content under different operating modes is significantly less than conventional PI control method, meets the grid-connected harmonic content standard (THD<5%) of national grid.
Table 1
。
The above; it is only preferably embodiment of the present invention; but protection scope of the present invention should be not limited to this; core technology, the variation that can make or replacement that any those skilled in the art of being familiar with disclose according to the present invention, within all should being encompassed in protection scope of the present invention.
Claims (5)
1. a photovoltaic network inverter, it is characterized in that, it comprises each photovoltaic battery panel multiple Miniature inverters one to one with photovoltaic plant, each Miniature inverter comprises MPU controller and is connected in successively the storage capacitor between photovoltaic battery panel and electrical network, anti exciting converter, power frequency inverter bridge and electromagnetic interface filter, described MPU controller gathers respectively photovoltaic battery panel voltage and line voltage by the first voltage sensor (UT1) and second voltage transducer (UT2), gather the grid-connected current of electromagnetic interface filter output by current sensor (CT), and control respectively anti exciting converter and power frequency inverter bridge by two driver modules, described MPU controller operates in the following manner:
1. gather photovoltaic battery panel voltage by the first voltage sensor (UT1)
v pv , gather line voltage by second voltage transducer (UT2)
v grid , gather grid-connected current by current sensor (CT)
i ac ; Using Miniature inverter as object, set up and train corresponding neural network contrary modeling, specific as follows:
A. choose the inversion model of three layers of BP neural network system, wherein, input layer nodes is 10, hidden layer neuron nodes is 18, output layer neuron node number is 1, hidden layer neuron transfer function uses hyperbolic tangent function, and output layer neuron transfer function uses S type function;
B. on photovoltaic miniature inverter, gather service data;
Utilize single loop PI controller to carry out grid-connected current control, make photovoltaic miniature inverter respectively under the operating mode in rated power, 80% rated power, 60% rated power, 40% rated power, 20% rated power, continuous acquisition under every kind of operating mode
ngroup grid-connected current
i ac , line voltage
v grid , anti exciting converter pwm signal duty ratio
d, photovoltaic battery panel voltage
v pv , form altogether 5
ngroup service data;
C. utilize the service data structure sample gathering;
For the k moment, the sample of constructing for
v grid (k),
v grid (k-1),
v grid (k-2),
i ac (k),
i ac (k-1),
i ac (k+1),
d(k-1),
d(k-2),
d(k-3),
v pv (k),
d(k) }, wherein,
v grid (k) be the line voltage in k moment,
v grid (k-1) be the line voltage in k-1 moment,
v grid (k-2) be the line voltage in k-2 moment,
i ac (k) be the grid-connected current in k moment,
i ac (k-1) be the grid-connected current in k-1 moment,
i ac (k+1) be the grid-connected current in k+1 moment,
d(k-1) be the anti exciting converter pwm signal duty ratio in k-1 moment,
d(k-2) be the anti exciting converter pwm signal duty ratio in k-2 moment,
d(k-3) be the anti exciting converter pwm signal duty ratio in k-3 moment,
v pv (k) be the voltage of the photovoltaic battery panel in k moment,
d(k) be the anti exciting converter pwm signal duty ratio in k moment, 5
ngroup service data forms 5 altogether
nindividual sample;
D. randomly draw 5
nin individual sample data 4
nindividual as training sample, all the other
nthree layers of individual as test samples, training is set up BP neural net; In training process, for the sample in k moment, first input of getting BP neural net is the line voltage in k moment
v grid (k), second line voltage that input is the k-1 moment
v grid (k-1), the 3rd input is the line voltage in k-2 moment
v grid (k-2), the 4th input is the grid-connected current in k moment
i ac (k), the 5th input is the grid-connected current in k-1 moment
i ac (k-1), the 6th input is the grid-connected current in k+1 moment
i ac (k+1), the 7th the anti exciting converter pwm signal duty ratio that input is the k-1 moment
d(k-1), the 8th the anti exciting converter pwm signal duty ratio that input is the k-2 moment
d(k-2), the 9th the anti exciting converter pwm signal duty ratio that input is the k-3 moment
d(k-3), the tenth input is the voltage of the photovoltaic battery panel in k moment
v pv (k), BP neural net is output as the anti exciting converter pwm signal duty ratio in k moment
d(k);
2. utilize the BP neural network contrary modeling training to control Miniature inverter, concrete steps are:
A. utilize the BP neural network contrary modeling training to carry out the calculating of anti exciting converter pwm signal duty ratio, for the current k moment, get the line voltage that first input is the k moment
v grid (k), second line voltage that input is the k-1 moment
v grid (k-1), the 3rd input is the line voltage in k-2 moment
v grid (k-2), the 4th input is the grid-connected current in k moment
i ac (k), the 5th input is the grid-connected current in k-1 moment
i ac (k-1), the 6th input is the grid-connected current set point in k+1 moment
i * ac (k+1), the 7th the anti exciting converter pwm signal duty ratio that input is the k-1 moment
d(k-1), the 8th the anti exciting converter pwm signal duty ratio that input is the k-2 moment
d(k-2), the 9th the anti exciting converter pwm signal duty ratio that input is the k-3 moment
d(k-3), the tenth input is the voltage of the photovoltaic battery panel in k moment
v pv (k), show that BP neural net is output as
d(k);
B. by next moment grid-connected current set point
i * ac (k+1) with current k moment grid-connected current
i ac (k) deviation is sent into PI controller, obtains closed-loop control amount
d c(k),
Wherein:
e(k)=
i * ac (k+1)-
i ac (k),
k p represent proportionality coefficient,
k i represent integral coefficient;
C. by the output of neural network contrary modeling
dand the output of PI controller (k)
d c(k) stack after as final anti exciting converter pwm signal duty ratio
d(k), that is:
D(k)=
d(k)+
d C(k)。
2. a kind of photovoltaic network inverter according to claim 1, it is characterized in that, the training of BP neural net is adopted to the error back propagation algorithm that becomes learning rate, be specially: first weight coefficient and threshold value in each layer of neuron are initialized as to (1, + 1) random quantity between, again using 10 neuron output values of input layer as the each neuronic input of hidden layer, 18 neuron output values of hidden layer are as the single neuronic input of output layer, the single neuronic output valve of output layer is network and finally exports, for
kindividual training sample, calculates the
tthe actual output of network after inferior training
with desired output
error:
And 4
nthe error of sum square of individual training sample is:
Complete one time of every calculating, relatively
e t with anticipation error
e o if,
e t <
e o , train end condition to meet, training finishes; Otherwise, will
e t carry out backpropagation along access path, and adjust one by one weight coefficient and the threshold value of each layer, until
e t <
e o till; Then, utilize
nindividual test samples is tested model, calculates sample mean square error
mSE, if having
mSE <
t r (
t r for a certain fixed threshold), think that trained BP neural network model meets required precision, now records each neuronic weight coefficient and threshold value; Otherwise, again BP neural network model is trained, until it meets required precision.
3. a kind of photovoltaic network inverter according to claim 1 and 2, it is characterized in that, described anti exciting converter comprises transformer (TX1), the first power tube (Q1), diode (D1) and the second electric capacity (C2), the primary coil of described transformer (TX1) connects photovoltaic battery panel voltage through the first power tube (Q1), its secondary coil is given the power supply of power frequency inverter bridge through diode (D1), the second electric capacity (C2) is connected to the direct-flow input end of power frequency inverter bridge, the grid of the first power tube (Q1) connects the output of the first driver module.
4. a kind of photovoltaic network inverter according to claim 3, it is characterized in that, described electromagnetic interface filter comprises three inductance and six electric capacity, the head end of two coils of the first inductance (L1) connects respectively two ac output ends of power frequency inverter bridge, the two ends that their tail end gets access to grid through the second inductance (L2) and the 3rd inductance (L3) respectively, the 3rd electric capacity (C3) is attempted by the ac output end of power frequency inverter bridge, one end ground connection of the 4th electric capacity (C4) and the 5th electric capacity (C5), the other end connects respectively two ac output ends of power frequency inverter bridge, the two ends of the 6th electric capacity (C6) connect respectively the tail end of two coils of the first inductance (L1), one end ground connection of the 7th electric capacity (C7) and the 8th electric capacity (C8), the two ends that the other end gets access to grid respectively.
5. a kind of photovoltaic network inverter according to claim 4, is characterized in that, described power frequency inverter bridge is the fully controlled bridge being connected into by four power tubes, and the grid of four power tubes connects the output of the second driver module.
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